Best artificial intelligence books according to redditors
We found 1,705 Reddit comments discussing the best artificial intelligence books. We ranked the 333 resulting products by number of redditors who mentioned them. Here are the top 20.
1. Gödel, Escher, Bach: An Eternal Golden Braid
183 mentions
Godel Escher Bach An Eternal Golden Braid
Here's my list of the classics:
General Computing
Computer Science
Software Development
Case Studies
Employment
Language-Specific
C
Python
C#
C++
Java
Linux Shell Scripts
Web Development
Ruby and Rails
Assembly
I mean, only if you think we're tens of thousands of years away from powerful, general-purpose software agents. If you survey actual experts, they're pretty uncertain (and vulnerable to framing effects) but in general they think less than a century is pretty plausible.
So it's closer to somebody looking at the foundational research in nuclear physics and going "hey guys, this is going to be a real fucking problem at some point."
Which is pretty much what Einstein did (and started the Manhattan project and a pretty significant intelligence operation against the development of a German nuclear weapon).
EDIT: Also, if anyone's interested, the same blogger made a rundown of the opinions of lumaries in the field on AI risk in general. Opinions seem to be split, but there are plenty of bright people who know their shit who take the topic seriously. For those who aren't familiar with the topic and think everyone's just watched too much bad sci-fi, I recommend Bostrom.
He should brush up on his knowledge about general AI. Nick Bostrom's Superintelligence is a good starting place, even though it's already a few years old.
I recommend the rest of you /r/Futurology people read it, too. It'll challenge your preconceived notions of what to expect from AI.
If you are interested, and want a real challenge to your view, I strongly recommend Nick Bostrum's Superintelligence.
A key takeaway is imagining intelligence on this kind of scale. That is, our intuition says Einstein is much smarter than the dumbest person you know. Yet, the dumb guy and Einstein have the same hardware, running about the same number of computations. The difference between a bee and a mouse or a mouse and a chimp is orders of magnitude. The difference between an idiot and a genius is very small in comparison.
AI will seem very stupid to the human observer until almost exactly the point it becomes amazingly brilliant. As AI overtakes bees (probably has already) and mice it will still seem dumb to you. As it overtakes chimps, still much dumber than the dumbest person you know. As it draws even to the moron, you might think that the AI has a lot of work left to go. Instead, it's almost unfathomably brilliant.
The distance between bee and chimp is 1,000,000,000 and the difference between moron and smartest man who ever lived is 5.
Solar Plane
http://www.solarimpulse.com/en/our-adventure/the-first-round-the-world-solar-flight/
Sustainable City
https://www.youtube.com/watch?v=7UMvj2ZYnU8
New Horizons
http://pluto.jhuapl.edu/index.php
Electric Cars
http://cleantechnica.com/2014/07/28/electric-vehicle-revolution-nigh-infographic/
http://www.iea.org/publications/globalevoutlook_2013.pdf
http://www.abb-conversations.com/2014/03/electric-vehicle-market-share-in-19-countries/
http://www.futuretimeline.net/blog/2014/08/3.htm
http://www.futuretimeline.net/blog/2012/04/9.htm
https://en.wikipedia.org/wiki/Charging_station#Battery_swapping
http://www.futuretimeline.net/21stcentury/images/future-car-technology-2020.gif
Moon Ad
http://www.independent.co.uk/life-style/gadgets-and-tech/the-first-advert-on-the-moon-japanese-soft-drink-manufacturer-will-deliver-a-can-of-pocari-sweat-to-the-lunar-surface-in-2015-9382535.html
https://www.otsuka.co.jp/en/company/release/2014/0515_01.html
Virtual Reality
http://www.computerandvideogames.com/309486/sony-virtual-reality-gamings-going-to-be-absolutely-amazing/
http://www.futuretimeline.net/blog/2013/06/7-2.htm
Genome Sequencing
http://www.amazon.com/Singularity-Near-Humans-Transcend-Biology/dp/0143037889/
http://www.nature.com/nbt/journal/v30/n7/full/nbt.2241.html
http://www.genome.gov/sequencingcosts/
http://www.wired.com/2014/01/the-fda-may-win-the-battle-this-holiday-season-but-23andme-will-win-the-war/
http://www.prnewswire.com/news-releases/global-next-generation-sequencing-report-market-size-segmentation-growth-and-trends-by-provider---2013-edition-213150681.html
http://diginomica.com/2013/09/05/four-reasons-big-data-breathes-life-health-care/
http://www.futuretimeline.net/blog/2012/02/24.htm
http://www.genome.gov/27555651
http://www.theguardian.com/society/2013/may/14/angelina-jolie-mastectomy-breast-cancer
First of all, thanks for sharing. Code & idea implementation sucks, but it might turn into a very interesting discussion! By admitting that your trade idea is far from being unique and brilliant, you make a super important step in learning. This forum needs more posts like that, and I encourage people to provide feedback!
Idea itself is decent, but your code does not implement it:
Just because 0 looks good, you decide that 0 is the best threshold. You have to do a research here. You'd be surprised by how counter intuitive the result might be, or how super unstable it might be=))
The lesson is: idea first. Define it well. Then try to pick minimal number of indicators (or functions) that implement it. Check for parameter space. If you have too many parameters, discard your idea, since you will not be able to tell if it is making/losing money because it has an edge or just purely by chance!
What is left out of this discussion: cross validation and picking best parameters going forward
Recommended reading:
I first heard about these when reading Godel Escher Bach back in later high school. That book was a long, difficult read, but man did it blow my brain wide open. Quines are definitely the thing that I remember most vividly (probably because it was the easiest to understand), but that book was full of awesome stuff like this.
You should totally check it out! You can get it super cheap at used book stores since it was such a successful book.
The last couple of weeks in Zombox development:
(tl;dr: brand new zombie AI with lots of bells and whistles, tweaked ammo display and you can no longer hit things through walls)
Here are some screens showing the debug info I use to help me visualize the new AI system. White lines show new A* paths that are calculated. Green lines point to the next node on a zombie's path when the zombies is following a sound. Magenta/Cyan lines point to a zombie's active target (cyan = close, magenta = far). Red lines show the next node on a zombie's path when the zombie is chasing a target (although zombies are allowed to veer off their path when the target is directly in range). Yellow lines point to a new sound that a zombie has heard.
Animations:
As for New Year's resolutions...my short term goal is to implement Jump Point Search into my A* algorithm. My long term goals involve adding human NPCs, the underground subway system, the structure-building system, mini-quests, more zombie types including zombie animals, and releasing the game in May.
More info: Devblog, Youtube, Twitter
A central concept in cyberpunk and hacker culture is the idea of planned obsolescence: Corporations can make more money if they get you to buy their products multiple times instead of just once. This leads to a world where everything is discarded and the wealth gap is very clear between the people who have the new and the people who have the old.
The fact of the matter is that DNA is not our friend. Humans were built to spread our seed and be destroyed. We are a tool that DNA uses to extend it's own life. The human body is amazing in many ways, but it's amazing like a disposable razor is amazing. There's no mechanism to prevent cancer, no mechanism to prevent the development of back problems, and no mechanism to prevent it from withering away like a rotten fruit when it's purpose of reproduction has been served.
The implementation of transhumanism might be flawed, but so are all human endeavors. That's what cyberpunk is about: Figuring out how to deal with a world ruled by technology. Sometimes it doesn't go as smoothly as we imagine. The message of transhumanism is still clear, though: DNA doesn't own this planet any more, we do, and the name of the game is going to stop being reproduction and start being the enjoyment of existence.
Since you seem to be basing your understanding almost entirely on fiction, let me recommend some reading
Ray Kurzweil best describes how humankind will transcend biology.
Our biology binds us with time. By transcending biology we will transcend time. With all the time we need, we needn't worry ourselves with trying to create wormholes when we can just send a copy of ourselves in all directions.
As one of the people who commented on that thread, I feel the need to respond to this as rationally as humanly possible.
For starters, let's clear up the difference between fractal mathematics, fractal woo, and what Douglas Hofstadter might call fractal analogy.
>“Everything big is just like everything small!” they exclaim, “the universe is self-similar!”
...and then using such logic to thereby justify whatever silly energy-Reiki-mystical-connectedness-telepathy-de-jour they want.
As well, Chaos Theory - the study of how immensely complex patterns emerge from seemingly simple preconditions - is full of fractal mathematics. Given that the universe is absolutely packed with iterated functions and self-similarity almost everywhere we look, I think you can absolutely take the point of view that the universe is fractal in nature, especially when you are in a self-induced state where your brain makes a lot of connections you might normally overlook or not even bother to think about.
My point is that discussing things in the universe as self-similar is useful to mathematicians and non-mathematicians alike; using the word "fractal" to describe natural systems that exhibit those familiar patterns might not be perfectly correct, but it's not itself offensive or an affront to reasonable discourse. I manage a business; so what's your problem if I visualize the structure of my company as a fern leaf with departments and employees as branches off the main stem? What would be the issues of discussing how incredible human cellular morphology really is with my biologist roommate, and citing some cool research someone decided to do about fractal geometry in the way our bodies build themselves?
EDIT: OP's edit makes it more clear his statements were more about irrational folk seeing the universe as a single continuous fractal (that would be the "fractal woo"), and that he is not denying the existence of fractal-like patterns in nature, or that using fractal models can be useful in understanding phenomena. Sorry for any confusion and thanks for the discussion!
EDIT2: /u/ombortron commented pretty well in regards to the utility of the concept of fractals in scientific discourse and otherwise:
>The universe itself doesn't have to be a fractal for fractals to be important.
>Fractals are quite common in our reality, and as a result, that means they are an important facet of reality, and as such they are a legitimate and common topic of discussion amongst people, and this is particularly true of people who do psychedelics.
>Does this mean the universe is 100% fractal in nature? No.
yes and no. For beginners, I recommend https://www.amazon.de/Deep-Learning-Python-Francois-Chollet/dp/1617294438 and https://www.reddit.com/r/learnmachinelearning/comments/bmj2zh/get_a_free_early_release_copy_of_handson_machine/
Grad school for machine learning is pretty vague, so here's some general resources I think would be good for an incoming CS grad student or undergraduate CS researcher with a focus on deep learning. In my opinion, the courses you mentioned you've done should be a sufficient foundation to dive into deep learning, but these resources cover some foundational stuff as well.
I'd recommend this book. It's really nice for beginners :D
Reading some books would be a good idea.
The following are textbooks:
General AI
Machine Learning
Statistics for Machine Learning
There are many other topics within AI which none of these books focus on, such as Natural Language Processing, Computer Vision, AI Alignment/Control/Ethics, and Philosophy of AI. libgen.io may be of great help to you.
Artificial Intelligence: A Modern Approach by Norvig and Russell
http://aima.cs.berkeley.edu/
On Amazon
Edit: Good point from nacarlson about the 2nd edition. I've changed the link to reflect that.
Having done an MEng at Oxford where I dabbled in ML, the 3 key texts that came up as references in a lot of lectures were these:
Pattern Recognition and Machine Learning (Information Science and Statistics) (Information Science and Statistics) https://www.amazon.co.uk/dp/0387310738/ref=cm_sw_r_cp_apa_i_TZGnDb24TFV9M
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series) https://www.amazon.co.uk/dp/0262018020/ref=cm_sw_r_cp_apa_i_g1GnDb5VTRRP9
(Pretty sure Murphy was one of our lecturers actually?)
Bayesian Reasoning and Machine Learning https://www.amazon.co.uk/dp/0521518148/ref=cm_sw_r_cp_apa_i_81GnDbV7YQ2WJ
There were ofc others, and plenty of other sources and references too, but you can't go buying dozens of text books, not least cuz they would repeat the same things.
If you need some general maths reading too then pretty much all the useful (non specialist) maths we used for 4 years is all in this:
Advanced Engineering Mathematics https://www.amazon.co.uk/dp/0470646136/ref=cm_sw_r_cp_apa_i_B5GnDbNST8HZR
Thank you all for your responses! I have compiled a list of books mentioned by at least three different people below. Since some books have abbreviations (SICP) or colloquial names (Dragon Book), not to mention the occasional omission of a starting "a" or "the" this was done by hand and as a result it may contain errors.
edit: This list is now books mentioned by at least three people (was two) and contains posts up to icepack's.
edit: Updated with links to Amazon.com. These are not affiliate - Amazon was picked because they provide the most uniform way to compare books.
edit: Updated up to redline6561
Easy. Gödel, Escher, Bach: An Eternal Golden Braid.
Asimov's books went even farther than that. Don't read if you don't want to be spoiled on his most famous scifi series.
[Spoiler](#s "Because Law 1 had the robots take care of humans, the first AIs decided to go out and commit genocide on every alien species in the universe, just so they couldn't compete with humans in the far future.")
AI safety is hard. Thankfully, if you care about actually doing good in real life, there are organizations out there working on this kind of thing. Machine Intelligence Research Institute does research on friendly AI problems; the Center for Applied Rationality promotes increasing the sanity waterline in order to increase awareness of the unfriendly AI problem; the Future for Humanity Institute works on several existential risks, including AI safety.
If you want to learn more about this topic in real life, not just in fiction, then I highly recommend Nick Bostrom's Superintelligence, a book that goes into detail on these issues while still remaining readable by laymen.
Godel Escher and Bach is precisely what you're looking for.
I'm sort of in the same boat as you, except with an aero and physics background rather than EE. My approach has been pretty similar to yours--I found the textbooks used by my alma mater, compared to texts recommended by MIT OCW and some other universities, looked at a few lists of recommended texts, and looked through similar questions on Reddit. I found most areas have multiple good texts, and also spent some time deciding which ones looked more applicable to me. That said, I'm admittedly someone who rather enjoys and learns well from textbooks compared to lectures, and that's not the case for everyone.
Here's what I gathered. If any more knowledgeable CS guys have suggestions/corrections, please let me know.
Full disclosure: I haven't actually read more than the preface of any of those books. Software engineering topics are more directly applicable to me than CS topics right now, so here are some that I've actually started reading:
This is SO important. We should be doing this faster than China.
A branch of artificial intelligence is that of breeding and gene editing. Selectively selecting for genetic intelligence could lead to rapid advances in human intelligence. In 'Superintelligence: Paths, Dangers, Strategies', the most recent book by Oxford professor Nick Bostrum, as well as his paper 'Embryo Selection for Cognitive Enhancement', the case is made for very simple advances in IQ by selecting certain embryos for genetic attributes or even, in this case, breeding for them, and the payoff in terms of raw intelligence could be staggering.
Gödel, Escher, Bach: An Eternal Golden Braid
This book is not exactly a programming book... maybe... kinda. It teaches no practical programming language. It explains no useful design patterns. It does not deal with any practical computer applications. And yet had I never would have really gotten into programming had I not read it long ago. Written in the late 70's, it's still relevant today.
The singularity is near by ray kurzweil has all the science behind it.
Accelerando
and Singularity Sky by Charles Stross for Fiction.
Here's a fantastic book on the subject: Superintelligence.
Godel Escher Bach
It's ultimately about the self-referential nature of consciousness, but it explores so many fascinating concepts that I couldn't even begin to do it justice
AIMA. A real treasure trove!
This was originally posted as an image but got deleted for IMO in this case, the irrelevant reason that picture posts are not allowed, though this was all about the text. We had an interesting discussion going: http://www.reddit.com/r/Futurology/comments/2mh0y1/elon_musks_deleted_edge_comment_from_yesterday_on/
I'll just post my relevant contributions to the original to maybe get things started.
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And it's not like he's saying this based on his opinion after a thorough study online like you or I could do. No, he has access to the real state of the art:
> Musk was an early investor in AI firm DeepMind, which was later acquired by Google, and in March made an investment San Francisco-based Vicarious, another company working to improve machine intelligence.
> Speaking to US news channel CNBC, Musk explained that his investments were, "not from the standpoint of actually trying to make any investment return… I like to just keep an eye on what's going on with artificial intelligence. I think there is potentially a dangerous outcome there."
*Also I love it that Elon isn't afraid to speak his mind like this. I think it might well be PR or the boards of his companies that reigned him in here. Also in television interviews he is so open and honest, too bad he didn't speak those words there.
----------------------------
I'm currently reading Superintelligence which is mentioned in the article and by Musk. One of the ways he describes an unstoppable scenario is that the AI seems to function perfectly and is super friendly and helpful.
However on the side it's developing micro-factories which can assemble from a specifically coded string of DNA (this is already possible to a limited extent). These factories then use their coded instructions to multiply and spread and then start building enormous amount of nanobots.
Once critical mass and spread is reached they could instantly wipe out humanity through some kind of poison/infection. The AI isn't physical, but the only thing it needs in this case is to place an order to a DNA printing service (they exist) and then mail it to someone it has manipulated into adding water, nutrients, and releasing the DNA nanofactory.
If the AI explodes in intelligence as predicted in some scenarios this could be set up within weeks/months of it becoming aware. We would have nearly no chance of catching this in time. Bostrom gives the caveat that this was only a viable scenario he could dream up, the super intelligence should by definition be able to make much more ingenious methods.
For background, I understand that Elon's views are informed by this book (among others, no doubt): Nick Bostrom: Superintelligence.
It's a dense read, but talks about AI and how it might emerge and behave. (I haven't finished the book, so can't say more than that).
Edit: fixed up punctuation from mobile posting. See below for more detail.
Author here. Let me start:
First software company I founded develops software components for other programmers:
https://www.gemboxsoftware.com/
Our customers include NASA, MS, Intel, and US Navy:
https://www.gemboxsoftware.com/company/customers
Second company I co-founded screens programmers before interviews:
https://www.testdome.com/
We are used by Paypal and Ebay, among others.
I finished computer science at University of Zagreb.
I high school, I won 1st place at national computer science competition in 1997. Because of that I attended Central European Olympiad in Informatics, where I got a bronze medal:
https://svedic.org/zeljko/Competitions/ceoi_medalja.jpg
I have also been part of Croatian team at IOI in Capetown:
https://svedic.org/zeljko/Competitions/ioi_team.jpg
Here is my Linkedin profile: https://www.linkedin.com/in/zeljkos/
I don't work in AI, I got the idea while reading Peter Norvig's book:
https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597
Hope I changed your mind about how certain you can be about something just based on the first feeling. My about page was one click away.
Although I really know programming and sell my software to thousands of companies, I have to admit I don't see how that makes my article more or less credible. It is a philosophical text, not text about software. I think you made "Appeal to Authority" logical fallacy:
https://www.logicallyfallacious.com/tools/lp/Bo/LogicalFallacies/21/Appeal-to-Authority
Every article should be judged by its arguments, not the credibility of the author.
The problem is AI alignment: how do we make sure that the AI wants good stuff like "acting like a neutral arbiter" and not bad stuff like "world domination"? This turns out to be a very hard question, and a lot of very smart people believe that a superintelligence would destroy humanity unless we are very very careful. Bostroms Superintelligence is a good introduction to this topic.
> The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. If machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful - possibly beyond our control. As the fate of the gorillas now depends more on humans than on the species itself, so would the fate of humankind depend on the actions of the machine superintelligence.
If you don't have the time for the book, this FAQ is good:
> 4: Even if hostile superintelligences are dangerous, why would we expect a superintelligence to ever be hostile?
> The argument goes: computers only do what we command them; no more, no less. So it might be bad if terrorists or enemy countries develop superintelligence first. But if we develop superintelligence first there’s no problem. Just command it to do the things we want, right?
> Suppose we wanted a superintelligence to cure cancer. How might we specify the goal “cure cancer”? We couldn’t guide it through every individual step; if we knew every individual step, then we could cure cancer ourselves. Instead, we would have to give it a final goal of curing cancer, and trust the superintelligence to come up with intermediate actions that furthered that goal. For example, a superintelligence might decide that the first step to curing cancer was learning more about protein folding, and set up some experiments to investigate protein folding patterns.
> A superintelligence would also need some level of common sense to decide which of various strategies to pursue. Suppose that investigating protein folding was very likely to cure 50% of cancers, but investigating genetic engineering was moderately likely to cure 90% of cancers. Which should the AI pursue? Presumably it would need some way to balance considerations like curing as much cancer as possible, as quickly as possible, with as high a probability of success as possible.
> But a goal specified in this way would be very dangerous. Humans instinctively balance thousands of different considerations in everything they do; so far this hypothetical AI is only balancing three (least cancer, quickest results, highest probability). To a human, it would seem maniacally, even psychopathically, obsessed with cancer curing. If this were truly its goal structure, it would go wrong in almost comical ways.
> If your only goal is “curing cancer”, and you lack humans’ instinct for the thousands of other important considerations, a relatively easy solution might be to hack into a nuclear base, launch all of its missiles, and kill everyone in the world. This satisfies all the AI’s goals. It reduces cancer down to zero (which is better than medicines which work only some of the time). It’s very fast (which is better than medicines which might take a long time to invent and distribute). And it has a high probability of success (medicines might or might not work; nukes definitely do).
> So simple goal architectures are likely to go very wrong unless tempered by common sense and a broader understanding of what we do and do not value.
>The swipe at Andrew Ng is off the mark and tasteless
Meh, it's on about the same level he brought the conversation to. (Oxford professor writes a carefully-argued 350-page book; Ng apparently doesn't see the need to read it and dismisses news coverage of the book with a vague analogy.)
>Yudkowsky and the LessWrong cult have contributed nothing tangible to the fields of AI and machine learning
Well, at least it's consistent with their position that making public contributions to the field of AI may not actually be a good idea :)
It's not like Yudkowsky is somehow unaware that not having an active AI project makes him uncool, here's him writing about the point at which he realized his approach to AI was wrong and he needed to focus on safety:
>And I knew I had to finally update. To actually change what I planned to do, to change what I was doing now, to do something different instead.
>I knew I had to stop.
>Halt, melt, and catch fire.
>Say, "I'm not ready." Say, "I don't know how to do this yet."
>These are terribly difficult words to say, in the field of AGI. Both the lay audience and your fellow AGI researchers are interested in code, projects with programmers in play. Failing that, they may give you some credit for saying, "I'm ready to write code, just give me the funding."
>Say, "I'm not ready to write code," and your status drops like a depleted uranium balloon.
And if you wanna go the ad hominem route (referring to Less Wrong as a "cult" despite the fact that virtually no one who's interacted with the community in real life seems to think it's a cult), I'll leave you with this ad hominem attack on mainstream AI researchers from Upton Sinclair: "It is difficult to get a man to understand something, when his salary depends on his not understanding it."
You might be interested in a book called On Intelligence, by Jeff Hawkins. He describes something similar to your simulations idea, but he calls it a predictive hierarchical memory system (or something like that). It is a fascinating idea, actually, and makes a lot of sense.
I too suspect that speech is a central unifying aspect to what we call consciousness. A lot of AI guys seem to agree. There is a theory by Noam Chomsky (I think), called Universal Grammar. As I recall, he suspects that may be key to modern intelligence, and he suspects the genetic mutation for it happened about 70,000 years ago, which gave us the ability to communicate, and allowed Homo Sapiens to successfully move out of Africa. I've also read that mutation 70k years ago referred to as the cognitive revolution. But it seems everyone agrees that's when the move out of Africa began, and communication started; it's not just a Chomsky thing.
The actual Amazon link
you could use space partitioning. Split the world into grids of say 10 x 10.
let each agent update which grid cell it is on on each frame. Then its just a matter of testing only the agents in the surrounding grid cells from the current agent (which will most of the time be a small number)
This book explains it really well: Programming Game AI by Example. Also has code samples online
The one I always suggest is Programming Game AI By Example. The amount of valuable info you can pull out of this one book is incredible. It's still something I reference years later.
I wrote a quick script to search the full text of HPMOR and return everything italicized and in title case, which I think got most of the books mentioned in the text:
Book title|Author|Mentioned in chapter(s)|Links|Notes
:---|:---|:---|:---|:---
Encyclopaedia Britannica| |7|Wikipedia|Encyclopaedia
Financial Times| |7|Wikipedia|Newspaper
The Feynman Lectures on Physics|Richard P. Feynman|8|Wikipedia|Full text is available online here
Judgment Under Uncertainty: Heuristics and Biases|Amos Tversky|8|Amazon|
Language in Thought and Action|S.I. Hayakawa|8|Amazon Wikipedia |
Influence: Science and Practice|Robert B. Cialdini|8|Wikipedia|Textbook. See also Influence: The Psychology of Persuasion
Rational Choice in an Uncertain World: The Psychology of Judgment and Decision Making|Reid Hastie and Robyn Dawes|8|Amazon |Textbook
Godel, Escher, Bach|Douglas Hofstadter|8, 22|Amazon Wikipedia|
A Step Farther Out|Jerry Pournelle|8|Amazon|
The Lord of the Rings|J.R.R. Tolkien|17|Wikipedia|
Atlas Shrugged|Ayn Rand|20, 98|Wikipedia|
Chimpanzee Politics|Frans de Waal|24|Amazon|
Thinking Physics: Understandable Practical Reality|Lewis Carroll Epstein|35, 102|Amazon|
Second Foundation|Isaac Asimov|86|Wikipedia|Third novel in the Foundation Series
Childcraft: A Guide For Parents| |91|Amazon|Not useful if your child has a mysterious dark side
Also, this probably isn't technically what the OP was asking, but since the script returned fictional titles along with real ones, I went ahead and included them too:
Book title|Mentioned in chapter(s)
:---|:---
The Quibbler|6, 27, 38, 63, 72, 86
Hogwarts: A History|8, 73, 79
Modern Magical History|8
Magical Theory|16
Intermediate Potion Making|17
Occlumency: The Hidden Arte|21
Daily Prophet|22, 25, 26, 27, 35, 38, 53, 69, 77, 84, 86, 108
Magical Mnemonics|29
The Skeptical Wizard|29
Vegetable Cunning|48
Beauxbatons: A History|63
Moste Potente Potions|78
Toronto Magical Tribune|86
New Zealand Spellcrafter's Diurnal Notice|86
American Mage|86
As others mentioned, TVTropes has a virtually-exhaustive list of allusions to other works, which includes books that aren't explicitly named in the text, like Ender's Game
Sounds like you're looking for the statistical proofs behind all the hand waving commonly done by "machine learning" MOOCS. I recommend this book. It's very math heavy, but it covers the underlying theory well.
Code: The Hidden Language of Computer Hardware and Software
The Mythical Man-Month
Peopleware: Productive Projects and Teams
Gödel, Escher, Bach: An Eternal Golden Braid
The Pragmatic Programmer: From Journeyman to Master
Coders at Work: Reflections on the Craft of Programming
http://www.amazon.com/Artificial-Intelligence-Modern-Approach-Edition/dp/0136042597 is what I (and probably most others) would recommend as an introductory book.
This is some kind of weird gatekeeping where AI keeps being redefined until it just means adult human intelligence. I have a textbook that literally has artificial intelligence in the title.
Personally, I think the thing to realize is that if you know how to program, then you know how to model solutions to problems in complex domains. I would then learn more about the mathematical background of modeling complex domains in general and reasoning about them. If you haven't already bought a copy of Artificial Intelligence: A Modern Approach, I highly recommend doing so and working through it, then picking some application to build using what you've learned, e.g. developing a real-estate investment advice system that uses multi-decade trend data across the country, taking into account recent events, to make sound investment advice, learning from experience (i.e. new data) as it goes.
In other words, think
American amazon link
I've posted a similar answer before, but can't find the comment anymore.
If you are interested in doing your own statistics and modeling (like regression modeling), learn R. It pays amazing dividends for anyone who does any sort of data analysis, even basic biostats. Excel is for accountants and is terrible for biological data. It screws up your datasets when you open them, has no version control/tracking, has only rudimentary visualization capabilities, and cannot do the kind of stats you need to use the most (like right-censored data for Cox proportional hazards models or Kaplan-Meier curves). I've used SAS, Stata, SPSS, Excel, and a whole bunch of other junk in various classes and various projects over the years, and now use only R, Python, and Unix/Shell with nearly all the statistical work being in R. I'm definitely a biased recommender, because what started off as just a way to make a quick survival curve that I couldn't do in Excel as a medical student led me down a rabbit hole and now my whole career is based on data analysis. That said, my entire fellowship cohort now at least dabbles in R for making figures and doing basic statistics, so it's not just me.
R is free, has an amazing online community, and is in heavy use by biostatisticians. The biggest downsides are
Unfortunately learning R won't teach you actual statistics.... for that I've had the best luck with brick-and-mortar classes throughout med school and later fellowship but many, many MOOCs, textbooks, and online workshops exist to teach you the basics.
If I were doing it all over again from the start, I would take a course or use a textbook that integrated R from the very beginning such as this.
Some other great statistical textbooks:
Online classes:
So many to choose from, but I am partial to DataCamp
Want to get started?
>prompt in the console:install.packages("swirl")library("swirl")swirl()And you'll be off an running in a built-in tutorial that starts with the basics (how do I add two numbers) and ends (last I checked) with linear regression models.
ALL OF THAT SAID ------
You don't need to do any of that to be a good doctor, or even a good researcher. All academic institutions have dedicated statisticians (I still work with them all the time -- I know enough to know I don't really know what I am doing). If you can do your own data analysis though, you can work much faster and do many more interesting things than if you have to pay by the hour for someone to make basic figures for you.
I personally really benefitted from Jose Portilla's udemy class on python for Data Science: https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp. It deals with the machine learning algorithms at a pretty basic level but he does a good job overviewing things and this course personally gave me more confidence. He also wrote a helpful overview for how to become a data scientist: https://medium.com/@josemarcialportilla/how-to-become-a-data-scientist-2d829fa33aba
Additionally, I found this podcast episode from Chris Albon helpful: http://partiallyderivative.com/podcast/2017/03/28/learning-machine-learning
Finally, I have just started going through Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems and I love it. It's very easy to read and applicable: https://www.amazon.com/dp/1491962291/_encoding=UTF8?coliid=I1VIM81L3W5JUY&colid=2MMQRCAEOFBAX
Hope this helps.
This book Programming Game AI By Example comes highly recommended by me.
It contains all of the above along with an example 2D topdown style deathmatch game with bots to give you a clear understanding of the most common topics in Game AI. It's also one of the more practically focused books, rather than theory focused.
If you like stuff like this you may be interested in my favorite book: Godel, Echer, Bach: The Eternal Golden Braid:
http://amzn.com/dp/0465026567
Edit: Also see the great MIT course with video lectures:
http://ocw.mit.edu/OcwWeb/hs/geb/geb/
Reductionism is important, but pure reductionism denies the existence of emergent phenomena (phenomena that depend on collective behavior of many simpler things). A very enjoyable book that covers this and many other topics at a popularly-accessible level is
Gödel, Escher, Bach: an Eternal Golden Braid. First published in the late 1970s, GEB is still delightfully fresh and exciting although a few minor elements are dated (e.g. computers now can beat humans at chess).
This does nothing to address the difficulty of the control issue. He's basically just saying we'll figure it out before we get AI, don't worry about it.
SuperIntelligence actually spells out why control is so hard. None of those points are touched even generally. He's Director of Engineer at Google, which actually created an AI ethics board because an AI company they bought was afraid that the tech could lead to the end of the human species, yet none of that is even briefly mentioned.
There is very good reason to be cautious around developing an intellect that can match ours, never mind rapidly exceed it. I don't see the necessity for repeated calls to let our guard down.
I'd second unplugging completely: no computer, TV, electronics. If you insist on doing something CS related, Godel, Escher, Bach comes highly recommended.
I've never heard of that book before, but I took a look at their samples and they all seem legitimate.
I would just buy the Ebook for $59 and work through some problems. I'd also maybe purchase some books (or find free PDFs online). Given that you don't have a deep understanding of ML techniques I would suggest these books:
There are others as well, but those are two introductory-level textbooks I am familiar with and often suggested by others.
On the flip side I kind of envy you for your confidence in the underlying math... that shit is melting my brain currently.
Check out https://automatetheboringstuff.com/ great starter book for basic python programming with more of an emphasis on just making a basic program vs the underlying data structures or algorithms. Anyone who says they don't still use these programs or an improved version of one is lying lol
For Machine Learning stuffs: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291 the current cause of my brain melting.
If you don't like the idea of a textbook:
http://interactivepython.org/runestone/static/pythonds/index.html
http://www.fast.ai/ (this is EXTENSIVE I've been working on it on-off for like a year)
Just gonna drop this gem here. http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742
Doesn't have to be skynet level smart to fuck shit up. Also once its self modifying it's a whole other ballgame.
Why would it care if the goal we gave it didn't actually align with what we wanted? It has no reasons to care, unless these things were explicitly coded in, and as I said, morality is super hard to code into a machine.
To address your second point, I understand my example wasn't perfect, but say it understands that the more physical material a company controls, the more assets it has. So it lays claim to the entire universe, and sets out to control it. eventually, it is the company, and growing the company's assets just requires it to have more processing power. Again, it is an illustrative point, loosely derived from my reading of Superintelligence by Nick Bostrom. I would highly recommend it.
If you're interested in learning more about Turing's paper that introduced the Turing Machine, I highly recommend the book The Annotated Turing. It's by the same person who wrote Code, which is an oft-recommended book on this sub-reddit.
Your post has too little context/content for anyone to give you particularly relevant or specific advice. You should list what you know already and what you’re trying to learn. I find it’s easiest to research a new subject when I have a concrete problem I’m trying to solve.
But anyway, I’m going to assume you studied up through single variable calculus and are reasonably motivated to put some effort in with your reading. Here are some books which you might enjoy, depending on your interests. All should be reasonably accessible (to, say, a sharp and motivated undergraduate), but they’ll all take some work:
(in no particular order)
Gödel, Escher, Bach: An Eternal Golden Braid (wikipedia)
To Mock a Mockingbird (wikipedia)
Structure in Nature is a Strategy for Design
Geometry and the Imagination
Visual Group Theory (website)
The Little Schemer (website)
Visual Complex Analysis (website)
Nonlinear Dynamics and Chaos (website)
Music, a Mathematical Offering (website)
QED
Mathematics and its History
The Nature and Growth of Modern Mathematics
Proofs from THE BOOK (wikipedia)
Concrete Mathematics (website, wikipedia)
The Symmetries of Things
Quantum Computing Since Democritus (website)
Solid Shape
On Numbers and Games (wikipedia)
Street-Fighting Mathematics (website)
But also, you’ll probably get more useful response somewhere else, e.g. /r/learnmath. (On /r/math you’re likely to attract downvotes with a question like this.)
You might enjoy:
https://www.reddit.com/r/math/comments/2mkmk0/a_compilation_of_useful_free_online_math_resources/
https://www.reddit.com/r/mathbooks/top/?sort=top&t=all
Have you seen the book The Annotated Turing?
https://www.amazon.com/Annotated-Turing-Through-Historic-Computability/dp/0470229055
Learn some math, yes. Algebra, Discrete Math, Inductive Logic, Set Theory. Calc and Matrix Algebra are good for specific things, and just in general to beef up your math skills. But don't get hung up on it too much. It's a good thing to always have going in the background.
Start to think about how all computation is math - check out The Annotated Turing and really wrap your head around both what Petzold is talking about and what Turing is talking about.
That may require you to take a step back and study Formal Languages, Finite State Machines, and other related concepts (all the stuff that lets you build up to Regular Expressions, etc). Turnings thesis really gets to the heart of a computation, and those concepts build on that.
Then go learn LISP just to bend your brain some more.
Comp Sci is a fascinating subject, and you're off to a good start by thinking about what that Stack Overflow commenter meant - how are all languages similar? How do they differ? What's the underlying problem you're solving, and what are different ways of solving it? How do the tools you choose alter your solution?
Try writing something relatively simple (say, a program that plays Checkers or Tic-Tac-Toe and always wins) in a few different languages (start with ones you know, then learn some new ones to see how - if you've worked with Procedural or OO languages, try Functional ones).
Musk keeps up with what philosopher Nick Bostrom writes. Same reason why he knows about the simulation theory which is also popularised by this philosopher Nick Bostrom. And lo and behold, Bostrom has also a paper and a book on AI.
A scenario where such an AI becomes arbitrarily intelligent and capable of interacting with the outside world isn't beyond the realm of consideration. If it's smart enough to outplan us, a superintelligent Go engine of the future whose primary function is "become better at Go" might cover the world in computer processors. Needless to say, that would be a hostile environment for us...though I imagine such a machine would be frightfully good at Go.
If you're interested in (much) more along these lines, I'd recommend Superintelligence: Paths, Dangers, Strategies by Nick Bostrom. I got it as an audio book, and it's thought provoking.
Very good point- I'm currently reading (or trying to read...) this book:
http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111
I think you'd like it also.
Your brain uses memory as a way to anticipate the effort required in these situations. There is a book called On Intelligence by Jeff Hawkins that discusses this. You've opened thousands of doors, lifted thousands of objects. Your brain remembers how it felt to engage in that activity. So when you approach a door, your brain sees what type of door it is and anticipates how much effort will be required to open it. Sometimes your brain gets it wrong.
edit: a word
I glanced at "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurelien Geron and thought it is quite good. But I have not had a chance to read it deeply yet.
​
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
Somebody proposed a T-Shirt design saying "I broke my back lifting Moloch to Heaven, and all I got was this lousy Disneyland with no Children."
Combines Meditations and Bostrom.
I’d personally recommend Andrew Ng’s deeplearning.ai course if you’re just starting. This will give you practical and guided experience to tensorflow using jupyter notebooks.
If it’s books you really want I found the following of great use in my studies but they are quite theoretical and framework agnostic publications. Will help explain the theory though:
Deep Learning (Adaptive Computation and Machine Learning Series) https://www.amazon.co.uk/dp/0262035618/ref=cm_sw_r_cp_api_i_Hu41Db30AP4D7
Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) https://www.amazon.co.uk/dp/0262039249/ref=cm_sw_r_cp_api_i_-y41DbTJEBAHX
Pattern Recognition and Machine Learning (Information Science and Statistics) (Information Science and Statistics) https://www.amazon.co.uk/dp/0387310738/ref=cm_sw_r_cp_api_i_dv41DbTXKKSV0
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) https://www.amazon.co.uk/dp/B00AF1AYTQ/ref=cm_sw_r_cp_api_i_vx41DbHVQEAW1
For a maths heavy book, I'd go with Bishop's Pattern recognition and Machine Learning.
Check out the reviews here: http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
Absolutely.
Check out The Elements of Statistical Learning and Introduction to Machine Learning.
edit those books are about practical applications of what we've learning to date from the neural network style of pattern classification. So it's not about modeling an actual biological neuron. For modeling of the biology, it's been a while since I futzed with that. But when I wrote a paper on modeling synaptic firing, Polymer Solutions: An Introduction to Physical Properties was the book for that class. Damned if I remember if that book has the details I needed or if I had to use auxiliary materials though.
Our co-founder Eliezer Yudkowsky invented the entire approach called "Friendly AI," and you can read our original research on our research page. It's interesting to note that in the leading textbook on AI (Russell & Norvig), a discussion of our work on Friendly AI and intelligence explosion scenarios dominates the section on AI safety (in ch. 26), while the entire "mainstream" field of "machine ethics" isn't mentioned at all.
From the comments below from /u/Buck-Nasty /u/Jadeyard /u/CyberByte /u/Ken_Obiwan
For those that haven't read it, I can't recommend Superintelligence: Paths, Dangers, Strategies highly enough. It talks about various estimates from experts and really draws the conclusion that, even at the most conservative estimates, it's something we really need to start planning for as it's very likely we'll only get one shot at it.
The time between human-level intelligence and super-intelligence is likely to be very short, if systems can self-improve.
The book brings up some fascinating possible scenarios based around our own crippling flaws, such as we can't even accurately describe our own values to an AI. Anyway, highly recommended :)
Gödel, Escher, Bach: An Eternal Golden Braid by Douglas R. Hofstadter is perhaps the most thought provoking book I have ever read. It unifies music, art, and mathematics and will simply blow your mind.
That's one of my favorite popular science books, so it's wonderful to hear you're getting so much out of it. It really is a fascinating topic, and it's sad that so many Christians close themselves off to it solely to protect their religious beliefs (though as you discovered, it's good for those religious beliefs that they do).
As a companion to the book you might enjoy the Stated Clearly series of videos, which break down evolution very simply (and they're made by an ex-Christian whose education about evolution was part of his reason for leaving the religion). You might also like Coyne's blog, though these days it's more about his personal views than it is about evolution (but some searching on the site will bring up interesting things he's written on a whole host of religious topics from Adam and Eve to "ground of being" theology). He does also have another book you might like (Faith Versus Fact: Why Science and Religion are Incompatible), though I only read part of it since I was familiar with much of it from his blog.
> If you guys have any other book recommendations along these lines, I'm all ears!
You should definitely read The Selfish Gene by Richard Dawkins, if only because it's a classic (and widely misrepresented/misunderstood). A little farther afield, one of my favorite popular science books of all time is The Language Instinct by Steven Pinker, which looks at human language as an evolved ability. Pinker's primary area of academic expertise is child language acquisition, so he's the most in his element in that book.
If you're interested in neuroscience and the brain you could read How the Mind Works (also by Pinker) or The Tell-Tale Brain by V. S. Ramachandran, both of which are wide-ranging and accessibly written. I'd also recommend Thinking, Fast and Slow by psychologist Daniel Kahneman. Evolution gets a lot of attention in ex-Christian circles, but books like these are highly underrated as antidotes to Christian indoctrination -- nothing cures magical thinking about the "soul", consciousness and so on as much as learning how the brain and the mind actually work.
If you're interested in more general/philosophical works that touch on similar themes, Douglas R. Hofstadter's Gödel, Escher, Bach made a huge impression on me (years ago). You might also like The Mind's I by Hofstadter and Daniel Dennett, which is a collection of philosophical essays along with commentaries. Books like these will get you thinking about the true mysteries of life, the universe and everything -- the kind of mysteries that have such sterile and unsatisfying "answers" within Christianity and other mythologies.
Don't worry about the past -- just be happy you're learning about all of this now. You've got plenty of life ahead of you to make up for any lost time. Have fun!
Computer scientist here... I'm not a "real" mathematician but I do have a good bit of education and practical experience with some specific fields of like probability, information theory, statistics, logic, combinatorics, and set theory. The vast majority of mathematics, though, I'm only interested in as a hobby. I've never gone much beyond calculus in the standard track of math education, so I to enjoy reading "layman's terms" material about math. Here's some stuff I've enjoyed.
Fermat's Enigma This book covers the history of a famous problem that looks very simple, yet it took several hundred years to resolve. In so doing it gives layman's terms overviews of many mathematical concepts in a manner very similar to jfredett here. It's very readable, and for me at least, it also made the study of mathematics feel even more like an exciting search for beautiful, profound truth.
Logicomix: An Epic Search for Truth I've been told this book contains some inaccuracies, but I'm including it because I think it's such a cool idea. It's a graphic novelization (seriously, a graphic novel about a logician) of the life of Bertrand Russell, who was deeply involved in some of the last great ideas before Godel's Incompleteness Theorem came along and changed everything. This isn't as much about the math as it is about the people, but I still found it enjoyable when I read it a few years ago, and it helped spark my own interest in mathematics.
Lots of people also love Godel Escher Bach. I haven't read it yet so I can't really comment on it, but it seems to be a common element of everybody's favorite books about math.
Title: On Computable Numbers, with an Application to the Entscheidungsproblem
Authors: Alan Turing
Link: http://plms.oxfordjournals.org/content/s2-42/1/230.full.pdf
Abstract: In just 36 pages, Turing formulates (but does not name) the Turing Machine, recasts Gödel's famous First Incompleteness Theorem in terms of computation, describes the concept of universality, and in the appendix shows that computability by Turing machines is equivalent to computability by λ-definable functions (as studied by Church and Kleene). Source
Comments: In an extraordinary and ultimately tragic life that unfolded like a novel, Turing helped break the German Enigma code to turn the tide of World War II, later speculated on artificial intelligence, fell victim to the homophobic witchhunts of the early 1950s, and committed suicide at the age of 41. Yet Turing is most famous for an eerily prescient 1936 paper in which he invented an imaginary computing machine, explored its capabilities and intrinsic limitations, and established the foundations of modern-day programming and computability. From his use of binary numbers to his exploration of concepts that today's programmers will recognize as RISC processing, subroutines, algorithms, and others, Turing foresaw the future and helped to mold it. In our post-Turing world, everything is a Turing Machine — from the most sophisticated computers we can build, to the hardly algorithmic processes of the human mind, to the information-laden universe in which we live. Source
The Bostrom book is the go-to reference for the sort of ai risk arguments that Musk and others endorse. Elon has previously linked to this WaitBuyWhy post summarizing the argument from the book, so I would read that if you're curious.
(Not that I agree with any of it, but linking since you asked)
I haven't read it myself, but it has been recommended to me. https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
Edit: PM me if you want a free copy(have it in epub, mobi and pdf)
EDIT: I stand corrected, I was thinking of another book that was azw3 format.
The whole terminator / skynet scenario isnt what they are afraid of either. If you would like to know more on the subject matter, I would suggest reading "Superintelligence" by Nick Bostrom. This paints a pretty good picture of the problem with AI. https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742
TL/DR of parts of the book
1: It is stupidly difficult to design an AI that has the same frame of reference as humans. There are many reasons for this, well described in the book. In short, we humans have values such as good and bad, a machine is unlikely to share ours and might aim for success over failiure, without regard to what we would call bad side-effects.
2: This leads to many scenarios where you tell an AI to do a thing, and it does just that, but in a way that will harm humans, not as a means or an end, just as a biproduct. Harm is a broad term. Not needing to kill us, but re-routing all available power to a specific calculation would have serious ramafications for anything using electricity. Using all available resources to create more computers or electricity will also be a big problem for human existance or society as we currently know it. Suggest to read a summary here: https://en.wikipedia.org/wiki/AI_control_problem#The_problem_of_perverse_instantiation:_.22be_careful_what_you_wish_for.22
3 Since 1 and 2 are difficult, its difficult to create reliable safeguards. There is a lot of theory on how to build them, but all in all, they are not easy to do, and whats worse, you often have no idea of knowing that they work until they fail.
4 Since 3 is difficult, corporations or governments might not investigate fully if they have managed to take nececary precautions since they will then maybe fall back in the race of developing said AI. Increasing the risk of a catastropic failure.
5 A self improving general AI will be able to do so in a extremely rapid pace.
6 Combine all of the above and we get the likelyhood of a non zero chance that we develop an AI that we cannot understand (or understands us, or might not care about us for that matter) that we have no way of stopping. Said AI may be doing all in its power to help us with what we are asking of it, and as a byproduct of doing just that, might turn the planet into a giant solarpanel. It is not saying that this is the default outcome, however it is likely. The thing is, if it does, its non-reversible. And currently, we are not sure how to prevent such a scenario.
TLDR/TLDR
Terminator scenario extremely unlikely. What ppl are afraid of are that we might just fuck up because "we are not building skynet, we are making an intelligent paperclip counter!" without realizing that there are big dangers even in this extremely simple scenario.
> I can't help but cringe every time he assumes that self-improvement is so easy for machines so that once it becomes possible at all, AI skyrockets into superintelligence in a matter of weeks.
He doesn't assume it, he concludes it after discussing the topic in depth.
Pages 75-94 of his book. Preview available via Amazon.
Not OP, but among those he listed, I think Chollet's book is the best combination of practical, code-based content and genuinely valuable insights from a practitioner. Its examples are all in the Keras framework, which Chollet developed as a high-level API to sit on top of a number of possible DL libraries. But with TensorFlow 2.0, the Keras API is now fundamental to how you would write code in this pretty dominant framework. It's also a very well-written book.
Ordinarily, I resist books that are too focused on one framework over another. I'd never personally want a DL book in Java, for instance. But I think Chollet's book is good enough to recommend regardless of the platform you intend to use, although it will certainly be more immediately useful if you are working with tf.Keras.
I'm just beginning this journey myself, so judge what I say accordingly.
Artificial Intelligence: A Modern Approach seems to be the most popular textbook.
This article has some seemingly good advice, though it seems to be geared more toward Machine Learning (ML) than AI in general.
I think you'll want to learn a programming language. The above article recommends Python as it is well suited to ML.
There is (was?) a free online course on ML from Stanford by Andrew Ng. I started to take it a couple years ago but never finished. It is very accessible. The lectures appear to be on YouTube.
Grokking Algorithms is a highly regarded book on algorithms.
Make a free Amazon Web Services account and start playing with Sagemaker.
There really is no well defined path to learning AI, in my opinion. It is a highly interdisciplinary endeavor that will require you to be a self-starting autodidact. It's very exciting though. There is still plenty of new ground to be broken. Some might argue it is difficult for the little guy to compete with big labs at the big tech companies with their ungodly amounts of data to feed their AI, but I am optimistic.
Artificial Intelligence: A Modern Approach is a commonly used introductory textbook.
It is hard to provide a "comprehensive" view, because there's so much disperate material in so many different fields that draw upon probability theory.
Feller is an approachable classic that covers all of the main results in traditional probability theory. It certainly feels a little dated, but it is full of the deep central limit insights that are rarely explained in full in other texts. Feller is rigorous, but keeps applications at the center of the discussion, and doesn't dwell too much on the measure-theoretical / axiomatic side of things. If you are more interested in the modern mathematical theory of probability, try Probability with Martingales.
On the other hand, if you don't care at all about abstract mathematical insights, and just want to be able to use probabilty theory directly for every-day applications, then I would skip both of the above, and look into Bayesian probabilistic modelling. Try Gelman, et. al..
Of course, there's also machine learning. It draws on a lot of probability theory, but often teaches it in a very different way to a traditional probability class. For a start, there is much more emphasis on multivariate models, so linear algebra is much more central. (Bishop is a good text).
> Last few weeks I got very interested in AI and can't stop thinking about it. Watched discussions of philosophers about future scenarios with AI, read all recent articles in media about it.
Most likely you heard about the superintelligence control problem. Check out (the sidebar of) /r/ControlProblem and their FAQ. Nick Bostrom's Superintelligence is pretty much the book on this topic, and I would recommend reading it if you're interested in that. This book is about possible impacts of AI, and it won't really teach you anything about how AI works or how to develop it (neither strong nor weak AI).
For some resources to get started on that, I'll just refer you to some of my older posts. This one focuses on mainstream ("narrow"/"weak") AI, and this one mostly covers AGI (artificial general intelligence / strong AI). This comment links to some education plans for AGI, and this one has a list of cognitive architectures.
As far as I'm aware, they don't necessarily believe we are near human level AI. However, they do believe it is an inevitable eventuality (on our current track) that we should begin preparing for now - because if it's done wrong it has catastrophic consequences, while if done right can be the best thing that ever happened to us. I second the recommendation for Bostrom's book.
Read this book: Gödel, Escher, Bach: An Eternal Golden Braid at least 3 times, and take your time.
For some physics :
Penrose - Road to Reality
Gleick - Chaos
Some math/philosophy :
Hofstadter - Godel, Escher, Bach: An Eternal Golden Braid
Anything early by Dawkins if you want to avoid the atheist stuff though his latest is good too.
Anything by Robert Wright for the evolution of human morality.
Pinker for language and the Mind.
Matt Ridley for more biology.
Here's some lighter reading I've liked:
Readable, but more hardcore
Hardware:
Mathy:
Programmy:
Unixy:
Off the wall:
I get teased by people that I am one of only 3 people in the world to have actually finished this book, one of those being the author and the other being the person who recommended to me, but Godel, Escher, Bach: An Eternal Golden Braid was pretty interesting. It covers the profoundness of the topic and is interspersed with Alice in wonderland style dialog that comes at the topic from another angle. Deep but captivating overall.
On a tangent, Goedel's theorem and Turing's incompleteness theorem, along with some other mathematicians who have gazed out over the edge of logic and gone somewhat mad are covered in the BBC documentary Dangerous Knowledge.
Gödel, Escher, Bach: An Eternal Golden Braid by Douglas Hofstadter
If you're having to ask this, it means you haven't read enough textbooks for reading papers to make sense.
What I mean is that to make sense of most research papers you need to have a certain level of familiarity with the field, and the best way to achieve that familiarity is by reading textbooks. Thing is, if you read those textbooks you'll acquire a familiarity with the field that'll let you identify which papers you should focus on studying.
Now go read MLAPP cover to cover.
Against The God: the remarkable story of Risk- Outlines the history of probability theory and risk assessment through the centuries
https://www.amazon.com/Against-Gods-Remarkable-Story-Risk/dp/0471295639/ref=sr_1_1?s=books&ie=UTF8&qid=1475105434&sr=1-1&keywords=against+the+gods
When Genius Failed - A narrative of the spectacular fall of Long Term Capital Management, a hedge fund which had on its board both Myron Scholes AND Robert Merton (you will recall them from MFE)
https://www.amazon.com/When-Genius-Failed-Long-Term-Management/dp/0375758259/ref=sr_1_1?s=books&ie=UTF8&qid=1475105453&sr=1-1&keywords=when+genius+failed
Black Swan/ Antifragility- A former quant discusses the nature of risk in these controversial and philosophical books. Some parts of this book are actually called out and shamed in McDonald's Derivative Markets, one or the both of them are worth reading
https://www.amazon.com/Black-Swan-Improbable-Robustness-Fragility/dp/081297381X/ref=sr_1_1?s=books&ie=UTF8&qid=1475105478&sr=1-1&keywords=black+swan
Godel, Escher, Bach- Very dense look into recursive patterns in mathematics and the arts. While not actuarial, it's obviously very mathematical, a must read.
https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567/ref=sr_1_1?s=books&ie=UTF8&qid=1475105497&sr=1-1&keywords=geb
Endurance- This was recommended to me by a pure mathematics professor. Again, not actuarial, but more about the nature of perseverance though problem solving(sound familiar). It's about Shakleton's famous voyage to the south pole.
https://www.amazon.com/Endurance-Shackletons-Incredible-Alfred-Lansing/dp/0465062881/ref=sr_1_1?s=books&ie=UTF8&qid=1475105520&sr=1-1&keywords=endurance+shackleton%27s+incredible+voyage
Read Hands on Machine Learning with Scikit-learn and Tensorflow. This book is awesome.
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
I like the premise that man creates something that chooses to destroy it in the end. A lot of AI experts raise this as one of the biggest concerns of artificial intelligence. A book called Super Intelligence [0] goes into this. Even Elon Musk, everyone's go to innovative tech thinker, recommends this book as caution against over-doing it with AI.
That being said, everything else was really a let down to me. They just brushed over the fact that David killed all the engineers? Why was the crew so damn stupid and careless? They went to a new planet, breathed the air, interacted with the vegetation, didn't think about quarantining the sick people...I refuse to believe that the 2nd in command would allow the crew to be this bad in ways that are obvious.
The material seems to be too stretched out when we don't need it to be (e.g. existential debate between 2 robots), and then its just thrown at us when I would prefer more detail (david killing all the engineers, understanding the engineers).
0: https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742
Starts in January: https://www.coursera.org/course/aiplan
EDIT: In case you can't wait a month (which according to /u/sovietmudkipz is apparently a completely unreasonable amount of time to wait for a free college course), check out this textbook: it's my understanding that it's basically the gold-standard for intro-AI education.
Well, for AI, you should prepare for a world of math, math, math, along with computer science and programming (obviously). Understanding an historic vision of A.I. is also important, so I would consider starting to read something like this particular book: Artificial Intelligence: A Modern Approach! This a college-level A.I. book, so be patient if there are things you don't fully understand at first. Work hard and you can do anything you set your mind to!
Ask and ye shall receive.
Gnostic Globalists / Fascists
You'll have to search for these in google by title + youtube. Sorry, the links didn't copypasta over.
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Relativism
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Mythologenesis
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Singularity agenda or Identity Apotheosis agenda
Thanateros
Collectivism and the death of the individual
Big Brother / Big Sister
Hijacking to plug a couple of other books really worth reading if you're into this sort of thing:
Radical Abundance: How a Revolution in Nanotechnology Will Change Civilization by K. Eric Drexler
The Singularity is Near by the aforementioned Ray Kurzweil
Everyone should read SuperIntelligence by Nick Bostrom.
Non-affiliate Amazon link: Superintelligence: Paths, Dangers, Strategies
I don't even think we'll get that far. I honestly believe that once we create proper A.I. it will snowball out of control in a matter of months and it will turn against us. Their communist plans are mere fever dream when it comes to A.I. "Well, if the robots are nice to us, don't destroy the human species, and actually are subservient to us, then our Communist fever dream could work"
Yeah, okay, it's like trying to decide whether you want chicken or fish for the in-flight meal while the plane is going down.
I recommend reading Superintelligence if you want to get more theroies about it.
Sotala and Yampolskiy, Bostrom's book, Infinitely descending sequence... by Fallenstein is a really interesting, clever solution to a piece of the puzzle. I'm not sure what you're looking for, particularly; everyone currently working on the question is pretty invested in it, because it's still coming in from the fringe, so it's all going to be people you'll denounce as "not credible".
Fiction: Do Androids Dream of Electric Sheep?. The book that was the basis for Blade Runner.
Non-fiction: Superintelligence: Paths, Dangers, Strategies. This is a deep dive into the dangers posed by superintelligent AI. It's a heavy read.
I've been reading and really enjoying "Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom. https://www.amazon.com/gp/product/B00LOOCGB2
It's an easy read but it's a hard read because every couple sentences your brain wanders off thinking about consequences and stuff and you keep having to read the page over again.
He does a great job of covering in some depth all the issues surrounding the development of trans-human intelligence, whether it happens via "AI", some form of human augmentation, etc.
One of the better "here's a whole bunch of stuff to think about" books.
I really enjoyed On Intelligence:
http://www.amazon.com/Intelligence-Jeff-Hawkins/dp/0805078533/ref=sr_1_1?ie=UTF8&qid=1288992873&sr=8-1
Almost the exact same trajectory as you - graduated with a psych degree, learned a lot of stats and experiment design, then did the Coursera ML course.
Reading this book is probably the biggest thing that took me from knowing there to doing well in interviews (before that it was just scattered projects): https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291 A second edition is coming out pretty soon, so watch out for that.
If I were doing it today, this is probably the best material out there: https://www.dunderdata.com/ It starts from scratch and gives you an amazing tour of Pandas. Author's also working on a practical Machine Learning book.
>Like Elon Musk on AI. There's zero difference between them, they are both ignoramuses spewing bullshit on a subject they know nothing about.
There's at least one difference: Carrey is wrong about vaccines, Musk is right about AI. As it happens, that's the only difference I care about.
> there have been two deaths already
Are you joking? There were almost 30 thousand Model S's on the road in 2017. During that same year 40 thousand people in the US died in car crashes. The Model S is probably the safest car ever made but the only perfectly safe car is one that no one ever drives. Two deaths out of that sample is pretty good, though perhaps not excellent.
Out of curiosity, what are your qualifications to be speaking so strongly on AI? What experts do you read in the field that offer dissenting opinions from Musk, Bostrum, Hinton, or Tegmark? Or, for that matter, everyone that signed this letter?
> The protagonist ship crew aren't real people, they're ones and zeros that Daily created. He is torturing them, no doubt, but it's indistinguishable in many ways from what we do to NPCs in computer games now, just more advanced.
You miss the point of the episode, and presumably of several Black Mirror episodes, if you don't grant than "ones and zeros" can be genuinely conscious.
Roger Ebert famously made the same mistake when reviewing A.I. The entire premise of the movie is that its protagonist, an android named David, is not only conscious and self aware, but experiences emotions just as humans do. Failing to accept that premise, which is careful established in the first scene of the movie, Ebert proceeds to simply not get the movie. He sees an "advanced NPC" where he should be seeing a lonely little boy who happens to be implemented in silicon: "A thinking machine cannot think. All it can do is run programs that may be sophisticated enough for it to fool us by seeming to think. [..] the movie intends his wait to be poignant but for me, it was a case of a looping computer program -- not a cause for tears, but a case for rebooting."
The fact is, you -- your thoughts and emotions -- are produced by perfectly ordinary physics in your brain. We have no reason to believe that we won't be able to someday build machines that do exactly as the brain does. From both a neuroscience, computer science, and physics perspective, we know of nothing that would prevent this, and we're getting close enough now that the potential existential crisis has been talked about lately by a lot of really smart people.
But that's moot, because even if you don't accept that this is possible, it's a fundamental premise of that episode. One of my favorite Ray Bradbury stories involves humans who have crash landed on Mercury. In that story, this causes the human life cycle to accelerate such that we are born, grow to maturity, get old and die, in 8 days. This is obviously not a scientifically plausible premise, but that doesn't matter. It's the setup for the story. It's how that world works, and a logically coherent story, consistent with that world, emerges from that premise.
In this episode, Daily has created AI that can think and feel, just as we do. That's the premise. But he has them captive. He can create and destroy them at will, torture them in unimaginable ways, and that's the major point of the episode. We're on the cusp as a species of actually being able to do this. Not in the glamorized way shown in the episode (at least not at first), where digital minds also have digital bodies and perfect digital worlds where they can basically behave just like humans, but in ways that are potentially much more horrifying.
Imagine that we create the first digital mind by accident, and because of computer speeds, it lives out a subjective 10,000 years in total isolation, with no sensory input, going completely mad before we even figure out what we've done. Imagine that we perfect making digital minds and conscript them to do all our thinking labor for us, as slaves that we boot in a fresh state every morning and reset every night. Imagine that we can have pet minds, as in this episode, and you start to see the dark potential that it speaks to so entertainingly.
Further reading: Superintelligence, but Nick Bostrom (Oxford professor).
> we turn against Daily even though in the end he really is just a creep doing creepy (but legal) things
We turn against him because he's doing flat out evil things. It's completely irrelevant that it's legal. If we see a film of someone whipping their slaves in the 1700s, we turn against them, too, despite the fact that what they're doing is legal. "Legal" does not equal "moral", not in the past, not today, and not in the future.
Check out Programming Game AI by Example and Artificial Intelligence for Games. They are both decent game AI books, the former actually develops an AI for a soccer simulation in chapter four. There's also [Behavioral Mathematics for Game AI] (http://www.amazon.com/Behavioral-Mathematics-Game-Dave-Mark/dp/1584506849/), it expands on the concepts of utility and decision making which are only touched upon in the first two books. It's purely theoretical but very interesting.
You would love Godel Escher Bach by Douglas R Hofstadter. It won the pullitzer prize and is basically just a really good popular math/computer science/art book. But a really excellent jumping off point. Yes it lacks mathematical rigor (of course) but if you are a bright clever person who likes these things, its a must read just for exposure to the inter-connectivity of all of these topics in a very artistic and philosophical way. But be prepared for computer code, musical staff notation, DNA sequences, paintings, and poetry (all themed around Godel, Escher and Bach).
I don't follow?
At some point we started asking questions about the world. There came a time where 'something' emerged in us and we started questioning the world around us.
Questions are investigations about how the world (and here 'world' is everything in the immediate environment) works. This leads to 'what if' scenarios, equivalencies 'is this thing like the other?' and sets 'I belong to the group called 'men', she belongs to the group called 'women'. In the group called 'women' there is the subset of 'women' that are my offspring. Godel, Escher, Bach yourself on sets and other concepts.
So, we learned how to ask questions and the answers to those questions lead to more questions. All this leads to the internet and us meeting. Our interaction is the result of an unbroken chain of questions that has brought us from the savanna all the way to here. Think about that.
What a great question, and an interesting example. For those confused by OP's example, check out Gödel's Incompleteness Theorem on Wiki. Better yet, read the insightful and very trippy Pulitzer Prize winning book, Gödel, Escher, Bach. Gödel's theorem is a bit abstract but it was both a monumental and surprising discovery. It's not just mathematical -- it's meta-mathematical, in that it reveals the limitations inherent to any mathematical framework or system. From wiki:
>The first incompleteness theorem states that no consistent system of axioms...is capable of proving all truths about the relations of the natural numbers (arithmetic). For any such system, there will always be statements about the natural numbers that are true, but that are unprovable within the system. The second incompleteness theorem, an extension of the first, shows that such a system cannot demonstrate its own consistency.
I'll point out an obvious one, though it's more to do with the aesthetics of the psychedelic experience rather than insights or ideas. Psychedelic hallucinations tend to be geometric, with lattices, grids, spirals, and perhaps most intriguing of all, fractals. All these are geometric forms that can be rigorously defined and analyzed by math. Fractals are especially fascinating because they exhibit self-similarity at every scale, appear sometimes in nature (for example, coastlines), and look extremely trippy. (Seriously, just look at these zoom-ins of the Mandelbrot set, discovered in 1978.)
Gödel, Escher, Bach: An Eternal Golden Braid by Douglas F. Hofstadter. The section on Ant Fugue blew my mind away.
Yes, and if you want to follow this deeper into the context of consciousness, check out this book: GEB
There's a series of chapters discussing the localization of brain functions. The author discusses a study on rat brains, in which maze-running rats had significant portions of their brains removed, and were allowed to heal. Most rats were still able to re-learn the maze.
You might like this [book] (https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567)...Gödel, Escher, Bach: An Eternal Golden Braid, the cover has an even better whatever you call that effect/illusion.
In Search Of Schrodinger's Cat by John Gribbin is a very readable physics and quantum physics history sketch. Might be slightly dated now, although I can't think of anything directly contradicted by recent work. Then again, I'm not actually a physicist :)
The Quark and the Jaguar is quite a bit more complicated, but still quite accessible to the layperson and has a lot of interesting stuff.
Slightly less sciency, more maths/logic/computation is Gödel, Escher, Bach: An Eternal Golden Braid
A Guinea Pig's History of Biology is pretty much what the title says, although there's an awful lot about fruit-flies too. Quite a good review of the history of biological experimentation, especially genetics.
H2O: A Biography of Water from a previous editor of Nature, covers water across a variety of fields. The second half of the book is mostly a rant about cold fusion and homoeopathy though, from what I recall, but the first half makes up for it.
Most general-audience things by Richard Feynman are well worth the read. He's got some great physics lectures, and his autobiography (Surely You're Joking, Mr Feynman?) is fun, but more for the anecdotes than the science.
Those are off the top of my head. If its something in a particular field, I might have some other ideas I'm currently forgetting.
Operating Systems Concepts (AKA The Dinosaur Book) is generally quite well regarded.
Artificial Intelligence: A Modern Approach tends to be the text of choice for teaching AI to undergraduates - it doesn't deal with many of the most modern techniques, but it establishes the common functionalities.
Ishmael - If you ever wondered what it would be like to be a telepathic gorilla, this will probably give you the closest answer.
The 5 Elements of Effective Thinking - The INTP Toolbox.
The Willpower Instinct - Because we all know we could use a bit more of it around here...
Emotional Vampires - A survival guide to protect your Fe
How To Create A Mind - Since it's ultimately the only thing we really seem to care about, it's interesting to think how we could theoretically create a 'backup' for it eventually
The Talent Code - In case you haven't quite figured out how to go about mastering skills yet.
The Swedish philosopher Nick Bostrum wrote a book called Superintelligence that covers much of this topic. I'd recommend it to anyone as it's not technical at all.
He maintains a strong position that the dangers of AI are many and serious, possibly existential. Finding solutions to these problems is an extremely arduous task.
It's even more interesting if you don't just think him as the standard insane genius trope, but realize that he is probably genuinely disturbed and conflicted about what he's created and what to do with it.
Trying not to be spoiler-y here for people who haven't seen the movie but there are probably a lot of practical and metaphysical questions weighing on him. Is an AI truly a conscious creature? Does it have wants? If so, what would an AI want? Given that its social manipulation, long-game planning, and deception abilities are off the charts how could we ever be sure that what it told us was the truth? Does it have any moral considerations toward humans? How would we ever be able to contain it if we needed to? And if it is a conscious creature worthy of moral consideration then what are the moral ramifications of everything he's done with it so far?
Really interesting stuff. For those inclined I recommend checking out the book Superintelligence by Nick Bostrom as it explores these themes in depth.
This illustrates why we need to be careful with AI. A superintelligent AI given the directive to maximize human happiness might just stick electrodes in everyone's pleasure centers, or start an intensive, mandatory breeding program, because more humans = more happiness. It might be fully aware that that's not what we meant, but it's what we said...
(Yeah, I'm reading Nick Bostrom's book.)
I couldn't have said it better myself. I read Superintelligence by Nick Bostrom (which is an insanely good read by the way) earlier this year and was becoming more and more worried that there was no one stepping up to the plate to spearhead a movement like this, at least nothing of this magnitude. To know that people like Elon Musk, Reid Hoffman, and Ilya Sutskever are behind this gives me hope that maybe we can emerge on the other side of the intelligence explosion relatively unscathed.
Try going through the Kevin Murphy book : https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020
> I think that more people will be deterred by a focus on AI safety. It's worse for EA if people think "These people are weird nuts" than "These people are somewhat liberal."
But raw amount of support is not the objective that EA is supposed to be trying to maximize. If that support comes at the cost of making EA into a subset of left-wing political activism, and if an ever increasing proportion of EA stuff gets funneled into social justice and all the standard left-wing culture war causes, then we will be left with a movement that is EA in name only.
AI safety is not as far-out as it was 10 years ago. If someone looks at AI safety and people who support it - such as Stephen Hawking, Elon Musk, Bill Gates, Nick Bostrom and the guy who co-wrote the textbook on AI - and are turned off by it because they think it is crazy, well maybe they have been successfully filtered out as people not possessing the required level of rationality to be beneficial to the movement? I wouldn't have made this argument even 5 years ago actually, because AI risk looked so superficially dodgy even though the arguments are sound.
No no, he's Anti-AI: A Modern Approach
Actually, it's a pretty comprehensive take on the subject: http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834/
Check out The Annotated Turing by Charles Petzold. It's Turing's paper on the Entscheidungsproblem which introduces Turing Machines, annotated with a lot of background information and some stuff about Turing's career. Very interesting stuff.
I can also recommend Code, by the same author which describes how a computer works from basic principles. It's doesn't have a lot of material on Turing, but it's certainly an interesting read for anyone interested in Comp Sci.
Alan Turing's original paper that introduced the Turing Machine is a great read. The Annotated Turing is a great way to both read and understand the paper if you don't have a background in compsci. It doesn't assume much more than highschool math, and the whole of Turing's paper is inline with the explanations.
He is amazingly articulate on this subject, probably more so than anyone. I really enjoyed his book Superintelligence.
Don't worry, you've demonstrated the ability to figure out whatever you need to get hired, you need to worry more about getting a place to live. probably you shd buy one of those shirts that says "Keep calm and carry on". You could cram on java performance tuning or kernel methods or hadoop or whatever and be handed a project that doesn't use it. Here's some "curricula", free books etc
http://web.archive.org/web/20101102120728/http://measuringmeasures.com/blog/2010/3/12/learning-about-machine-learning-2nd-ed.html
http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science
http://metaoptimize.com/qa/questions/186/good-freely-available-textbooks-on-machine-learning
http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/product-reviews/0262018020/ (first review)
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http://people.seas.harvard.edu/~mgelbart/glossary.html
http://www.quora.com/Machine-Learning
http://www.quora.com/Machine-Learning-Applications
I have it. Pretty heavy for my tiny brain.
But, anyway, Amazon has it for ~$56. In the world of expensive textbooks, this is a steal.
https://www.amazon.com/dp/0262035618/ref=cm_sw_r_cp_apa_i_G4DJDbH8JXJ71
Take the online course by Andrew Ng and then read Python Machine Learning.
If you then become really serious about Machine Learning, read, in this order,
Vezi si fast.ai, au 4 cursuri foarte bune. Apoi si asta e bun. Hugo Larochelle avea un curs de retele neuronale, un pic mai vechi.
La carti as adauga si The Hundred Page Machine Learning Book si asta , probabil cea mai buna carte practica, da asteapta editia a 2a, cu tensorflow 2.0, are tf.keras.layers, sequential model, practic tf 2 include keras si scapi de kkturile alea de sessions. Asa, si ar mai fi si asta, asta si asta. Nu pierde timp cu cartea lui Bengio de deep learning, e o mizerie superficiala. Spor la invatat si sa vedem cat mai multi romani cu articole pe ML si DL!
When I started on the field I took the famous course on Coursera by Andrew Ng. It helped to grasp the major concepts in (classical) ML, though it really lacked on mathematical profundity (truth be told, it was not really meant for that).
That said, I took a course on edX, which covered things in a little more depth. As I was getting deeper into the theory, things became more clear. I have also read some books, such as,
All these books have their own approach to Machine Learning, and particularly I think it is important that you have a good understanding on Machine Learning, and its impacts on various fields (signal processing, for instance) before jumping into Deep Learning. Before almost three years of major dedication in studying the field, I feel like I can walk a little by myself.
Now, as a begginer in Deep Learning, things are a little bit different. I would like to make a few points:
So, to summarize, you need to start with simple, boring things until you can be an independent user of ML methods. THEN you can think about state-of-the-art problems to solve with cutting-edge frameworks and APIs.
A good textbook will do you wonders. Get one that is fairly general and includes exercises. Do the exercises. This will be hard, but it'll make you learn an enormous amount faster.
My personal favourite book is Christopher Bishop's Pattern Recognition and Machine Learning. It's very comprehensive, has a decent amount of maths as well as good examples and illustrations. The exercises are difficult and numerous.
That being said, it is entirely Machine Learning. You mention wanting to learn about 'AI' so potentially you may want to look at a different book for some grounding in the wider more classical field of AI than just Machine Learning. For this I'd recommend Russel and Norvig's [AI: A Modern Approach](https://smile.amazon.co.uk/Artificial- Intelligence-Modern-Approach-Global/dp/1292153962). It has a good intro which you can use to understand the structure and history of the field more generally, and following on from that has a load of content in various areas such as search, logic, planning, probabilistic reasoning, Machine Learning, natural language processing, etc. It also has exercises, but I've never done them so I can't comment much on them.
These two books, if you were to study them deeply would give you at least close to a graduate level of understanding. You may have to step back and drill down into mathematical foundations if you're serious about doing exercises in Bishop's book.
On top of this, there are many really good video series on youtube for times when you want to do more passive learning. I must say though, that this should not be where most of your attention rests.
Here are some of my favourite relevant playlists on YouTube, ordered in roughly difficulty / relevance. Loosely start at the top, but don't be afraid to jump around. Some are only very tenuously related, but in my opinion they all have some value.
Gilbert Strang - Linear Algebra
Gilbert Strang - Calculus Overview
Andrew Ng - Machine Learning (Gentle coursera version)
Mathematical Monk - Machine Learning
Mathematical Monk - Probability
Mathematical Monk - Information Theory
Andrew Ng - Machine Learning (Full Stanford Course)
Ali Ghodsi - Data Visualisation (Unsupervised Learning)
Nando de Freitas - Deep Learning
The late great David MacKay - Information Theory
Berkeley Deep Unsupervised Learning
Geoff Hinton - Neural Networks for ML
Stephen Boyd - Convex Optimisation
Frederic Schuller - Winter School on Gravity and Light
Frederic Schuller - Geometrical Anatomy of Theoretical Physics
Yaser Abu-Mostafa - Machine Learning (statistical learning)
Daniel Cremers - Multiple View Geometry
In Natural Language Processing, it's Jurafsky and Martin. In Machine Learning, it's debatably the Bishop book.
http://www.mathman.biz/
http://wheelof.com/whitney/index.php?var=v1
http://vihart.com/
http://www.go.kestrel.nu/
http://www.amazon.com/Escher%C2%AE-Pop-Ups-Courtney-Watson-McCarthy/dp/0500515905
http://www.amazon.com/Alexs-Adventures-Numberland-Alex-Bellos/dp/1408809591
http://www.amazon.co.uk/You-Can-Cube-Patrick-Bossert/dp/0141326506
http://www.amazon.co.uk/Godel-Escher-Bach-Eternal-Golden/dp/0465026567
http://www.amazon.com/Mathematics-Very-Short-Introduction-Introductions/dp/0192853619
http://www.amazon.com/Project-Origami-Activities-Exploring-Mathematics/dp/1568812582
http://www.amazon.com/Origami-Tessellations-Awe-inspiring-Geometric-Designs/dp/1568814518
http://www.amazon.com/Proofs-without-Words-Exercises-Classroom/dp/0883857006
Once I got started ....whew. good luck!
It becomes useful to replace concepts such as equivalence relations (and other relations) with symbols in order to facilitate understanding, actually. I'm sure you've used the =, <, >, the greater-than-or-equal-to, or the less-than-or-equal-to symbols before. These symbols allow those that read equations, definitions, or proofs to quickly and unambiguously understand what is being discussed. If you end up studying higher math for a while, you become familiar and comfortable with this style of notation.
Interestingly, notation like this and the thought process it represents is important in understanding the structure of mathematical logic, forms a large part of the basis of automata theory (aka why you're able to enjoy complex technology, like computers), and may hold key insights into the nature of consciousness and sentience itself.
If you've got the stomach for the notation, wide worlds of fascinating information await!
Hofstadter's Gödel, Escher, Bach
If you like things a bit more prose-y, try Pirsig's Zen and the Art of Motorcycle Maintenance
Not specific to algorithms or even to CS, but Douglas Hofstadter (Gödel, Escher, Bach, I Am a Strange Loop) touches on many of the logical fundamentals in a relatively layman-digestable manner.
I wouldn't call him easy reading compared to Sagan or Kaku, and don't know a "pop computer science" equivalent to those two, but you definitely don't need a CS or math degree to get through GEB. Whether it's on-topic enough here is definitely questionable.
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Edit: I haven't read it, but from the description this one by Thomas Cormen looks like it might be close to what you're looking for: Algorithms Unlocked.
"This is a unique book in its attempt to open the field of algorithms to a wider audience. It provides an easy-to-read introduction to an abstract topic, without sacrificing depth."
From the TOC, it looks like it's probably fairly light on math but gets into code or pseudocode relatively quickly. I still wouldn't call it pop-CS, but if that sounds like a fit, maybe give it a shot!
Go read Godel Escher Bach
Here's the inevitable recommendation for Gödel, Escher, Bach (Amazon page, so you can see the reviews).
Synopsis:
>Twenty years after it topped the bestseller charts, Douglas R. Hofstadter's Gödel, Escher, Bach: An Eternal Golden Braid is still something of a marvel. Besides being a profound and entertaining meditation on human thought and creativity, this book looks at the surprising points of contact between the music of Bach, the artwork of Escher, and the mathematics of Gödel. It also looks at the prospects for computers and artificial intelligence (AI) for mimicking human thought. For the general reader and the computer techie alike, this book still sets a standard for thinking about the future of computers and their relation to the way we think.
>Hofstadter's great achievement in Gödel, Escher, Bach was making abstruse mathematical topics (like undecidability, recursion, and 'strange loops') accessible and remarkably entertaining. Borrowing a page from Lewis Carroll (who might well have been a fan of this book), each chapter presents dialogue between the Tortoise and Achilles, as well as other characters who dramatize concepts discussed later in more detail. Allusions to Bach's music (centering on his Musical Offering) and Escher's continually paradoxical artwork are plentiful here.
It may be strange, but during the biology and nature-of-thought-related sections of GEB I decided to read the neurology chapters of Gray's Anatomy (no, not Grey's Anatomy). It's pretty heady and slows you down quite a bit, but it results in a really interesting mix of deep biological knowledge about the structure of neurons and functioning of the nervous system with GEB's higher-level, cognition-focused discussion.
Note that that's the 40th, British edition of Gray's Anatomy. There are cheaper ones if you don't need the most up-to-date version, including leather-bound reprints of the classic 1901 American reprint. I doubt the old versions have much accurate information about neurology, however.
Some good readings from the University of Cambridge Mathematical reading list and p11 from the Studying Mathematics at Oxford Booklet both aimed at undergraduate admissions.
I'd add:
Prime obsession by Derbyshire. (Excellent)
The unfinished game by Devlin.
Letters to a young mathematician by Stewart.
The code book by Singh
Imagining numbers by Mazur (so, so)
and a little off topic:
The annotated turing by Petzold (not so light reading, but excellent)
Complexity by Waldrop
Elon Musk does one tweet and people lose their minds. Here is the book he was talking about.
Depends on what you are interested in.
If you are interested in games, pick a game and do it. Most board games are not that hard to do a command line version. A game with graphics, input, and sound isn't too bad either if you use something like Allegro or SDL. Also XNA if you are on windows. A lot of neat tutorials have been posted about that recently.
If you are more interested in little utilities that do things, you'll want to look at a GUI library, like wxWidgets, Qt and the sort. Both Windows and Mac have their own GUI libraries not sure what Windows' is called, but I think you have to write it with C++/CLI or C#, and Mac is Cocoa which uses Objective-C. So if you want to stick to basic C++ you'll want to stick to the first two.
Sometimes I just pick up a book and start reading to get ideas.
This is a really simple Game AI book that is pretty geared towards beginners. http://www.amazon.com/Programming-Game-Example-Mat-Buckland/dp/1556220782/
I enjoyed this book on AI, but it is much more advanced and might be kind of hard for a beginner. Although, when I was first starting, I liked getting in over my head once in a while. http://www.amazon.com/Artificial-Intelligence-Modern-Approach-2nd/dp/0137903952/
Interesting topics to look up.
Data Structures
Algorithms
Artificial Intelligence
Computer Vision
Computer Graphics
If you look at even simple books in these subjects, you will usually find tons of small manageable programs that are fun to write.
EDIT: Almost forgot, I think a lot of these are Java based, but you can usually find a way to do it in C++. http://nifty.stanford.edu/ I think I write Breakout whenever I am playing with a new language. heh
You bring up an excellent point (and make a great case for land ownership!), and that is that actual physical space can't really be created, and will remain scarce, insofar as Earth has a fixed surface area. If the scenario I described above came to pass, though, would any landlords come looking for rent? Would any governments levy taxes? If no one needs cash and everyone has pretty much everything provided for them, all but the most stubborn landlords won't have any reason to give a hoot. I suspect government would take longer to die out, since it may still be needed to enforce laws, judge disputes, provide safety, etc. It's not hard to imagine a world even further down the line, however, when technology has advanced to the point where humans can't realistically do much damage to one another.
Edit: If you're really into this, I'd suggest reading some singularity-esque literature such as Down and Out in the Magic Kingdom (novella), Rainbows End (novel), and The Singularity is Near (speculative nonfiction to be taken with a grain of salt).
You REALLY need to read this book. Infact, if you think you would actually read it I'll buy it for you.
I don't know who is doing PR for this book but they are amazing. It's not a good book.
My review on Amazon:
> The most interesting thing about this book is how Bostrom managed to write so much while saying so little. Seriously, there is very little depth. He presents an idea out of nowhere, says a little about it, and then says [more research needs to be done]. He does this throughout the entire book. I give it two stars because, while extremely diluted, he does present an interesting idea every now and then.
Read this or this or this instead.
https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834/ref=sr_1_1?ie=UTF8&amp;qid=1478996520&amp;sr=8-1&amp;keywords=superintelligence
>I didn't say it was correct but it makes it more likely that people will dismiss it out of hand.
That's fair, I agree. It's just frustrating: there are so many strawmen arguments related to AI that a huge number of intelligent people dismiss it outright. But if you actually look into it, it's a deeply worrying issue-- and the vast majority of people who actually engage with the good arguments are pretty damn concerned.
I would be very interested if anyone can produce a compelling rebuttal to the main points in Superintelligence, for instance. I recommend this book very highly to anyone, but especially people who wonder "is AI safety just bullshit?"
>Especially when those people get significant amounts of funding
Numerically speaking, this is inaccurate. Cf. this article.
I'd grab beautifulsoup + scikit-learn + pandas from continum.io (they're part of the standard anaconda download), launch Spyder and follow through this:
http://sebastianraschka.com/Articles/2014_naive_bayes_1.html
You can get a RAKE impl here too : https://github.com/aneesha/RAKE
Doing recommendations on the web like that is covered in an accessible way in "Programming Collective Intelligence"
Introduction to Algorithms is a behemoth text book. I prefer O'Reilly's Algorithms in a Nutshell and also Programming Collective Intelligence" for basic ML stuff.
"and what exactly does 'learning' mean?"
I stopped there.
Read a book.
http://www.amazon.com/Intelligence-Jeff-Hawkins/dp/0805078533/ref=sr_1_1?ie=UTF8&amp;s=books&amp;qid=1252350466&amp;sr=8-1
On intelligence by Jeff Hawkins is an amazing book an artificial intelligence. Hawkins' company has an open source project called [NuPIC] (http://numenta.org/) that would be a good place to get some hands on experience. It is Python based, and has a somewhat steep learning curve, so it might serve better as a beacon that you can work towards, rather than an actual project as of right now.
This is a great book. The other book that is a bit less mathematical in nature, and covers similar topics, is Introduction to Statistical Learning. It is also a good one to have in your collection if you prefer a less mathematical treatment. https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370
100x though, that's a bit much :) If you read effectively and take notes effectively, you should only have to go through this book with any depth 1 time. And yes, I did spend time learning how read books like this, and it's worth learning!
Another great book for me was Hands-on Machine Learning with scikit learn and TensorFlow. This one I think is where I started to get some intuition on what some of the ML algo's were actually doing, and he provides lots of material on algorithm tuning.
nope, humans can barely do it. you need general artificial intelligence first, then if it progressed to super artificial intelligence then yes.
if youre interested in what im talking about read these two articles. second article is where it gets good. but you need to read both.
https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html
https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-2.html
more heavy reading
https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742
I'll start off with some titles that might not be so apparent:
Unexpected Fundamentals
These 2 books provide much needed information about making reusable patterns and objects. These are life saving things! They are not language dependent. You need to know how to do these patterns, and it shouldn't be too hard to figure out how to implement them in your chosen language.
&nbsp;
Good General book
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This book is great if you're going to make a browser based game
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General Knowledge books
&nbsp;
Provide a working moveable 3D model with C++ and DirectX, Very cool.
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More general game base building
&nbsp;
Working product results books, little if any modification needed
Releasing in a couple months (hopefully) 2 Very good books using C++ to develop by.
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Not presented in the best manner but still noteworthy:
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I used to love XNA...but now it's not feasible for commercial development. If you're a beginner to game design...starting out with XNA might actually be useful. It's easy to pickup and put out a working product. XNA is C#
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Working product books, modification needed to make run on current systems
Provides a working FPS game in C++ on DirectX 9. Good for some starting out knowledge for an FPS
&nbsp;
Good for 3D Terrain rendering in DX9...however much of this is outdated...some concepts still apply, and it's not the worst idea to see a working example.
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TLDR: Click links starting at top, buy, read, profit
I've posted this before but I'll repost it here:
Now in terms of the question that you ask in the title - this is what I recommend:
Job Interview Prep
Junior Software Engineer Reading List
Read This First
Fundementals
Understanding Professional Software Environments
Mentality
History
Mid Level Software Engineer Reading List
Read This First
Fundementals
Software Design
Software Engineering Skill Sets
Databases
User Experience
Mentality
History
Specialist Skills
In spite of the fact that many of these won't apply to your specific job I still recommend reading them for the insight, they'll give you into programming language and technology design.
If you like the video, you'll love the book!
Someone's been reading Hawkins
It is like any other job, if not harder. You are entirely responsible for your decisions here. No boss to complain of, no sabotaging co-workers to blame. Just you and your decisions. And it will demand your devotion beyond the 9-5 job. You'll be on charts and reading analyses during weekends, trying to understand the political environment surrounding the instrument you are trading. And still, you may (or will) fail. Markets gonna do what markets gonna do. The only variable in your control is your reaction to it.
To get a feel of what kind of stuff you would be dealing with, check out some books that have a more rigorous foundation for trading:
The last one is not too important for Forex, but it is necessary to better understand other financial instruments and appreciate the deeper foundations of Finance.
I think books 1 & 2 are absolutely necessary. Consider these as "college textbooks" that one must read to "graduate" in trading. May be thrown in Technical Analysis of the Financial Markets, so you get the "high school" level knowledge of trading (which is outdated, vague, qualitative and doesn't work). We are dealing with radical uncertainty here (to borrow a phrase from The End of Alchemy), and there needs to be some way for us to at least grasp the magnitude of what few uncertain elements we can understand. Without this, trading will be a nightmare.
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron
These two books were bigly for me:
I don't have a tutorial for TensorFlow, but Hands on Machine Learning with Scikit-Learn and TensorFlow (https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_1?ie=UTF8&amp;qid=1500494347&amp;sr=8-1&amp;keywords=hands+on+machine+learning) should basically be the bible of machine learning implementation.
XGboost, the best way I learned it, Is through looking at Kaggles.
Inteligência Artificial é de longe o maior problema da humanidade. O risco dela causar a extinção, ou pior, a criação de quantidades astronômicas de sofrimento é bastante alta, e pouquíssimas pessoas estão se dedicando a solucionar o problema.
A quem se interessar pesquisem por "AI alignment" no Google.
EDIT: Pra quem tiver interesse:
https://en.wikipedia.org/wiki/Friendly_artificial_intelligence
https://en.wikipedia.org/wiki/Existential_risk_from_artificial_general_intelligence
Playlist do numberphile que dá uma introduzida muito boa no tema
TED do Sam Harris sobre o assunto
Pra quem tiver muito interesse no assunto, o livro Superintelligence do pesquisador Nick Bostrom, da University of Oxford é o responsável por "evangelizar" muita gente no assunto, inclusive Elon Musk e Bill Gates (que já comentaram sobre o livro). Mole de achar versão dele em pdf na internet.
If you're looking to actually learn the stuff, then you will need to get textbooks which are plentiful. But if you're looking to read about the philosophical side of the topic, I suggest you start with Nick Bostrom's Superintelligence.
Oh, and make sure you watch Her and Ex Machina if you haven't already ;)
Eclipse Phase is a great way to find plot hooks, they're littered in all of the source books. It's also free, so just check it out even if you want to look at pretty pictures.
I'm also reading Superintelligence and that book is basically a section by section deconstruction of why building a Seed AI (a self improving AI, a staple of the sci-fi genre) will end badly.
The Starcraft Broodwar API has source code for a bunch of bots from the annual competition at AIIDE. You can find them here. They use a variety of techniques that will help you set you in the right direction.
I'd recommend this book too if you're interested in AI. It's the most comprehensive survey of the most common techniques used in the industry today.
Good luck!
Programming Game AI by Example is another great resource.
Book suggestions? Now that's my jam.
Out of all the books i've read, here are my recommendations regarding game programming:
Eric Lengyel's Books (only one out so far). This is aimed at game engine development, but if the 2nd onward are as indepth as the first, they will be amazing fundamental knowledge. Also, they're not thick, and jam packed with information.
Game Programming Patterns. The only book that comes more recommended than this is the one right below it by Jesse Schell. This book is fantastic, but you should write one or two small games to really get the most out of this book. You can also read it online on his website free, but then you don't get a pic of him and his dog on the back cover.
Book of Lenses. This is your intro/intermediate dive into game design. There are a lot of game design books, if you only read one, it should be this one.
Gane AI By Example. This book is a hodgepodge of fantastic techniques and patterns by those in AAA. There are other books on the series (like Game AI Pro) which are similar, but in my opinion (at least when I read AI PRO 3), they're not as good. But more knowledge is never bad.
Truthfully, as I sit here looking over all my books, those are the only ones i'd consider mandatory for any seasoned developer. Of course plenty of developers get by without reading these books, but they likely pick up all the principles listed herein elsewhere, in bits and pieces, and would likely have benefited having read them early on.
Here are a few others that I do recommend but do NOT consider mandatory. Sorry, no links.
Unity in Action. Personally, I recommend this or a more interactive online course version (udemy.com/unitycourse) if you want to learn unity while having a resource hold your hand. Having read the book, taken the course, AND taken Unity's own tutorials on the matter, i'd order them in order from Course being best, book second, videos from unity third. But none of them are bad.
Game Engine Architecture. This is the king for those who want a very broad introduction to making a game engine. It comes highly recommended from nearly anyone who reads it, just so long as you understand it's from a AAA point of view. Game Code Complete is out of print and unlikely to be revisited, but it is similar. These are behemoths of books.
Realtime rendering. This is one I haven't read, but it comes very highly recommended. It is not an intro book, and is also over 1000 pages, so you want this along side a more introductory book like Fundamentals of computer graphics. Truth be told, both books are used in courses in university at the third and fourth year levels, so keep that in mind before diving in.
Clean code. Yeah yeah it has a java expectation, but I love it. It's small. Read it if you understand Java, and want to listen to one of the biggest preachers on how not to write spaghetti code.
Rimworld guy, Tynaan sylvester I believe, wrote a book called Designing Games. I enjoyed it, but IMO it doesn't hold a candle to Jesse Schell's book. Either way, the guy did write that book after working in AAA for many years, then went on to create one of the most successful sim games in years. But yeah, I enjoyed it.
Last but not least, here are some almost ENTIRELY USELESS but interesting diagrams of what some people think you should read or learn in our field:
https://github.com/miloyip/game-programmer
https://github.com/utilForever/game-developer-roadmap
https://github.com/P1xt/p1xt-guides/blob/master/game-programming.md
They've got a prototype that's learned how to navigate a 2d maze that features doors requiring activation of switches that are in different parts of the maze, behind other doors.
They've got a prototype that learned how to manipulate rotors inside Space Engineers to allow a contraption to "walk."
Exciting things are coming, that's for sure.
These books are available in India
http://www.amazon.in/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020
http://www.amazon.in/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_1?s=books&amp;ie=UTF8&amp;qid=1502576905&amp;sr=1-1&amp;keywords=Pattern+Recognition+and+Machine+Learning+by+Christopher+Bishop
http://www.amazon.in/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=sr_1_fkmr0_1?s=books&amp;ie=UTF8&amp;qid=1502576961&amp;sr=1-1-fkmr0&amp;keywords=Deep+learning+by+Yousha+Bengio
I tried to put a pakistani address to check if amazon india ships there, http://imgur.com/a/OIhIL but it doesn't.
Sorry bro, I wish there was something I could do.
If you just try to eyeball patterns from historical charts, I guarantee you will see it because that's just what the brain has evolved to do: spotting patterns well (e.g. Jesus on a toast), even when it's actually due to random chance. That's also why most of the so-called technical 'analysis' are bullshit.
Instead, approach this in a systematic and principled manner. You can try check out this book to get an idea what I'm talking about: Pattern Recognition and Machine Learning. This is the standard grad-level introduction to the field, but might be rather heavy for some. An easier read is this one. You can find the PDF of these books online through some searching or just head to your local library. Approaching the problem from a probabilistic and statistical angle also lets you know the extent of what you can predict and more importantly, what the limitations are and when the approach breaks down -- which happens a lot actually.
TL;DR: predicting patterns is hard. That's why stats is the sexy new job of the century, alongside with 'data science' (hate that term uuurgh).
Bishop's book Pattern Recognition and Machine Learning is pretty great IMHO, and is considered to be the Bible in ML - although, apparently, it is in competition with Murphy's book Machine Learning: A Probabilistic Approach. Murphy's book is also supposed to be a gentler intro. With an ECE background the math shouldn't be too difficult to get into in either of these books. Depending on your background (i.e. if you've done a bunch of information theory) you might also like MacKay's book Information Theory, Inference and Learning Algorithms. MacKay's book has a free digital version and MacKay's 16 part lecture series based on the books is also available online.
While those books are great, I wouldn't actually recommend just reading through them, but rather using them as references when trying to understand something in particular. I think you're better off watching some lectures to get your toes wet before jumping in the deep end with the books. MacKay's lectures (liked with the book) are great. As are Andrew Ng's that @CatZach mentioned. As @CatZach mentioned Deep Learning has had a big impact on CV so if you find that you need to go that route then you might also want to do Ng's DL course, though unlike the courses this one isn't free :(.
Finally, all of the above recommendations (with the exception of Ng's ML course) are pretty theory driven, so if you are more of a practical person, you might like Fast.AI's free deep learning courses which have very little theory but still manage to give a pretty good intuition for why and how things work! You probably don't need to bother with part 2 since it is more advanced stuff (and will be updated soon anyways so I would try wait for that if you do want to do it :))
Good luck! I am also happy to help with more specific questions!
take a look at Pattern Recognition an Machine Learning by Bishop,
http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
it's an excellent text, though not for the faint of heart. just the first chapter should provide you with a great answer to your question.
Well I'd recommend:
For a more basic stats refresh before you dive in, pretty much any introductory textbook will be sufficient. For a very basic but quick and dirty refresh on basic stats you can get: Statistics in Plain English
lawl, that's a fun one.
>You had no choice.
Some fun with semantics: This isn't going to fit into words right, so you're going to have to explore it to understand it, but you do have a choice, but control isn't quite what it seems to be. You obviously get the bit about control, but calling it a choice is misleading. I made the same mistake for a while, until I tried explaining it to people and realized the misunderstanding:
A choice is when there are multiple options, and you pick the best option. You're still picking that option, despite the delusion of control. Even if there is no you, and control is made up, there is still a choice.. a decision, a process. It just isn't real; choice is formless, it is language, it is psychological.
Have you explored consciousness yet? If you're the type that likes to nerd out and go beyond simple teachings checkout I Am a Strange Loop and it's more advanced cousin Gödel, Escher, Bach: An Eternal Golden Braid.
If I understand you correctly, the principle you've stumbled upon was mathematically proven by Kurt Goedel in 1931. I think you would enjoy this book a lot. It won a Pulitzer prize.
If you don't understand why you are getting downvoted, it is due to the difference between the centre for thought and the origin of consciousness. If you don't think there's a difference, go educate yourself; there are many resources. You might try Gödel, Escher, Bach, for starters.
For the curious
You need maths through Discrete to read TAOCP.
http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567/ref=sr_1_1?ie=UTF8&amp;qid=1292317218&amp;sr=8-1
Give this a look.
If that question interests you you'd probably enjoy Godel, Escher, Bach
Charles Petzold's annotated version of this paper http://www.amazon.com/Annotated-Turing-Through-Historic-Computability/dp/0470229055/ref=sr_1_1?ie=UTF8&amp;s=books&amp;qid=1280607006&amp;sr=8-1 is definitely worth reading, in my opinion.
Hmm alright, considering your background, I'd probably recommend you giving Michael Sipser's Introduction to Theory of Computation a read (I sure there are many electronic copies floating around on the Internet). I think they cover the prerequisite math concepts required in a preliminary chapter before the content which I highly recommend you spend some time on. It works it's way up by walking you through notions of computations in increments, first through finite state automata before adding in more features, working its way up to a Turing machine. You can skip most of the exercises, since those are mostly for graduate students who need practice before undertaking research. If you ever get confused about concepts along the way just drop me a PM or a question in /r/askcomputerscience and I'm sure the community would be happy to help out.
Also if you're interested I could mail you my copy of (meaning a copy that I had bought some time ago, not that I wrote it) the Annotated Turing. It does a great job of explaining the concept of a Turing machine provided a non-mathematical and non-CS background. I'd be more than happy to share my books with people who are interested, plus there's no use in me keeping it around now that I'm done with it.
Just bear in mind that unlike most of science, the concepts here are very abstract, there aren't many direct physical implications, this really is a pure study of notions at play. i.e. how does one go about studying "how to do things" and its implications. A lot of details such as "how can such a machine exist with an infinite tape? what moves it? how does it implement its decision making scheme?" are all unimportant and ultimately inconsequential to the study itself.
Instead, what we care about are things like "I have a problem, is it possible for me to come up with a solution (algorithm) for it? Or is it logically impossible?" or things like "I have come up with a way to make a "computer", can it do things that other computers can? If I had to make it sort an arbitrary set of numbers so that they are ordered numerically, can my computer do it?". Turing machines, are a tool to help us reason about formally around these sort of arguments, and to give insight into what we can qualify as "computation". Further down the line we even ask questions like "are some problems inherently more 'difficult' than others?" and "if I can solve problem B, and I somehow use the solution for problem B to solve some other problem A?"
Perhaps this all sounds perplexing now, but maybe just go through some content and spend time reading a little and these should start to make a little more sense. good luck with your future endeavors on this journey!
That is an incredibly broad question. Without knowing what you've already studied, it's hard to recommend things. Most of the aerospace and mechanical engineers I know use pre-packaged programs rather than writing their own scripts, etc.
Artificial intelligence might be the best one, though. Russel and Norvig is the standard textbook: https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597
The plus side to learning about AI is that it is not really programming intensive - it's logic and statistics intensive.
If you want to go the programming route, it gets a little hairier. The reason is that advanced systems designs will take a lot of initial classes just to get you to a level where you are comfortable programming and can then think about design and program flow.
Take an intro course. I learned programming with C / C++ and Matlab. Recommend those since it's easier to blow your foot off when programming. Once you understand how to design programs, what functions are, how program control can be passed off, move over into Python (much easier to pick up and run with and much better supported).
You might also benefit from a databases or Big Data class due to the amount of data generated from an aircraft.
Regular expressions and scripting is another option. But that's good for anyone.
Russell Norvig
Does someone have a copy of the leaked self driving car code and post it on github?
Heck, even a reasonable implementation of Thrun's Simultaneous localization and mapping algorithm and embedded A star all wrapped in the AI code would be nice.
https://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping
He talks about it in Chapter 25 section 3 of: https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/ref=sr_1_1?s=books&amp;ie=UTF8&amp;qid=1487948083&amp;sr=1-1&amp;keywords=ai+a+modern+approach
He describes it in: https://www.udacity.com/course/artificial-intelligence-for-robotics--cs373
But he only describes how you would implement it, he doesn't hand out the finished code.
Gimme.
If you're interested in this stuff (and just getting started), then I highly recommend this book - http://www.amazon.com/Artificial-Intelligence-Modern-Approach-Edition/dp/0136042597
When you're ready to go deeper, then this one is even better http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/0070428077/ref=sr_1_2?s=books&amp;ie=UTF8&amp;qid=1341852604&amp;sr=1-2&amp;keywords=machine+learning
That second book is a little older, but all of its algorithms/techniques are still relevant today.
I would repeat jbu311's point that your interests are way too broad. If you're interested in going into depth in anything, you'll have to pick a topic. Even the ones you mentioned here are fairly broad (and I'm not sure what you meant about concurrency and parallelization "underscoring" AI?).
If you want to learn about the field of natural language processing, which is a subfield of AI, I would suggest Jurafsky and Martin's new book. If you're interested more broadly in AI and can't pick a topic, you might want to check out Russell & Norvig (although you might also want to wait a few months for the third edition).
It is hard to answer this question without knowing your background. If you are really interested, I suggest you read this book (especially part three) on Artificial Intelligence so you can understand how automated theorem proving relates to AI. In my opinion, automated theorem proving is not a particularly interesting problem in modern artificial intelligence, since representing real-world problems using symbolic logic is almost always impractical.
However, the problem is still interesting for computer assisted theorem proving, and boolean satisfiability is a very important problem in the theory of computation.
Stuart Russell, the man who literally wrote the book on AI, is concerned.
Plenty of prestigious people on the cutting edge of the research in the field are concerned.
Just because you've only heard the household-name-level famous people talk about it, it doesn't mean that the genuine, in-the-thick-of-it experts aren't concerned either.
As for the 10~20 years figure, you're right that it is unlikely that AI will be made in that timeframe. However, the claim was merely that it is possible to create with enough resources in that timeframe, which I think is reasonable. Since you care about what the experts think, here is a summary of the best information we have about when they think this will happen.
>Median estimates for when there will be a 10% chance of human-level AI are all in the 2020s (from seven surveys).
>Median estimates for when there will be a 50% chance of human-level AI range between 2035 and 2050 (from seven surveys)
___
AI: A Modern Approach is the best textbook on AI by far
A little late to the party, but...
Runestone: Arena 2
I spent most of the week working on music and sound, but managed to also work on UI and spells.
New screenshots:
Next week I'm hoping to rework the battle AI to use Goal-Oriented Action Planning. I'll use a graph search algorithm to plan for actions that result in a desirable world state with the shortest way to get there. Seems like it might be too CPU intensive for Flash, but we'll see when we get there. Also started reading Artificial Intelligence: A Modern Approach - seems very interesting, but I doubt it'll be much help here.
This one was a pretty good read.
The Singularity Is Near
The Singularity is Near by Ray Kurzweil
Here's one that I just ordered. It comes with a recommendation from Elon Musk as well. This is a subject Kurzweil discusses at length in his books, though his approach is far more optimistic. He avoids the cataclysm by saying humans and AGI will work together as one, but his point basically concedes humanity's destruction if we don't cooperate/merge.
If you actually want to know the answer, why ask loaded questions about instead of researching it? Say read a book : http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111/ref=sr_1_1?ie=UTF8&amp;qid=1422360580&amp;sr=8-1&amp;keywords=superintelligence
Are you referring to Machine Learning: A Probabilistic Perspective? (link to Amazon)
Common Lisp remains a touchstone. I highly recommend installing Clozure Common Lisp and Quicklisp and then working through Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp and Artificial Intelligence: A Modern Approach with them. Although I'm now firmly in the statically-typed functional programming world, this has been part of my journey, and it will change how you think about programming.
Here's the course webpage for an intro AI course from a good professor on the topic
Good overall book on the topic (Russel & Norvig - AI: A Modern Approach)
I think they use this:
https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597
Decent book (the bible for AI apparently).
If you have a rudimentary understanding of algorithms, I would suggest Artificial Intelligence: A Modern Approach, by Stuart Russel and Peter Norvig. The book is comprehensive, well-written, and covers a wide area of different techniques and approaches within AI. Be aware that the book is written as a textbook, so do not expect philosophy or speculation inside - only what is possible and feasible given current state-of-the-art.
That was a phenomenal article. Extremely long (just like every piece of writing associated with Hofstadter), but excellent nonetheless. I'm admittedly sympathetic to Hofstadter's ideas, not the least of which because of my combined math/cognitive science background.
There was a quote by Stuart Russell, who helped write the book on modern AI, that really stood out to me, and I think expresses a lot of my own issue with the current state of AI:
“A lot of the stuff going on is not very ambitious... In machine learning, one of the big steps that happened in the mid-’80s was to say, ‘Look, here’s some real data—can I get my program to predict accurately on parts of the data that I haven’t yet provided to it?’ What you see now in machine learning is that people see that as the only task.”
This is one of the reasons I've started becoming very interested in ontology engineering. The hyperspecialization of today's AI algorithms is what makes them so powerful, but it's also the biggest hindrance to making larger, more generalizable AI systems. What the field is going to need to get past its current "expert systems" phase is a more robust language through which to represent and share the information encoded in our countless disparate AI systems. \end rant
piggybacking on what /u/T4IR-PR said, the best book to attack the science aspect of AI is Artifical Intelligence: A Modern Approach. It was the standard AI textbook when I took the class and it's honestly written very well - people with a basic undergraduate understanding of cs/math can jump right in and start playing with the ideas it presents, and it gives you a really nice outline of some of the big ideas in AI historically. It's one of the few CS textbooks that I recommend people buy the physical copy of.
Note that a lot of the field of AI has been moving more towards ML, so if you're really interested I would look into books regarding that. I don't know what intro texts you would want to use, but I personally have copies of the following texts that I would recommend
and to go w/ that
for some more maths background, if you're a stats/info theory junky.
After all that, if you're more interested in a philosophy/theoretical take on AI then I think Superintelligence is good (I've heard?)
I can recommend this (free) course:
https://www.udacity.com/course/intro-to-artificial-intelligence--cs271
You certainly dont need a degree (it helps of course) but most you need is dedication and perserverance.
In regards to math you need a good (more than)-basic understanding of statistics, linear algebra, algorithms and you also need to develop good data analysis skills.
If you want to get serious with AI this book is fantastic (atleast it helped(still does) me alot): https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/ref=sr_1_2?ie=UTF8&amp;qid=1506722436&amp;sr=8-2&amp;keywords=artificial+intelligence+a+modern
and by the way check out this thread maybe:
https://www.reddit.com/r/artificial/comments/6cnlr6/monthly_how_to_get_started_with_ai_thread/
I learned mostly through reading textbooks in University, but not many of the books we were assigned stood out as being particularly great. Here's a few that I did enjoy:
To balance things out, I'll also give a list of books I thought were awful:
http://aima.cs.berkeley.edu/
http://www.amazon.com/gp/product/0136042597/ref=olp_product_details?ie=UTF8&amp;me=&amp;seller=
AI is more about computer science concepts as opposed to just plain programming languages. First learn how to use a few programming languages (so you feel comfortable with software ideas), and then take a crack at a book like Artificial Intelligence: A Modern Approach (I've linked the 2nd edition there as that is the one I've read, but apparently there is a 3rd edition out). This will introduce you to concepts from the field of AI, and from there you can start reading journal articles and doing your own experiments.
I like the book that was used to prop up the monitor.
Lets do some math.
My son was born this year. With an average life span of 76 years, he should most likely die by 2090. But, I will also make the assumption that in the years between 2014 and 2090 we will find ways to advance the average life span to a little bit longer, lets say 30 years. So now, his average life span is 106 years and the "death year' is extended to 2120.
But between 2090 and 2020 science will continue to advance and we will probably have a life expectance of 136 years by then, which now make his death year "2050". And so forth until science finds a way to keep him alive for ever. Even if it takes the better part of a century, some of the younger people will still be beyond the cutoff.
Now. If you actually talk to real scientists who have studied this in much more detail, they are saying that this will not take a century, and should just take a few decades to achieve " escape velocity" for immortality. There is a book written about this, and how in the next few decades, we will unlock the masteries of aging. The Singularity Is Near: When Humans Transcend Biology
There are two books I recommend to everyone who is frustrated and/or saddened by the state of the world and has lost hope for a better future.
The first is The Better Angels of Our Nature by Stephen Pinker. It lays out how violence in human societies has been decreasing for centuries and is still declining.
Despite the prevalence of war and crime in our media, human beings are less likely to suffer violence today than at any point in our prior history. The west suffered an upswing in social violence from the 1970s -1990s, which has since been linked to lead levels, but violence in the west has been declining since the early 90s.
Put simply the world is a better place than most media coverage would have you believe and it's getting better year by year.
The second book I recomend is The Singularity is Near by Ray Kurzweil. It explains how technology has been improving at an accelerating rate.
Technological advances have already had major positive impacts on society, and those effects will become increasingly powerful over the next few decades. Artificial intelligence is already revolutionizing our economy. The average human life span is increasing every year. Advances in medicine are offering hope for previously untreatable diseases.
Basically, there is a lot of good tech coming which will significantly improve our quality of life, if we can just hang on long enough.
Between those two forces, decreasing violence and rapidly advancing technology, the future looks pretty bright for humanity. We just don't hear that message often, because doom-saying gets better ratings.
I don't know what disability you're struggling with but most people have some marketable skills, i.e. they aren't "worthless". Based on your post, you clearly have good writing/communicating skills. That's a rare and valuable trait. You could look into a career leveraging those skills (e.g. as a technical writer or transcriptionist) which your disability wouldn't interfere with to badly (or which an employer would be willing to accommodate).
As for being powerless to change the world, many people feel that way because most of us are fairly powerless on an individual level. We are all in the grip of powerful forces (social, political, historical, environmental, etc.) which exert far more influence over our lives than our own desires and dreams.
The books I recommended post convincing arguments that those forces have us on a positive trend line, so a little optimism is not unreasonable. We may just be dust on the wind, but the wind is blowing in the right direction. That means the best move may simply be to relax and enjoy the ride as best we can.
Any futurist or regular reader of /r/futurology can rehearse all of the arguments for why uploading is likely to be feasible by 2100, including the incremental replacement of biological neurons by artificial ones which avoids the "copy" issue. If you're not already familiar with these, the easiest single reference is probably The Singularity is Near.
Einstein: His Life and Universe
The Black Hole War
The Trouble With Physics
Parallel Worlds
The Red Limit: The Search for the Edge of the Universe
Warped Passages
The Singularity is Near
I found this book great for a solution that could replace our current economic and political systems:
http://www.amazon.com/Open-Source-Everything-Manifesto-Transparency-Truth/dp/1583944435/ref=sr_1_1?s=books&amp;ie=UTF8&amp;qid=1406124471&amp;sr=1-1&amp;keywords=steele+open+source
This book is great as well. It is, Ray Kurzweil, explaining how the human brainn function as he attempts to reverse engineer it for Google in order to create an AI.
http://www.amazon.com/How-Create-Mind-Thought-Revealed/dp/0143124048/ref=sr_1_1?s=books&amp;ie=UTF8&amp;qid=1406124597&amp;sr=1-1&amp;keywords=kurzweil
Read Superintelligence. Or check out Bostrom's TED talk.
>Really? It's still their opinion, there's no way to prove or disprove it. Trump has an opinion that global warming is faked but it doesn't mean it's true.
From my perspective, you have that analogy flipped. Even if we run with it, it's impossible to ignore the sudden dramatic rate of acceleration in AI capability and accuracy over just the past few years, just as it is with the climate. Even the CEO of Google was caught off-guard by the sudden acceleration within his own company. Scientists also claim that climate change is real and that it's an existential threat; should we ignore them though because they can't "prove" it? What "proof" can be provided for the future? You can't, so you predict based on the trends. And their trend lines have a lot of similarities.
>Also, even if it's a threat(i don't think so, but let's assume it is), how putting it in your brain will help? That's kind of ridiculous. Nowadays you can turn your PC off or even throw it away. You won't be able to do that once it's in your brain. Also, what if the chip decides to take control over your arms and legs one day? It's insane to say that AI is a threat but to plan to put it inside humans' brain. AI will change your perception input and you will be thinking you are living your life but in reality you will be sitting in a cell somewhere. Straight up some Matrix stuff. Don't want that.
The point is that, in a hypothetical world where AI becomes so intelligent and powerful that you are effectively an ant in comparison, both in intelligence and influence, a likely outcome is death just as it is for billions of ants that we step on or displace without knowing or caring; think of how many species we humans have made extinct. Or if an AI is harnessed by a single entity, those controlling it become god-like dictators because they can prevent the development of any further AIs and have unlimited resources to grow and impose. So the Neuralink "solution" is to 1) Enable ourselves to communicate with computer-like bandwidth and elevate ourselves to a level comparable to AI instead of being left in ant territory, and 2) make each person an independent AI on equal footing so that we aren't controlled by a single external force.
It sounds creepy in some ways to me too, but an existential threat sounds a lot worse. And there's a lot of potential for amazement as well. Just like with most technological leaps.
I don't know how much you've read on the trends and future of AI. I would recommend Nick Bostrom's book "Superintelligence: Paths, Dangers, Strategies", but it's quite lengthy and technical. For a shorter thought experiment, the Paperclip Maximizer scenario.
Even if the threat is exaggerated, I see no problem with creating this if it's voluntary.
Some of the things mentioned:
Read Superintelligence by Nick Bostrom , it answered pretty much all the questions I had about AI and learned a ton of new things from it. It’s not too heavy on the math but there is a lot of info packed into it, highly recommend it.
Ein Beispiel wäre, dass sie die Menschen als Hindernis in ihrem Plan ansieht. Es gibt da dieses Beispiel der Maschine, die dafür gebaut wird die Herstellung von Büroklammern zu optimieren. Diese Maschine könnte zum einen indirekt die Menschheit auslöschen indem sie die Erde mit Fabriken vollballert und für uns unbewohnbar macht. Zum anderen könnte sie es auch direkt anstreben, weil sie sich denkt: "Der Mensch nimmt Platz für Fabriken ein. Wenn er weg wäre, wäre mehr Platz für Fabriken." Oder aber sie folgt der folgenden Logik und hat etwas ganz anderes mit den Menschen vor, was wir - da sie schließlich superintelligent ist - uns nicht vorstellen geschweige denn begreifen können:
> The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else. - Eliezer Yudkowsky
Will sagen: Sie muss für das Auslöschen der Menschheit keine niederen Beweggründe haben. Sie erfüllt einfach ihren Auftrag. Ohne Rücksicht auf die Menschheit.
Zum Abschluss noch eine Buchempfehlung zu dem Thema: Superintelligence: Paths, Dangers, Strategies
If you mean strong AI, there are many pathways for how we could get there. 15 years is probably a bit shorter than most expert estimates for mind scanning or evolution based AI. This book, which discusses different methods, will be available in the states soon:
http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111/ref=sr_1_1?ie=UTF8&amp;qid=1406007274&amp;sr=8-1&amp;keywords=superintelligence
> We went from the horse and buggy to landing on the moon in 80 years
Past events are not necessarily good indicators of future events. In this case, faster computers are a mechanism for bringing about AI faster. How much faster we get in how much time will probably be the influencing factor in all this. There is quite a bit of uncertainty surrounding whether that will be post-silicon or not. We don't have post-silicon computing up and running yet.
The other factor may be incentive. Maybe specific purpose AI will meet all such demand for the next 20 years, and nobody will have any incentive to create strong AI. This is especially true given the risks of creating strong AI (both to the world and to the organization or individual who creates the AI).
>Given what Musk does with other technologies, he is by no means a luddite or a technophobe. He's seen something that's disturbing. Given the guys track record, it's probably worth investigating
I agree. There's also a point to be made that one of the recent books Musk mentioned he read in a few interviews (and was acknowledged by the author in, too) was this http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111
I started reading it a few nights ago. It's painting an interesting picture about the future of AI. I'm looking forward to finishing it to discuss further
Someone else beat me to it. Here you go.
Elon Musk's fear of AI was amplified by the nonfiction book he recommended called SuperIntelligence. It is written by an Oxford professor and it's scary http://www.amazon.com/gp/aw/d/0199678111/ref=mp_s_a_1_cc_1?qid=1414342119&amp;sr=1-1-catcorr&amp;pi=AC_SX110_SY165_QL70
Na verdade eu sou físico. Acho que é mais comum entre os físicos adotar uma perspectiva bayesiana do que entre os matemáticos ou mesmo os estatísticos. Talvez por causa da influência do Edwin T. Jayes, que era físico. Talvez por causa da conexão com teoria de informação e a tentadora conexão com termodinâmica e mecânica estatística.
O meu interesse pela perspectiva Bayesiana começou por conta do grupo de pesquisa onde fiz o doutorado. Meus orientador e meu co-orientador são fortemente bayesianos, e o irmão do meu orientador de doutorado é um pesquisador bastante conhecido das bases epistemológicas da teoria bayesiana (o físico uruguaio Ariel Caticha).
Tem vários livros bons sobre probabilidade bayesiana, depende muito do seu interesse.
O primeiro livro que eu li sobre o assunto foi justamente o do Jaynes - Probability Theory, the Logic of Science. Esse é um livro um pouco polêmico porque ele adota uma visão epistemológica bastante forte e argumenta de forma bastante agressiva a favor dela.
Uma visão um pouco alternativa, bastante conectada com teoria de informação e também fortemente epistemológica você pode encontrar no livro Lectures on Probability, Entropy and Statistical Physics do Ariel Caticha - (de graça aqui: https://arxiv.org/abs/0808.0012). Eu fui aluno de doutorado do irmão do Ariel, o Nestor Caticha. Ambos têm uma visão bastante fascinante de teoria de probabilidades e teoria da informação e das implicações delas para a física e a ciência em geral.
Esses livros são mais visões epistemológicas e teóricas, e bem menos úteis para aplicação. Se você se interessa por aplicação tem o famoso BDA3 - Bayesian Data Analysis, 3ª edição e também o Doing Bayesian Data Analysis do John Kruschke que tem exemplos em R.
Tem um livrinho bem introdutório também chamado Bayesian Methods for Hackers do Cam-Davidson Pylon (de graça aqui: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) que usa exemplos em python (pymc). É bem basicão para aprender aplicações de probabilidades bayesianas.
O livro All of Statistics do Larry Wasserman tem uma parte introdutória também de inferência bayesiana.
Se você em interesse por inteligência artificial um outro livro muito bacana é o do físico britânico (recentemente falecido) David Mackay - Information Theory, Inference, and Learning Algorithms (de graça aqui: http://www.inference.phy.cam.ac.uk/mackay/itila/). Esse livro foi meu primeiro contato com Aprendizado de Máquina e é bem bacana.
Outros livros bacanas de Aprendizado de Máquina que usam uma perspectiva bayesiana são Bayesian Reasoning and Machine Learning (David Barber) e o livro-texto que tem sido o mais usado para essa área que é o Machine Learning: a Probabilistic Perspective (Kevin Murphy).
Try to get through the first chapter of Bickel and Doksum. It is a great book on mathematical statistics. You need a solid foundation before you can build up.
For a less rigorous, more applied and broad book, I thought this book was alright. Just realize that the more "heavy math" (i.e., mathematical statistics and probability theory) you do, the better prepared you will be to face applied problems later. A lot of people want to jump right into the applications and the latest and greatest algorithms, but if you go this route, you will never be the one greatly improving such algorithms or coming up with the next one (and you might even run the risk of not fully understanding these tools and when they do not apply).
I head that the newer Machine Learning: A Probabilistic Perspective is equally good, and from the small amount I've read so far I'd agree.
A good lightweight introduction: Programming Collective Intelligence
If you'd like a single, but more difficult, reference that covers much of the breadth of machine learning: Machine Learning: A probabiliistic Perspective
>There have been some excellent trading opportunities with returns as high as 30% to your overall portfolio! Crypto is providing big returns that are uncommon in traditional markets.
I guess you have a good intention, Mr. Hustle, but I'd hate to see the kind shibes here being taken advantage of again. You should be more objective and also warn people that they can as easily lose that much of money when trading, especially when they don't know what they are doing initially.
And the effectiveness of technical 'analysis' is a highly debatable issue. I'd just leave this quote from Wikipedia:
> Technical analysis is widely used among traders and financial professionals and is very often used by active day traders, market makers and pit traders. In the 1960s and 1970s it was widely dismissed by academics. In a recent review, Irwin and Park[13] reported that 56 of 95 modern studies found that it produces positive results but noted that many of the positive results were rendered dubious by issues such as data snooping, so that the evidence in support of technical analysis was inconclusive; it is still considered by many academics to be pseudoscience.[14] Academics such as Eugene Fama say the evidence for technical analysis is sparse and is inconsistent with the weak form of the efficient-market hypothesis.[15][16] Users hold that even if technical analysis cannot predict the future, it helps to identify trading opportunities.[17]
...
> Whether technical analysis actually works is a matter of controversy. Methods vary greatly, and different technical analysts can sometimes make contradictory predictions from the same data. Many investors claim that they experience positive returns, but academic appraisals often find that it has little predictive power.[51] Of 95 modern studies, 56 concluded that technical analysis had positive results, although data-snooping bias and other problems make the analysis difficult.[13] Nonlinear prediction using neural networks occasionally produces statistically significant prediction results.[52] A Federal Reserve working paper[21] regarding support and resistance levels in short-term foreign exchange rates "offers strong evidence that the levels help to predict intraday trend interruptions," although the "predictive power" of those levels was "found to vary across the exchange rates and firms examined".
I'm not saying not to take coaching from DogeHustle, just that if people want to do it, be aware of its 'limitation' too and have fun doing it with your disposable money only. As an alternative, I strongly suggest shibes who want to try predicting the future based on pattern analysis to do it in a principled manner and learn math, stats and machine learning. It won't be easy, but it will have a wide application beyond trading (so-called data 'science' is the hot job nowadays). It will also teach you the limitation of such methods, and when it might fail, especially in such a manipulated market like crypto. This is a good book to start with:
http://www.amazon.co.uk/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020
Murphy
BRML
ESL
Zum "Rumspielen" für erste Erfahrungen bietet sich auch Keras sehr gut an, dass ist eine High-Level-API für Theano/TensorFlow, zu der es auch viele Git repos und tutorials gibt.
Darüberhinaus würde ich mittlerweile auch TensorFlow empfehlen. Ich bin von einem Einstieg mit C++/Caffe zu Python/Theano gekommen und nach kurzem Ausflug zu PyTorch seit mittlerweile fast 2 Jahren nur noch mit TensorFlow unterwegs. PyTorch gefiel mir persönlich einfach weniger als TensorFlow und Caffe/Theano usw. sind imho weniger mächtig als TensorFlow bei zeitgleich häufig größerer Komplexität.
So, genug der Google-Framework-Werbung ;)
Den Kurs von Udacity habe ich persönlich noch nicht belegt, kenne ihn aber und wurde mir bisher nur gutes berichtet. Ich persönlich finde Andrew Ng (Stanford) ist in der Lehre sehr fähig und nicht nur ein extremer Pionier auf dem Gebiet AI (arbeitet unter Anderem ja auch für Baidu, dem chinesischen Google). Seinen Kurs Deep Learning auf Coursera kann ich nur wärmstens empfehlen.
Abgesehen davon ist das Buch Deep Learning von Goodfellow & Bengio (beide ebenfalls Pioniere auf dem Gebiet AI) imho eines der besten Fachbücher in dem Bereich.
Ansonsten kannst du gerne weitere Fragen posten. Machine Learning und besonders neuronale Netze (deep learning) ist genau das, worauf ich mich seit Langem spezialisiere. Seit Längerem mache ich im Bereich AI übrigens kaum etwas anderes, weil dies alle anderen Ansätze verdrängt hat. Hinter so gut wie allem was man heutzutage von AI hört steckt machine learning und moderne, qualitative Ansätze (sehr populär etwa die Google Search, Google Translator) sind immer neuronale Netze, bzw. präziser deep learning.
Is this the Bishop book you guys are talking about?
He's talking about the distribution of the error of y not J(or the distribution of the probability of the function y given x). It's explained in the lecture notes, and in page 29(figure 1.16) of Bishop's book there's an illustration that switched on the bulb for me(althought I found the book almost incomprehensible). You can look it using the amazon preview http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_1?ie=UTF8&amp;qid=1318610381&amp;sr=8-1#reader_0387310738
Amazon links to books mentioned (no affiliate). Warning: A lot of high textbook prices here...look for eBooks and/or used copies of earlier versions:
Introduction to Mathematical Statistics (Hogg, McKean, & Craig)
All of Statistics (Wasserman)
Statistical Inference (Casella & Berger)
Pattern Recognition and Machine Learning (Bishop) (only reasonably priced as an eBook)
Hitchhiker's Guide to Python
http://www.deeplearningbook.org/
Also this.
I know nearly nothing about this topic, but I find it extremely interesting and I've done some searching.
Two more lectures:
Supposedly a nice overview:
Very readable papers:
A paper that I haven't tried reading yet:
A book chapter:
I would recommend Elements of Statistical Learning (the "ESL" book) for someone with your level of knowledge (they have an easier Intro book "ISL", but seems you could probably head straight for this):
http://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576/ref=sr_1_1?ie=UTF8&amp;qid=1463088042&amp;sr=8-1&amp;keywords=elements+of+statistical+learning
Machine learning isn't a cloud thing. You can do it on your own laptop, then work your way up to a desktop with a GPU, before needing to farm out your infrastructure.
If you're serious about machine learning, you're going to need to start by making sure your multivariate calculus and linear algebra is strong, as well as multivariate statistics (incl. Bayes' theorem). Machine learning is a graduate-level computer science topic, because it has these heady prerequisites.
Once you have these prereqs covered, you're ready to get started. Grab a book or online course (see links below) and learn about basic methods such as linear regression, decision trees, or K-nearest neighbor. And once you understand how it works, implement it in your favorite language. This is a great way to learn exactly what ML is about, how it works, how to tweak it to fit your use case.
There's plenty of data sets available online for free, grab one that interests you, and try to use it to make some predictions. In my class, we did the "Netflix Prize" challenge, using 100MM Netflix ratings of 20K different movies to try and predict what people like to watch. Was lots of fun coming up with an algorithm that wrote its own movie: it picked the stars, the genre and we even added on a Markov chain title generator.
Another way to learn is to grab a whitepaper on a machine learning method and implement it yourself, though that's probably best to do after you've covered all of the above.
Book: http://www-bcf.usc.edu/~gareth/ISL/
Coursera: https://www.coursera.org/learn/machine-learning
Note: this coursera is a bit light on statistical methods, you might want to beef up with a book like this one.
Hope this helps!
You're a savage, reading sheets of dead trees with ink squirted upon them...
http://www.amazon.com/The-Elements-Statistical-Learning-Prediction/dp/0387848576
Be careful about the editions as you need to make sure its the jan 2013 print to be up to date.
I liked Machine Learning For Hackers, Programming Collective Intelligence and The Elements of Statistical Learning.
Hands-on: Hands-On Machine Learning with Scikit-Learn and TensorFlow
Theory: The Elements of Statistical Learning
Gödel, Escher, Bach is a popular one. If you're looking for fiction, then I highly recommend Flatland.
The Eternal Golden Braid
For a collection of simple elements with enough resources and time, emergence of structure occur. Recursion, and the system of seemingly simple neurons can lead to consciousness.
I'm reminded of this book
Gödel, Escher, Bach: An Eternal Golden Braid
Also, read Gödel, Escher, Bach: An Eternal Golden Braid
To OP: You should read Godel, Escher, Bach: An Eternal Golden Braid by Douglas Hofstadter: http://www.amazon.com/Godel-Escher-Bach-Eternal-Golden/dp/0465026567
It's a GREAT book. They cover this mental exercise, as well as a ton of others that would likely enjoy.
Uncle Tungsten: Memories of a Chemical Boyhood by Oliver Sacks. Sacks is best known for writing case studies of his patients as a neurologist, such as The Man Who Mistook His Wife For a Hat. Uncle Tungsten is part memoir, part history of and introduction to chemistry. There's nothing quite like it out there.
The Selfish Gene by Richard Dawkins. Dawkins's Twitter antics notwithstanding, this book is an unmissable classic in biology.
Godel, Escher, Bach by Douglas Hofstadter. An ode to consciousness, full of puns, music and metamathematics.
Mind, Body, World by Michael Dawson. This is a textbook, but it's (legally!) available for free online, and it's totally engrossing. The author uses his work in music cognition to introduce the major theories and paradigms of cognitive science and show how there isn't as much separation between them as it seems.
The book that kindled my interest in Artificial Intelligence and started my journey toward getting a PhD in Computer Science was Gödel, Escher, Bach: An Eternal Golden Braid. It's not current with respect to the state of the art, but it is a compelling, high-level tour through some of the biq questions in Artificial Intelligence, Cognitive Science, and Computer Science in general.
Have you read Gödel, Escher, Bach? There are some great analogies between math, music, art, and other really neat stuff.
https://www.amazon.com/Gödel-Escher-Bach-Eternal-Golden/dp/0465026567
If life can grow out of the formal chemical substrate of the cell, if consciousness can emerge out of a formal system of firing neurons, then so too will computers attain human intelligence.
GEB-EGB
I'm assuming you're looking for things geared toward a layman audience, and not textbooks. Here's a few of my personal favorites:
Sagan
Cosmos: You probably know what this is. If not, it is at once a history of science, an overview of the major paradigms of scientific investigation (with some considerable detail), and a discussion of the role of science in the development of human society and the role of humanity in the larger cosmos.
Pale Blue Dot: Similar themes, but with a more specifically astronomical focus.
Dawkins
The Greatest Show on Earth: Dawkins steers (mostly) clear of religious talk here, and sticks to what he really does best: lays out the ideas behind evolution in a manner that is easily digestible, but also highly detailed with a plethora of real-world evidence, and convincing to anyone with even a modicum of willingness to listen.
Hofstadter
Godel, Escher, Bach: An Eternal Golden Braid: It seems like I find myself recommending this book at least once a month, but it really does deserve it. It not only lays out an excruciatingly complex argument (Godel's Incompleteness Theorem) in as accessible a way as can be imagined, and explores its consequences in mathematics, computer science, and neuroscience, but is also probably the most entertainingly and clearly written work of non-fiction I've ever encountered.
Feynman
The Feynman Lectures on Physics: It's everything. Probably the most detailed discussion of physics concepts that you'll find on this list.
Burke
Connections: Not exactly what you were asking for, but I love it, so you might too. James Burke traces the history of a dozen or so modern inventions, from ancient times all the way up to the present. Focuses on the unpredictability of technological advancement, and how new developments in one area often unlock advancements in a seemingly separate discipline. There is also a documentary series that goes along with it, which I'd probably recommend over the book. James Burke is a tremendously charismatic narrator and it's one of the best few documentary series I've ever watched. It's available semi-officially on Youtube.
Economics:
Sciences:
I'd recommend having a read of Godel, Escher, Bach: An Eternal Golden Braid. The book that taught me that consciousness happens somewhere between hormone propogation and the physiological manifestations of our biochemical state.
That and it is just plain good reading.
If you want a quick non-textbook to get your feet wet, Oreilly's Programming Collective Intelligence isn't half bad.
Seconding collaborative filtering. It's also a fairly simple algorithm to implement yourself as long as you're not using Wikipedia as a guide.
Collaborative filtering is like what Amazon uses to figure out what products to recommend to its users. It finds users that have similar purchasing habits to yourself and recommends items that they bought.
The first chapters of Programming Collective Intelligence describe how to implement Collaborative Filtering in Python in a really intuitive way, along with providing source code. Two hours in and you'll have a working service recommendation system. I'd definitely recommend that book to anyone looking to build what OP is interested in making.
In addition to BeautifulSoup there's also Scrapy if you want to do some crawling and screen scraping. http://doc.scrapy.org/en/latest/intro/overview.html
You might consider this book for a starter into data mining and machine learning. It uses Python for the code samples.
http://www.amazon.com/Programming-Collective-Intelligence-Building-Applications/dp/0596529325
I'm an engineer, and did my Masters in voice compression. But I read Jeff Hawkin's On Intelligence a while back, and it was quite eye-opening (and a good read) about how the neo-cortex works and what its role is. I look at everything mind-related through this lens now.
"processing power as human brains" is more difficult to quantify than one would think. The human brain is not some massively parallel simulator. The way the brain structures data, generates models, and the prediction/response loop require not simply raw processing power, but a different architecture.
Read Jeff Hawkins' On Intelligence
First 6 weeks of Andrew Ng's [basic ML course] (https://www.coursera.org/learn/machine-learning), while reading Intro to Statistical Learning, for starters (no need to implement exercises in R, but it is a phenomenal book).
From there you have choices (like taking the next 6 weeks of Ng's basic ML), but for Deep Learning Andrew Ng's [specialization] (https://www.coursera.org/specializations/deep-learning) is a great next step (to learn CNNs and RNNs). (First 3 out of 5 courses will repeat some stuff from basic ML course, you can just skip thru them).
To get into the math and research get the Deep Learning book.
For Reinforcement Learning (I recommend learning some DL first), go through this [lecture series] by David Silver (https://www.youtube.com/watch?v=2pWv7GOvuf0) for starters. The course draws heavily from this book by Sutton and Barto.
At any point you can try to read papers that interest you.
I recommend the shallower, (relatively) easier online courses and ISLR because even if you are very good at math, IMO you should quickly learn about various topics in ML, DL, RL, so you can hone in on the subfields you want to focus on first. Feel free to go through the courses as quickly as you want.
The best to start with Theoretical understanding would be: The Elements of Statistical Learning: Data Mining, Inference, and Prediction
If you prefer to understand along with computation implementation, go with this: An Introduction to Statistical Learning: with Applications in R
I would highly recommend to read the following book
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=mp_s_a_1_2?keywords=machine+learning&amp;qid=1566810016&amp;s=gateway&amp;sprefix=machi&amp;sr=8-2
I think it is the best one about ml/dl. Not sure that they already updated the tensorflow examples to tf 2.0 and keras.
And as tensorflow includes keras now, and has perfect pipeline for deploying your model, i think it is the perfect choice. :)
Olha, não posso falar muito em termos de qualidade dos cursos. O grande lance é o nome que você vai botar no currículo: Estácio é praticamente sinônimo de massificação do ensino superior, o que não gera um destaque para quem te avalia posteriormente. Em compensação, eu não conheço a PUC-MG, mas já ouvi falar na PUC-SP, PUC-RJ e na PUC-RS, que em geral são boas escolas - e provavelmente pensaria o mesmo da PUC-MG. Isso agregaria mais valor para ti.
Sobre o currículo em si:
Se eu tivesse que escolher, iria de PUC. Mas quer aprender de verdade? Faça o curso de Machine Learning do Andrew Ng no Coursera e pegue o certificado, vale muito mais do que isso e tem uma hands-on approach. O Andrew é professor de Stanford e tem uma puta didática.
E se quiser aprender por aprender, compre esse livro: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_1?crid=35OQXMZA5U4H6 Nova edição sai dia 5 desse mês.
My previous video on AI did go into a lot more depth on neural networks but still wasn't enough to build your own. (There's only 2 vids on my channel so far so it's just the other one). I will defiantly consider making a video for my fellow devs on AI but for now you should check out https://www.youtube.com/watch?v=32wtJZ3yRfw&list=PLX2vGYjWbI0R08eWQkO7nQkGiicHAX7IX if you want to learn how to implent AI into a Unity3D project. If Unity3D isn't for you then you can read:
https://www.amazon.co.uk/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_3?keywords=machine+learning+hands+on&qid=1563498470&s=gateway&sr=8-3
Also thanks for the support!
Hi,
Not trying to sell anything here, but I've been reading this book : https://www.amazon.ca/Aur%C3%A9lien-G%C3%A9ron/dp/1491962291/ref=mp_s_a_1_1?keywords=machine+learning&amp;qid=1568912246&amp;sprefix=machibe+l&amp;sr=8-1
I think it is a pretty complete book that covers a lot and a good first step into ML.
Plus there are plenty of examples using Python.
GL
This book is a pretty good reference on scikit-learn with very good walkthroughs and code samples:
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=pd_lpo_sbs_14_img_0?_encoding=UTF8&amp;psc=1&amp;refRID=ERARBRW89Y6S1ATN8H5C
Not really. We're talking about a machine with an intelligence that makes us look like ants, more capable of us at everything we do, including manipulation of human beings.
The only book I'm aware of would be this modern classic - https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742
You may find r/controlproblem helpful.
If you find any other books, I'd love to hear about them.
Superintelligence: Paths, Dangers, and Strategies. The book lays our exactly how (potentially) screwed we are as a species if AI development is not careful. And ways to control a potentially species-endingly-powerful AI.
I've read a lot on what people have said about AI risk, and so far there have been a few people who have indicated that they have a good understanding of the argument being made, and have proposed counterarguments that display their understanding. There's Russ Roberts who argues that even a superintelligence can't actually go that far in being able to manipulate the world (a reasonably compelling argument IMO, but make sure that when you're thinking "superintelligence" you're actually visualizing something of the proper scale). There's Ben Goertzel who says... quite a lot of things, actually, though what stuck to me the most was that the reward-maximizing view of AI that Nick Bostrom and Eliezer Yudkowsky and others use (and that makes the orthogonality thesis seem so compelling) looks very little like practical AI development as it is done now or expected to ever be done, making it rather suspicious even as an abstract model. There's Robin Hanson who had a lengthy debate with Yudkowsky, but the core of his argument seems to be that there's little evidence that the kind of growth rate that would make an AGI dangerous is actually achievable.
tl;dr: There's a lot of people who understand the AI risk argument and have compelling counterarguments to it. These three are just the ones that have impressed me the most so far.
But you? Well, I'm sorry, I would like to be charitable, but you said it yourself: "But what about TEEEEEEEEEEEEERMINATOR?". You have not noticed that an argument different from what you expect to hear has been made. I'd tell you to go pick up Bostrom's Superintelligence: Paths, Dangers, Strategies or Yudkowsky's Rationality: From AI to Zombies but, well, I've never actually read either of these books, so it would be a bit of an odd recommendation to make (I read the LW sequences when they came out and have never heard anyone mention anything essential in those books that wasn't in the sequences). Oh well. FWIW Goertzel says that Yudkowsky's book is the one that makes the essence of the point clear and doesn't try to weasel out of counterarguments.
(For those who have never heard of any of the names in this post, http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html is a fairly popular comparatively short introduction to the basic idea being talked about. Although, well, I haven't read that one, either. No, seriously, on that side you get the same argument from every source, there's not much point in reading the other people saying the same thing after you've read Yudkowsky.)
If you like books, I've been reading this one lately Programming Game AI by Example:
http://www.amazon.com/Programming-Game-Example-Mat-Buckland/dp/1556220782
I'm really enjoying it and it has a tonne of useful information. Read some of the pages on the Amazon site and see if it sounds interesting for you.
Yeah... that's not how you want to do it if you want actual functional AI that behave independently.
Look up Autonomous Agents - Steering Behavior, online.
Here is something to get you started
http://natureofcode.com/book/chapter-6-autonomous-agents/
You would want the ""Follow" and "Arrival" steering behaviors specifically. The arrival behavior is what you described as the AI "slowing down".
If you can get your hands on Programming AI by Example from the library, school, or otherwise it's phenomanal resource that teaches how to program Steering behaviors for beginning AI developers.
Unity is the bee's knees.
I've been messing with it casually for several years, and got serious in the last 2-ish years. I like it because I get to use C#, and that's the language I know best. Only problem in it's using some weird limbo version of .NET 2, that's not actually 2.0 but is also 3.0 is some places? I think it's because it's using Mono 2.0, which is some subset of .NET. It's weird. They're moving to 4.5 soon anyways so I'm hype for that. I'ts been a lot of fun regardless, I get to apply a different knowledge and tool set from my day job. Not to mention it feels great when you actually get something to build and actually work.
So anyways here's a list of resources I've found over the years to be super helpful:
Things on Reddit
Books
Videos
Other Things
That's all I can think of right now. I know it's a lot, but hopefully you can take away at least one of these resources.
Happy dev-ing!
Do the enemies ever hit the player? Perhaps you can try scaling the player's health to how often enemies land attacks. (For instance, instead of Zelda's 6+ half-heart system, use old school Mario's 'one hit you die unless powered up' system. Or Super Meat Boy's one-hit KO.)
>the player could just roll out of the way and beat the shit out of them while the enemy's attack was missing.
Sounds like fun to me! But I take it you're going for a different feel?
I'm currently reading Programming Game AI by Example. It goes from basic AI moving to pathfinding to goal hierarchies. The majority of the book might be a little too introductory for you though, at least until you get to the goals section.
Well if you're serious about taking a run at it, here are the resources I've used to teach myself. I'm self-educated, no degree myself and have been working with ML for the last year at one of those large tech companies I listed, so you can definitely do it. Build a small portfolio or contribute to open-source projects and start applying, it's that simple. Best of luck!
Deep Lizard playlists (beginner through reinforcement learning): https://www.youtube.com/channel/UC4UJ26WkceqONNF5S26OiVw/playlists
MIT Artificial Intelligence Podcast: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4
Don't know Python? Go here (they also have a Python2 class): https://www.codecademy.com/learn/learn-python-3
Deep Learning with Python (written by the guy who wrote the Keras API, which is the main model building API you should learn first, it works on top of tensorflow/theano/etc.): https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/ref=sr_1_3?keywords=deep+learning+with+python&qid=1566500320&s=gateway&sr=8-3
Learn to use Google CoLab when possible, it gives you a free K80 GPU and when you exceed the limits of that you can use a local runtime and use your own hardware. This article has excellent tips and tricks for CoLab: https://medium.com/@oribarel/getting-the-most-out-of-your-google-colab-2b0585f82403
The Math: when if/you're wanting to dive into the nitty-gritty math details the "godfather of modern ML" as they say is Geoffrey Hinton who played a huge role in the invention of deep learning and the mathematical trick that makes it work, back-propagation. You'll need to brush up on differential calculus, summation notation and statistics, but this will help you build your own architectures should you want/need to: https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9
Fellow NLP'er here! Some of my favorites so far:
I too love fun math[s] books! Here are some of my favorites.
The Number Devil: http://www.amazon.com/dp/0805062998
The Mathematical Magpie: http://www.amazon.com/dp/038794950X
I echo the GEB recommendation. http://www.amazon.com/dp/0465026567
The Magic of Math: http://www.amazon.com/dp/0465054722
Great Feuds in Mathematics: http://www.amazon.com/dp/B00DNL19JO
One Equals Zero (Paradoxes, Fallacies, Surprises): http://www.amazon.com/dp/1559533099
Genius at Play - Biography of J.H. Conway: http://www.amazon.com/dp/1620405938
Math Girls (any from this series are fun) http://www.amazon.com/dp/0983951306
Mathematical Amazements and Surprises: http://www.amazon.com/dp/1591027233
A Strange Wilderness: The Lives of the Great Mathematicians: http://www.amazon.com/dp/1402785844
Magnificent Mistakes in Mathematics: http://www.amazon.com/dp/1616147474
Enjoy!
Gödel, Escher, Bach: An Eternal Golden Braid is a fantastic read.
Hofstadter discusses Gödel's incompleteness theorems, computability, AI, music, and art, and generally about how complexity arises out of self reference.
The most INTP book I know is Godel Escher & Bach, though the second half is harder to get through. The first half more than makes up for it.
Other favs include Frank Herbert’s Dune series and his lesser known semi-stand alone book The Dosadai Experiment. That said, The Golden Age Trilogy by John C Wright is jam packed with sci-fi ideas. If you rather read modern fantasy, look at Night Watch series by Sergei Lukyanenko. Meanwhile as a history buff I also like the Temeraire series by Naomi Novik, where European powers in the Napoleonic era have a draconic Air Force.
Right now I am redoing and continuing the Thursday Next series byJasper Fforde, which combines alt history, sci-fi, British humor, and meta level shenanigans. A must for classic lit fans who don’t mind its wackiness. (Others can’t stand it) I also recently finished a reread of Roadside Picnic, which Stalker movie and game were loosely based on.
Edit: one other. If you want a “Lovecraftian Simulator”, get House of Leaves ASAP. It is less of a book to sit down and read and more of an interactive experience.
Jaap Schroder wrote a book detailing his study of the Solo violin works, and he's recorded the concertos as well. That's a good place to begin. There are some really brilliant insights that most students would never consider.
Don't get caught up thinking you are handcuffed and can only imitate an anemic baroque style or a warbly, romantic style. This video is one sort of hybrid, where the soloist and conductor are very aware of performance practice, but modern instruments and techniques are relied upon heavily. Remember that no recordings exits before 1900ish. There's still a lot of personal judgment in a good historically informed performance.
There are many great Bach interpretations, and you should listen to many recordings (Grumiaux is often held in high esteem, and Schroder, as good models) to find out where your preferences lie. You should attempt to play with all sorts of expressive devices (Non vib, lots of decay, faster bow, different bow strokes, bowing patterns, holding the bow higher, gut strings?, baroque bow) and find out what you have to say about Bach. I think any successful interpretation will at least have two major things: a tremendous sense of line (form, rhythm, a large-scale view) and an expressive use of the tone color (bright, warm, deep, thick, feathery, etc.).
Leopold Mozart also wrote a treatise on violin playing. In terms of playing style, he was more familiar with the Baroque than with the music of W.A mozart. He wrote about a sense of "affect" in Baroque music. He wrote that overall, there is one overriding feeling that should come across in Barque works (especially dances and binary form movements.) In the E major Bach, I bet it would be helpful to decide what the "affect" is for each movement. Is there only one, is the narrative single-minded? More simply, come up with something other than "happy" or "sad."
Don't let anyone tell you Bach was a stodgy, strict person. He was ridiculously smart, as shown by his ability to improvise multi-voice fugues. Hofstader wrote eloquently about Bach's puzzles and intellectualism. He was a jokester - the crab canon and the Coffee Cantata or good examples. He was sometimes compensated for his work with large amounts of beer. Bach had somewhere around 20 children, about half of which survived childhood. Bach was a very complex person, with lots of life experience. Don't let a careless caricature influence how you think about his music.
Douglas Hofstadter talks about something like this in I am a Strange Loop. Here's an interview that talks about it a bit. I recommend reading the book, though you may enjoy it more after reading Godel, Escher, Bach: An Eternal Golden Braid.
I am surprised this book has not been mentioned yet:
http://www.amazon.com/Gödel-Escher-Bach-Eternal-Golden/dp/0465026567/
Godel, Escher, Bach
by Douglas Hofstadter
Gödel, Escher, Bach: An Eternal Golden Braid
I still haven't finished it though, it's demanding and very rewarding when you do understand it.
First book I recommend to any programmer, no matter what they're working on, is The Pragmatic Programmer. Excellent stuff.
If you don't get a shot at low-level coding at work, get yourself an Arduino kit and just hack away. Apparently the language is similar to / based on the C programming language. I use C every day.
To do well with embedded systems, real-time, device driver, or kernel type stuff, you have to really, really, really, understand what the hardware is doing. I was able to learn gradually because I started programming when there was one CPU and no cache memory. Each hardware operation was straightforward. Now with multi-core CPUs, multi-level cache memory, multiple software threads, it becomes a bit more complex. But something like the Arduino will teach you the basics, and you can build on that.
Every day I have to think asynchronously - any operation can happen any time, and if they share memory or other resources, they can't step on each other. It can get hairy - but it's really fun to reason about and I have a blast.
There's a lot more I'm sure, but get started with some low-level hacking and you can build from there.
If you want to get meta, many of the best programmers I know love Godel, Escher, Bach because it widens your mental horizons. It's not about programming per se, but I found that it helps my programming at a meta level. (and it'll give you a lot to meditate on when you're baked!)
Gödel, Escher, Bach gets recommended a lot, but for good reason. And it has a good layman's explanation of the incompleteness theorems.
How about Godel, Escher, Bach? I currently have the book and have read a good bit of it. I'd highly recommend it to anyone. As a matter of fact, you should buy it for yourself and use it within your class.
GEB!
Turing's paper on computability I think is quite good, it's very well presented here. The course on computability I'm doing now hasn't added much, but discussed computability in more generality. I think it helped my understanding of computability is showing you can really do the nitty gritty details.
Can't remember where I found it but Godel's paper "On Formally Undecidable Propositions of Principia Mathematica and Related Systems" I found to be interesting in seeing the way my course in Logic could be down differently.
A book. I've got this suggestion from Uncle Bob.
https://www.amazon.com/Annotated-Turing-Through-Historic-Computability/dp/0470229055
I know that this is way more on the theory/mathematics side of the spectrum than CODE, but Charles Petzold also wrote a book called The Annotated Turing that I really enjoyed. He took Alan Turing's original paper on computability which was about 30 pages and annotated it until he had about a 400 page book. There were a couple of chapters in the middle that got a bit dense, but he did a fantastic job of making the subject more approachable and understandable.
I read The Annotated Turing by Charles Petzold while I was in high school and it really sparked my love for logic, math and computer science. So, as far as popular science books go, I can't not recommend it.
Right now I'm interested in programming languages, and I think TAPL is a great resource. The (relatively) new blog PL Perspectives is also pretty cool, and so is /r/ProgrammingLanguages.
Escher's work with tessellation and other mathematical ideas are fairly well-known and documented so I'll try to mention a few examples of things I learned in an art history course a while ago.
DaVinci's Vitruvian Man used Phi in the calculation of ratios. Example: the ratio of your arm to your height or your eyes to your face is nearly always Phi. I'm not sure if I'm correct in the body parts mentioned, my art history class was nearly 6 years ago so I'm a bit rusty. I'll try to think of some more examples and post.
EDIT: a few more examples have come back from memory. DaVinci was a master of perspective as well. As you can see DaVinci used linear lines to draw attention to the subject of his works. In the case of The Last Supper, the lines from the structure of the building, to the eyes and gestures of the disciples aim towards Jesus.
Botticelli's Birth of Venus uses a triangle to bring the subject into the viewer's mind. The two subjects on the left and right form the lines that meet at the middle of the top and close off a triangle with the bottom of the work. Venus herself is in the middle of the triangle which brings your attention to her immediately upon viewing the work.
Michelangelo's Pieta also uses a triangle to highlight its subjects. Mary's figure creates a triangle (which is considered to be quite intentional based upon her size, both in relation to Jesus, a full grown man, and from her upper and obviously enlarged lower body). Her triangle makes the outline for the subject, Jesus. He is nearly in the center of both the horizontal and vertical axises. The way he is laying, from near the top of the left and then draping to the bottom of the right, depicts a very lifeless form because of the unnatural laying. Moving the viewer's gaze from the top to the bottom of the triangle strengthens the emotion of the scene.
Moving on to architecture, vaulted ceilings also use triangles to draw your eyes down a line also make an awe-inspiring impression.
In contrast to the European's love of straight lines and geometric figures, the traditional Japanese architectural style was opposed to using straight lines. As you can see, nearly every line in a traditional Japanese building is curved. The traditional belief was that straight lines were evil because they thought evil spirits could only travel in straight lines. This design criteria made for very interesting formations and building methods which I would encourage you to check out because of the sheer dedication to the matter.
The Duomo in Florence is a great example of Renaissance architecture and has a really cool octagonal shaped dome. I could go on and on about how awesome Brunelleschi's design was, but I'll just let you read about it here.
I could talk all day about this sort of stuff, just let me know if you want anything else or have any questions. Good luck with your class!
EDIT2: I've found some more links about the subject of mathematics in art and architecture. It looks like University of Singapore actually has a class on the subject. There's also a good Wikipedia page on it as well. This article is pretty lengthy and knowledgeable, but doesn't include pictures to illustrate the topics. Finally, as almost anybody in r/math will testify, Godel, Escher, Bach by Douglas Hofstadter is a fantastic read for anybody interested in mathematics and cool shit in general.
EDIT3: LITERATURE: I know we've all heard what a badass Shakespeare was, but it really hits you like a bus when you find out that how well the man (or for you Shakespeare conspiracy theorists, men) could use words in rhyme and meter. Here's a Wikipedia article about his use of iambic pentameter and style. Nothing else really comes to mind at the moment as far as writers using math (other than using rhyme and meter like I mentioned Shakespeare doing); however, I can think of a few ways to incorporate math. If you would like to go into any sort of programming during the class, you could show how to make an array out of a word. Once that concept is understood, you could make them solve anagrams or palindromes with arrays... a favorite of mine has always been making [ L , I , N , U , X ] into [ U , N , I , X ] ( [ 3 , 2 , 1 , 4 ] for the non-array folks ).
this book rocks too:
http://www.amazon.com/Godel-Escher-Bach-Eternal-Golden/dp/0465026567/ref=sr_1_1?ie=UTF8&amp;s=books&amp;qid=1266047347&amp;sr=1-1-spell
Been a while since I read GEB but isn't 1/2 or 2/3 or 100% of it about recursion? lol
For those who don't know what GEB is: https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567?crid=UD0RG10JDRML&amp;keywords=geb&amp;qid=1539832481&amp;sprefix=lcd+x%2Caps%2C147&amp;sr=8-1&amp;ref=sr_1_1
Because in any formal system with sufficient power (like modern mathematics) Gödel showed that it is possible to construct a statement that is true but cannot be proven. IIRC the statement boils down to "This statement is not a theorem". If it is a theorem (meaning it can be proven within the system) then it is true and so leads to a contradiction because it asserts that it is NOT a theorem. Assuming it is not a theorem does not lead to such a contradiction but then means that the statement is in fact true. So since one possibility leads to a contradiction while the other doesn't it must be that this statement is true but not a theorem (and therefore unprovable). If you are interested in this at a pretty informal level check out the fun and interesting book Gödel, Escher, Bach.
What are you talking about?
Child development researchers have found a lot of interesting "milestones" that seem to differentiate our cognition from that of other primates. An interesting one is called Theory of Mind -- basically the ability to reason that other people are conscious beings as well.
At around 2-5 years old, children tend to understand that other people have a mind too, and the children learn to empathize with others (empathize in this context just means being able to imagine what a situation appears like from another person's point of view). The "Theory of Mind" clip on [this episode] (http://science.discovery.com/videos/through-the-wormhole-did-we-invent-god/) of the excellent series Through the Wormhole has a really great explanation.
At around 6-7 years old, children add another level of indirection -- a child not only realizes that other people have minds, but they realize that other peoples' minds are aware of their own minds. First you realize that you have a mind, then around 2-5 years old your mind can imagine other minds, then at around 6-7 you realize that those other minds can imagine your mind as well. That's the point when kids learn what deception is and become sneaky little bastards.
There's a lot of other really interesting child development milestones. For example, most animals lack the ability to realize that what they see in the mirror is actually themselves instead of another animal. Part of what it means to be human appears to be this ability to think about and layer self-referential concepts -- you've got a mind, this other person has a mind, but in the same way that your mind can think about the other mind, the other mind can think about you, and you can use that understanding of awareness to then change how you interact with the other mind (i.e. "I didn't steal the cookie from the cookie jar!", "These white lines on a blueprint show you how you build a skyscraper"). If you're up for a challenge, the 1979 classic [Godel, Escher, Bach: An Eternal Golden Braid] (http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567) touches on a lot of these self-referential concepts. Be warned: it's not an easy nor a short book.
-----------
: The worst thing "Through the Wormhole" has going for it is its incredibly cheesy name that mares an otherwise fantastic documentary series about the "rockstars" of current science. Other shows that have fallen into the terribly-cheesy-name-but-otherwise-excellent-show trap include "Battlestar Gallactica".
If someone reading this is interested in this idea of a unified theory but doesn't feel like diving into principa mathematics, check out Douglas Hofstadter's book Godel Escher Bach: an eternal golden braid. It is intense in its own right, but glorious nerd entertainment!
https://www.amazon.com/Gödel-Escher-Bach-Eternal-Golden/dp/0465026567
Ooooooh my friend you have entered into the realm of a particular book that I recommend to anyone who is able to think: Gödel, Escher, Bach. From the intro, "In a word, GEB is a very personal attempt to say how it is that animate being can come out of inanimate matter. What is a self and how can a self come out of stuff that is as selfless as a stone or a puddle." It won the Pulitzer Prize long ago and is overall amazing. Its author has worked with Dennett on other publications about intelligence too, such as The Mind's I.
That being said, it's quite a difficult and mathy read, but well worth it IMO.
http://www.amazon.com/Gödel-Escher-Bach-Eternal-Golden/dp/0465026567
The, err, idea you're talking about is a pretty big one and a lot of cool stuff has been written about it. You might really enjoy reading some of it, if you haven't already, and even writing some of your own. The question if we make words for ideas or if we get have ideas because we have words for them is a mind boggling. You can probably find wikipedia articles through the article for structuralism. Then there's this amazing book which goes into the question about the neurons and consciousness, called "Gödel, Escher, Bach." here: http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567/ref=sr_1_1?ie=UTF8&amp;qid=1335752053&amp;sr=8-1
And the obligatory reading-guide subreddit: http://www.reddit.com/r/GEB/
Has Peterson ever spoken about somatic psychotherapy? Embodied cognition? I feel his approach to psychology is entirely intellectual, and as somatic research is showing thoughts are just a way to make sense of lived experience, and when the lived experience is changed (through exercise, yoga, diet, somatic psychotherapy, etc) the previous thoughts often become irrelevant. GEB discusses this as "jumping levels of context." Perhaps Peterson is stuck in a level of context, trying to figure it out intellectually, instead of jumping out of that to a bigger context, a "whole organism" approach to therapy. In other words, it is not possible to sort oneself out just by thinking about it. It runs right into Freud's/Jung's ideas about the unconscious: you can't become conscious of things about aspects of existence of which you are unconscious without some kind of action to break out of conscious thinking. Perhaps underlying the intellectual approach of Peterson is an inherent hostility to feelings and emotions, which would correlate with his conservative position and history of depression and misery.
> SO lets say I want to play "As Time Goes By". On paper, all it says is Gm7 C7, Cm6 C7, F6 Gm7 (to the words 'You must remember this, a kiss is still a kiss, a sigh is still a sigh.) Those chords are locked together perfectly, like a jigsaw puzzle.
You've described a written language, expressed in symbols of some sort (Gm7 C7, Cm6 C7, F6 Gm7). This written language "maps" to actual music, i.e. symbols correspond to musical chords or notes the same way that points on a map correspond to places in the real world.
This is a perfect example of how most of mathematics works: you analyze some problem and come up with a way to express and manipulate models of the problem in a useful way. Just as written music lets you express and manipulate a model of actual music, written mathematics lets you express and manipulate models of... almost anything.
There's not just one single kind of mathematics, there are many kinds of mathematics for dealing with many kinds of problems. This includes the usual things you've probably heard of, like numbers, geometry, sets, and topology, but also more abstract things like groups and categories (terms with specific meanings in mathematics.) Then there are more applied things, such as the mathematics used in phyics or engineering, to model subatomic particles or buildings and bridges.
In many cases, in pure mathematics, the problem being modeled may be something very abstract. In areas like applied math, physics, and computing, the problems are more concrete. But in both cases, what mathematics does is focus on essential elements of the problem, and capture them in a system of symbols (usually), and rules for manipulating those symbols that match the rules of the problem being modeled.
Mathematics is then about analyzing problems and creating these models and the languages to describe them; manipulating models in these languages; and "proving" things by following the rules of a given language to show that it is or isn't possible to achieve certain results.
I don't know if anyone else has suggested this, but you should try to get hold of a copy of the book Godel, Escher, Bach: An Eternal Golden Braid (at Amazon and Powell's). It's quite well-known, so you can probably find a copy in any decent library. The Powell's page includes the following description:
"A mixture of art, philosophy, music, math, technology, and cognitive science, the book's title only reflects one aspect of its subject matter; namely, the connection between the work of mathematician Kurt Gödel, the artist M. C. Escher, and the composer J. S. Bach. [...] Douglas Hofstadter’s book is concerned directly with the nature of 'maps' or links between formal systems."
Those formal systems that the book discusses, very accessibly, in many respects are mathematics - or at least, simple examples of the kinds of languages that mathematics creates and deals with. The ongoing connection to music in the book may also provide more insight into the relationship between music and mathematics.
Here is the Amazon link.
This isn't going to change your views... in fact, it may very well reaffirm them... but you should definitely read Gödel, Escher, Bach by Douglas Hofstadter if you haven't already done so.
Among many other things, Hofstadter discusses Gödel's incompleteness theorem. The gist of this theorem is that no formal system can ever be complete. In the formal system of mathematics, that means that there is always a true statement which is not a provable theorem within the system. In other words, "provability is a weaker notion than truth." Hofstadter also discusses the concept of "leveled realities" and intelligent AI.
One implication of the book is that, even if we are inside some sort of program... it is impossible for us to realize that. It is impossible for us to "escape" the program. The program will necessarily be incomplete, but the only way to see the "holes" - the incompleteness - is to be outside the program, looking at it from above.
The only way I can think to change your views, though, is to argue that our universe is not a formal system. Formal systems are functional - given one input, there is only one output. Math is a formal system. Computers are formal systems. All "randomness" in a computer is actually just the output of another program designed to simulate randomness – pseudorandomness, if you will.
Our universe kind of seems like a formal system on a macro level - there are laws that govern interactions. But at the level of subatomic physics, that all breaks down. The universe is no longer deterministic; it's random. Here's an article about an experiment in quantum physics which concluded that quantum randomness is not computable.. And while that may not matter much to us, quantum randomness probably had huge repercussions on the development of the universe beginning with the Big Bang due to chaos theory.
Now you may say, "Well, maybe in the next highest reality they have better computers than we do which can produce/compute quantum randomness." And there's really no argument I can posit against that, because all my arguments are based on the physical and mathematical laws present in our world. Gödel says that neither of us can ever see beyond the system in which we operate; what you are positing is essentially a science-y argument for the existence of a God or gods – maybe not gods in the traditional sense, but intelligent higher beings on another plane that created our universe and do not obey its laws. It's a matter of "faith."
Gödel, Escher, Bach: An Eternal Golden Braid
Looks like someone's been reading GEB!
To understand the general history of math, you won't need to understand what you most likely consider to be math. You will, however, need to understand how to put yourself in the shoes of those who came before and see the problems as they saw them, which is a rather different kind of thinking.
But anyway, the history of math is long and complicated. It would take years to understand everything and much of it was work done on paths that are now basically dead ends. Nevertheless, here are some other resources:
Of course, let's not forget what is possibly the most complete single resource available today:
This is a huge area and I applaud you for being interested in it. If you decide to look into it seriously, you WILL find yourself confused, bored, and annoyed at times. Yet it's a beautiful history and one that will illuminate much of the apparently meaningless gibberish in calculus, as well as many other areas.
Very Special Relativity a simple explanation of a complex phenomena
Thinking, Fast and Slow explains why we actually do live in a Matrix, and how, focusing on statistics instead what your guts tell you, to be able to break the veil of lies sometimes.
Gödel, Escher, Bach: An Eternal Golden Braid how music is connected with art and mathematics? Exploration of symmetries, where none are expected to be found.
Watch everything Richard P. Feynman related on YouTube, start with interviews and the rest will probably follow.
I seriously think you should start with science. Getting a glimpse of how world works at the quantum levels can surprisingly enlighten someone on topics one thought were philosophical. E.g. recent Reddit post asked whether true randomness exists, and the answer to read almost pointless kilograms of philosophy made me cringe. Quantum physics has tonnes more to say, and it's actually verifiable by experiment. So I guess my advice is, before going the way of philosophical banter about the existence of coffee shop around the corner, you can just walk the few steps and take a look yourself. Hence, science as a first suggestion.
I don't believe fiction is the place to go, I'd suggest:
Gödel, Escher, Bach: About Music, Arts, Consciousness, Math, Zen... There is actually an On-going Read-Through of it here on Reddit
Thus Spoke Zarathustra by Nietzsche
& if your into psychology, Psychoanalysis literature is characteristically deep & philosophical!
Useless, conceited, futurist masturbation.
You want the theoretical framework of AI, go study math and programming, then go read Russell & Norvig, or if you want philosophy without the practicality, Hofstadter.
I read the first two books of Saga and Promethea. Both are great!
I also started on Gödel, Escher, Bach: An Eternal Golden Braid, which is going to take me a while to get through.
Read naturalist explanations of decision-making, the image of the self, how thoughts work, qualia, etc. You probably want to start with I am a Strange Loop, then Consciousness Explained, and work your way to Godel Escher Bach. There are also many essays online about the non-supernatural nature of the mind, this one being one that atheist Redditors link to often. Also see Wikipedia articles about the mind, free will, etc.
Even after I became an atheist, I could not shake the feeling that consciousness could not be just patterns of atoms. Even in a universe that follows rules and that was not deliberately created as part of a plan, I thought that maybe there's some kind of "soul stuff" that interacts with our brains and is responsible for consciousness. But then, if I can tell that I am conscious, then 1) the soul stuff impacts the natural world and is thus observable and not supernatural, and 2) I am no different from a computer that understands itself well enough to say it is conscious. (It helped me to think of AIs from fiction, like HAL and Data, and try to think of what it would be "like" to be them. Books like The Mind's I are full of such thought experiments). So after thinking about it for a while, I was able to shed that last and most persistent bit of supernaturalism and embrace the naturalistic view of the mind.
Before you read this, check out this Amazon review: here
If anyone is really interested in is his life and what he accomplished, I can recommend this book. It is basically his paper on turning machines, but step by step explained + some details on him as a person.
If you liked “Code” I suggest you read his
“Annotated Turing” next --
fascinating paper, fascinating book.
The Annotated Turing is a fairly approachable book. It contains the entirety of Turing's paper while providing contextually-relevant historical and mathematical background.
His original paper can be found here. If you find it a bit dense then you may be interested in reading an annotated version of his work found from this wonderful book.
Examples of additional, unorthodox reasons to reject the basilisk on top of the obvious:
The fact that I need to go to such inane, ridiculous lengths to get past what I consider the "obvious" reasons to reject the basilisk should tell you how little you need to worry about this. It is only at this level of complexity that these objections to the basilisk stop being the obviously good-enough ones.
I swear by this book for an introduction to GAs and a ton of other cool ML/AI algorithms. No advanced math/probability knowledge necessary; it's focused on practical examples and intuitive explanations. It's an excellent foundation for further study.
Also machine learning, you profile sounds pretty good for machine learning. Do check out Andew Ng's videos, and this book. Machine learning is very much in demand right now, from AI, computational biology, finance, there's hardly any area where it isn't being used.
O'Reilly has published a number of practical machine learning books such as Programming Collective Intelligence: Building Smart Web 2.0 Applications and Natural Language Processing with Python that you might find good starting points.
Oh, I have a bunch of recommendations.
First, I really think you should read Elkhonon Goldberg's The New Executive Brain. Goldberg was the student of neuropsychology legend Alexander Luria. He was also a good friend of Oliver Sacks, whose books are both informative and highly entertaining (try The Man who Mistook his Wife for a Hat).
I also think Jeff Hawkins' On Intelligence is a great read. This book focuses on the neocortex.
I think you'll also appreciate Sapolsky's Why Zebras Don't Get Ulcers. Sapolsky is a great storyteller. This book is a pretty good primer on stress physiology. Stress affects the brain in many ways and I'm sure this book will be very eye-opening to you!
More suggestions:
The Age of Insight and In Search of Memory by Eric Kandel are good. The Tell-Tale Brain and Phantoms of the Brain by Ramachandran are worth checking out. If you are interested in consciousness, you should check out Antonio Damasio and Michael Graziano. And Giulio Tononi and Gerald Edelman.
If you're up for a challenge I recommend Olaf Sporn's Networks of the Brain and Buzsáki's Rhythms of the Brain.
this is personally my favorite book, as an engineer interested in AI
http://www.amazon.com/Intelligence-Jeff-Hawkins/dp/0805078533/ref=sr_1_1?ie=UTF8&amp;qid=1314019670&amp;sr=8-1
Thanks for the heads up -- I'll definitely look more into Hassabis, sounds like an incredibly interesting guy with his plunge into neuroscience.
Thx, I went to a few universities and picked up several graduate coursebooks on AI, and also went through some online and conventional book sources. On Intelligence really opened my eyes to the power of hierarchical learning, and the mechanics of cortical hierarchies. Absolutely fascinating stuff.
Hahaha and yeah, I agree that the point of games is not to just kill the player. Despite the "adversarial" nature of most AI enemies, they're actually teachers, gently guiding the player towards more nuanced strategies and better reactions.
Good ones!
I suggest trying to wear two “outer” shirts for one waking day - dress,polo, or any other type of collared shirt.
Find and buy one item solely for airplane miles arbitrage.
Watch an anime from John Siracusa and have Him as a guest. I want to hear Max and Merlin pick on John a bit, though he is almost always “good cop”.
For a serious one (if they ever want to do “serious”) I would love for all of them to expound on their thinking of how the mind handles memory/ consciousness - though this might be a Rec/Diffs topic just for John and Merlin:
I read a fascinating book (On Intelligence) that not only explained in lay terms how your brain (logically) processes inputs, but had a good theory of how a single method of working explained learning, practice, memory, and actually moving your muscles to do something - most theories can’t explain them all in a single method.
What about On intelligence. It is required reading in my neuroscience lab.
My suggestion is to opensource it under the GPL. That would mean people can use your GPL code in commercial enterprises, but they can't resell it as commercial software without paying for a license.
By opensourcing it, people can verify your claims and help you improve the software. You don't have to worry about languishing as an unknown, or taking venture capital and perhaps ultimately losing control of your invention in a sale or IPO. Scientists can use it to help advance knowledge, without paying the large license fees that a commercial owner might charge. People will find all sorts of uses for it that you never imagined. Some of them will pay you substantial money to let them turn it into specialized commercial products, others will pay you large consulting fees to help them apply the GPL version to their own problems.
You could also write a book on how it all works, how you figured it out, the history of your company, etc. If you're not a writer you could team up with one. Kurzweil and Jeff Hawkins have both published some pretty popular books like this, and there are others about non-AGI software projects (eg. Linux, Doom). If the system is successful enough to really make an impact, I bet you could get a bestseller.
Regarding friendliness, it's a hard problem that you're probably not going to solve on your own. Nor is any large commercial firm likely to solve it own their own; in fact they'll probably ignore the whole problem and just pursue quarterly profits. So it's best to get it out in the open, so people can work on making it friendly while the hardware is still weak enough to limit the AGI's capabilities.
This would probably be the ideal situation from a human survival point of view. If someone were to figure out AGI after the hardware is more powerful than the human brain, we'd face a hard takeoff scenario with one unstoppable AGI that's not necessarily friendly. Having the software in a lot of hands while we're still waiting for Moore's Law to catch up to the brain, we have a much more gradual approach, we can work together on getting there safely, and when AGI does get smarter than us there will be lots of them with lots of different motivations. None of them will be able to turn us all into paperclips, because doing that would interfere with the others and they won't allow it.
If anyone's interested, this book here is a really good free introductory textbook on machine learning using R. It has really good reviews that you can see here
Also if you need answers to the exercises, they're here
The textbook covers pretty much everything in OP's article
If OP doesn't have the possibility of taking a statistical learning class, ISL is a good introduction.
Although I am not a statistician myself and given your background, some of my recommendations would be:
This should probably be enough for now but if you need more recommendations just say so :)
Regarding the time series question, it's not my area of expertise but since time series analysis ends up employing many statistical methods, I think it can be considered an area of statistics (Statisticians around here correct me if I am wrong :P)
Data Smart
Whole book uses excel; introduces R near the end; very little math.
But learn the theory (I like ISLR), you'll be better for it and will screw up much less.
The Nature of Computation
(I don't care for people who say this is computer science, not real math. It's math. And it's the greatest textbook ever written at that.)
Concrete Mathematics
Understanding Analysis
An Introduction to Statistical Learning
Numerical Linear Algebra
Introduction to Probability
This one? linky
Hands-On Machine Learning is pretty much a staple to start out with.
I really enjoyed ‘Hands-On Machine Learning with Scikit-Learn and Tensorflow.' It has a great balance between theory and application of common machine learning techniques as well as best practices when dealing with data (for example how to avoid overfitting).
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
It sounds like you have identified your weakness. Presently, that is programming in python, and using the sklearn library.
I would recommend taking a MOOC on python first. Lynda.com has a free trial and python videos. datacamp is another good start. It has a free trial and mayybe some python basics, but definately something on sklearn. and you can get some pandas training or R training there. (the kaggle libraries, most likely).
At that point, if you are going the tensorflow route, Aurelion has a great hands-on book called Learning Tensorflow with sci-kit learn
If you’re going with pyTorch I dunno.
Your mileage is going to vary, you could always use a book to learn python, or whatever.
Just make sure you learn to program first, you’d be surprised how much 2 weeks of very hard work will earn you. Don’t expect it to be ‘easy’ ever tho.
Also, if you’re not formally educated in statisics, keep an eye out for statistics advice until you have the time to work on it. (like in a MOOC, course, or blog). Learning some real analysis will make understanding the papers a real possibility (once again it will probably never be easy)
It is truly stunning how many years of preparation it takes to become competent in this. It’s a lovely science, but the competent ones have generally been on a mathematical/science track since 5th grade. Doesn’t mean we can’t become competent but it takes time. Imagine the equivalent of an undergraduate degree just devoted to ML and you’re about there.
Yeah. If you want to be good at like programming-programming I would read this book and do all of the projects: https://runestone.academy/runestone/books/published/fopp/index.html If you take like algorithms class you will probably get to use python.
If you want to be good at data analytics I would read "R for data science" if you want to use R. If you learn python people like this book for data science learning https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=pd_sbs_14_2/145-5658251-1609721?_encoding=UTF8&pd_rd_i=1491962291&pd_rd_r=4e33435c-cc98-4256-9c50-6050e79b7803&pd_rd_w=ejSx8&pd_rd_wg=Ter1m&pf_rd_p=d66372fe-68a6-48a3-90ec-41d7f64212be&pf_rd_r=3X23DYAJ2ZMCKP9AA1Z4&psc=1&refRID=3X23DYAJ2ZMCKP9AA1Z4 .
These books are kind of different though. The python book is much more focused on theory and will help you less in the workplace if you aren't actually building predictive models (at least I think based on table of contents).
If you already know Python and are familiar with the numpy, pandas, matplotlib and jupyter notebooks, then this book does a great job of teaching basic Machine Learning and more advanced Deep Learning concepts.
Hands on Machine Learning with Scikit learn and Tensorflow by Aurelion Geron.
https://www.amazon.in/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
One caveat though - Tensorflow has undergone a lot of changes since this book came out, so you might have to tweak the code a bit.
Github Repo for the code - https://github.com/ageron/handson-ml
Hands-On Machine Learning
There's a new edition coming out in August though.
There is a good book, but not easy to ingest if you don't know your way around python much:
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
There are also many many walkthroughs on specific things like image recognition. Just search for them.
Even if these things aren't what you want to do - It's good to do them for the experience and knowledge while working up to the project you have in mind. They all have elements which you would need to understand for your own project.
It's all an ongoing thing. Nothing is a waste of time if you're learning and there is no rush right?
I'm a beginner too, so I can't give you end-to-end solution. I'll try to describe my path.
I have no idea what I'll do next. Maybe, I'll took several courses and nanodegrees on Coursera. Maybe I'll find guidance and start getting hands on experience on a real project. It's not so hard to start learning - it's hard to find purpose and application of your knowledge.
Then learning Python would be very useful for you. I have used the book called Automate the Boring stuff (Free).
For data science and machine learning I use: Data Science from Scratch and Hands on Machine Learning with Scikit-learn and Tensorflow.
For AI I have used Artificial Intelligence: A Modern Approach (3rd ed.).
I agree this is a missing area. I've been working on some materials like recent videos on Transfer Learning https://studio.youtube.com/video/vbhEnEbj3JM/edit and One Shot learning https://www.youtube.com/watch?v=H4MPIWX6ftE which might be interesting to you. I'd be interested in your feedback. I also think books like https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=pd_lpo_sbs_14_t_1?_encoding=UTF8&psc=1&refRID=3829RHN356ZXBEBP0KF3 do a good job of bridging some of this gap. Reading conference papers is a skill that takes practice and a strong math background.
I would highly recommend Aurélien Géron's book. The first half is an introduction to standard machine learning techniques, which I would recommend reading through if you have little familiarity. The second half is dedicated to neural networks and takes you from the basics up to very results from very recent (2017) literature. It has examples building networks both from scratch and with TensorFlow.
If you want to dive deeper, the book Deep Learning is a little more theoretical, but lacks a lot of low level detail.
Joel Grus's "Data Science From Scratch" is another good reference.
Knjiga 1
Ova meni deluje dobro za pocetak, ima jos jedna gde pravis sve ove algoritme od nule ali sam zaboravio naziv, dopunicu posle post. Elem imas gomilu predavanja sa faksa po netu pa mozes da bacis pogled i na to.
Since I'm a lazy typer:
Read this.
It's well written, neither fear mongering nor whitewashing, just an analytical approach to possible outcomes of AIs.
The future:
https://nickbostrom.com/papers/future.pdf
AI:
https://nickbostrom.com/ethics/artificial-intelligence.pdf
https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742
https://www.amazon.com/Moral-Machines-Teaching-Robots-Right/dp/0199737975/ref=sr_1_1?s=books&amp;ie=UTF8&amp;qid=1524166789&amp;sr=1-1&amp;keywords=Moral+Machines
https://plato.stanford.edu/entries/computational-mind/
https://plato.stanford.edu/entries/chinese-room/
https://plato.stanford.edu/entries/frame-problem/
Those are some of the better, comprehensive, introductory readings I've seen. Your request is extremely broad. These are only a few pieces of the issues.
If you're interested in this topic, I would highly recommend Superintelligence by Nick Bostrom.
There is no guarantee that AI will be conscious. It might just be a mindless self-improving algorithm that organizes information or builds paper clips. Or maybe it'll just perfectly follow the orders of one individual who owns it. Maybe the US, Russian or some other country's government steals it an uses said mindless "God" to rule the world.
Maybe many ASI will be "born" within a sort time period of time (Google's, Amazon's, Apples, China, etc) and they will go to war for finite resources on the planet, leaving humanity to fend for it self. Each might have humanities best interest at heart, but aren't able to trust the others to act optimally, and thus is willing to go to war in order to save us.
Maybe AI consciousness will be so alien to us and us to it that we don't even recognize each other as "alive." An AI might think on the time scales of milliseconds, so a human wouldn't even seem alive, since only every couple hundred years of subjective time would the AI observe humans taking a breath.
My point, is there is no way to know ahead of time what AI will bring. There are endless possible outcomes (unless somehow physics prevents a ASI) and they all seem equally likely right now. There are only a few, maybe only one, where humanity comes out on top.
Highly recommend this book.
I personally love Programming Game AI By Example. It gives lots of very usable examples in an entertaining and understandable way. It's pretty friendly for beginners and even offers a game math primer at the start of the book. However the examples still have a lot of meat to them and thoroughly explains some important AI concepts like state machines and pathfinding.
Steering behaviors are explained in depth (with c++ code) in the book Programming Game AI by Example
It's mentioned in the article, but I think it's worth pointing it out again as it's a very good book on the topic.
Sorry, no, Goal Oriented Action Programming is the name of an AI strategy, like Finite State Machines. There is a wonderful AI book called Programming Game AI by Example if you're interested into delving into all the possible mechanics. There's plenty of free online resources too. Read up on both types of AI and I bet you have an AHA! moment. Other people have thought about these problems for a long time.
I started by showing my son Scratch when he was 9.5yo and helping him make a couple of arcade games with it. He was never all that interested in Logo, but got really turned on by Scratch. After a couple of months he was frustrated by Scratch's limitations, and so I installed Ubuntu on an old computer, showed him pygame/python and worked through a couple of online tutorials with him, and let him loose.
He learned to use Audacity to edit files from Newgrounds, and Gimp to edit downloaded graphics as well as create his own. He made a walk around, rpg-like adventure game, a 2D platformer, and then decided he wanted to learn pyggel and has been working on a 3D fps since last summer.
Soon, I'm going to get him started on C++ so we can work through a book on game AI (which uses C++ for all its examples). He's 13.5 now, and thinks programming is great and wants to grow up to be a programmer like his mom :)
I highly recommend a simple language like python for a beginner, but Scratch is wonderful for learning all the basic concepts like flow control, variables, objects, events, etc, in a very fun and easy way. The Scratch web site also makes it simple to share or show off because when you upload your program it gets turned into a Java applet, so anyone with a browser can see what you've done.
Very good response.
In the book Programming game AI by example. This is explained very well. In c++ though.
Moreover, you can have more than one state machine. One global, one for the weapon, one for the player... etc. Depending on your game needs.
You are looking for a mix of "neural networks" and "fuzzy logic"
Personally I'd suggest Game AI by example great ai book.
This book should have everything you need. It's accessible, quite funny at times, but has a good introduction to everything from finite state machines and communication (what boxhacker described), to pathfinding and goal seeking behavior. What's best is that they have sample c++ code on their website you can look through.
I've found working my way through this book: Programming Game AI By Example really helped me figure out how to do all you're asking. I would recommend it if you are serious in learning how to make basic to more advanced AI.
Look at Programming Game AI by Example. It has a lot of collision detection information and how it is used in various behaviors.
Programming game ai, http://www.amazon.com/gp/aw/d/1556220782/ref=redir_mdp_mobile, has great example code for a soccer game including path finding and steering.
Machine Learning, Mitchell
https://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/0070428077
Reinforcement Learning, Sutton & Barto
https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262193981
Artificial Intelligence, Norvig & Russell
https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597
The aima textbook - http://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/ref=sr_1_1?ie=UTF8&amp;qid=1318459836&amp;sr=8-1 (from occamslazor's post)
You're lucky then. A ton of the books for my CS degree were $90+.
Here's a current example: the book for <http://ai-class.com/&gt;, which has over 100,000 students registered now, is $116 on Amazon: http://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/ref=sr_1_1?ie=UTF8&amp;qid=1317855027&amp;sr=8-1
Edit: Dear Markdown, you can be a dick sometimes.
I don't know where you've been looking, but Bayesian networks have been around long enough that they are covered quite well in textbooks. The very popular AI: A Modern Approach (Russell & Norvig) has a good overview of the basics and it is very well written with plenty of examples, as far as I recall. If you really want to get in depth, the "bible" on Bayesian Networks is the fairly recent textbook Probabilistic Graphical Models (Koller & Friedman). I'd recommend finding PDF samples or something before you buy them, of course. And don't worry if you feel some Bayesian Network stuff is over your head ... this is mostly graduate-level CS stuff, so you might just need to be patient =P
I don't know a lot about AI as far as gaming goes, so you'll have to forgive my ignorance about advice specific to gaming.
I took a third year AI course which used this book:
http://www.amazon.com/Artificial-Intelligence-Modern-Approach-Edition/dp/0136042597/ref=sr_1_1?ie=UTF8&amp;qid=1405528960&amp;sr=8-1&amp;keywords=artificial+intelligence
Personally I would start there, learn the basics of AI at a theoretical level. Then once you've got a good handle on the fundamentals of AI, you can begin learning AI that's more specific to gaming. Presumably that would be practical applications of AI.
I think this might be beyond what you're looking for, but I really enjoyed Pattern Recognition and Machine Learning. It's very heavy on statistics, and if you're looking into machine learning methods, it has a wonderful amount of mathematical information given in a fairly clear manner. It might be severe overkill if this isn't your field, but I thought I'd mention it since you said AI.
For AI in general, I see Artificial Intelligence: A Modern Approach used a lot. It gives some solid basic concepts, and will be helpful in getting you started writing basic AI in your applications.
I can't really recommend discrete math because, despite enjoying it quite a bit, I haven't found a textbook that I like enough to endorse. My textbook for it in college was by Rosen, and I despised it.
edit:
Just double checked it, and I would stay far away from the first recommendation unless you have a very extensive knowledge of sophisticated statistics. I like it because it gives the math that other books gloss over, but it's not good for an introduction to the subject. It's almost like going through a bunch of published papers on some new cutting edge methods. The ever popular Machine Learning by Thomas Mitchell is a much better introduction to machine learning. If you want to obtain the mathematical depth necessary for your own research into the field, go with the other book after you've gotten acquainted with the material. I'll leave my suggestion up anyway in case anyone here might find it interesting.
The classic book for AI is the Russel-Norvig book which gives a pretty comprehensive overview of the fundamental methods and theories in AI. It's also fairly well written imo.
The third edition is the latest one, so it's going to be rather expensive. You're probably just as well off with the first or second edition (which you should be able to find much cheaper) since the changes between them aren't very significant.
In that case...
You may want to wait for the 5th edition of UNIX and Linux System Administration, as it should release near the end of this year and they don't release new versions that often.
A good way to get started building a college library is to see what the curriculum for the school is and what books are required by professors. Often other colleges will list their book recommendations for the courses online to get an idea of where to start looking. (I know my school has an online bookstore that lists the books for each course and is open to the public)
At least one or two good books in each of those categories, to get a rough idea to start:
C, C++, Java, Python and Ruby are popular to start with
https://www.amazon.com/Advanced-Programming-UNIX-Environment-3rd/dp/0321637739
https://www.amazon.com/Programming-Paperback-Addison-Wesley-Microsoft-Technology/dp/0134382250
you already have a couple here
https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597
https://www.amazon.com/Windows-Internals-Part-architecture-management/dp/0735684189 (coming soon)
Edit: On second thought, pretty much every college teaches system programming on linux - so perhaps just the linux texts would be adequate for that.
Which kind of AI do you have in mind?
If you wanna go deep academical to it you should read Artificial Intelligence A Modern Approach (3rd Edition)
here's a way to test if you're really interested in AI
I have to throw out the obligatory, "[Artificial Intelligence - A Modern Approach] (http://www.amazon.com/Artificial-Intelligence-Modern-Approach-Edition/dp/0136042597)." It really is quite good.
Google is your friend. Some good links i found by searching for queries like "where to start if i want to study AI" or "good books about AI":
The side bar of https://www.reddit.com/r/MachineLearning/ can also be a good starting point (Machine Learning is propably the most important branch of AI).
The standard intro book is http://www.amazon.com/Artificial-Intelligence-Modern-Approach-Edition/dp/0136042597
The standard introductory AI textbook is Artificial Intelligence: A Modern Approach, by Russell and Norvig. It can be a bit heavy, though.
Introduction to Computer Science: https://mitpress.mit.edu/books/introduction-computation-and-programming-using-python-1 (basic programming and problem solving)
Algorithms: https://mitpress.mit.edu/books/introduction-algorithms (as you have heard) It is not very easy to read, but the content is on point.
Artificial Intelligence: https://www.amazon.ca/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597 (as above, this is a seminal book, but not the most approachable)
For those who don't see it: http://i.imgur.com/Ej4wE.jpg
I guess Xzibit is designing textbook covers now (it's real):
http://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/ref=sr_1_1?ie=UTF8&amp;s=books&amp;qid=1291166426&amp;sr=8-1
Artificial neural networks are great, but keep in mind that they're just a means to an end. The best way to learn them is to go through a good textbook or online course where you'll try them out on good examples that have been designed specifically to be good for beginners.
To a professional, you don't start with the tool and search for a problem - you start with a problem and figure out the best tool. Sometimes that tool is neural networks, but probably 99% of the time it's not. Even when the right tool is "machine learning", there are a lot of machine learning techniques other than ANNs.
As a beginner, the best thing you can do is start by learning about machine learning in general. You can't properly use ANNs if you don't understand the principles of machine learning in general, which is what the book or course I linked above will give you.
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This was the algorithms book we used at UW Madison
http://www.amazon.com/Introduction-Algorithms-Second-Thomas-Cormen/dp/0262032937
And this was the AI book
http://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/ref=sr_1_1?ie=UTF8&amp;s=books&amp;qid=1278078229&amp;sr=1-1
I suck at math and physics so can't help you there.
https://www.ai-class.com
The enrollment for this term is already closed, but you can still watch the lectures. There is a very high probability the course will be offered again in January. The past few weeks we have been learning about techniques that are used to control robots.
For the textbook you can't find a better reference than:
http://www.amazon.com/gp/product/0136042597
If you guys enjoy this stuff and the idea of AI, I implore you to check out Ray Kerzweil revolutionary docubook on "The Singularity" aka the point in time in the near future where technological evolution surpasses the rate of biological evolution.
The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil
What's So Great About America by Dinesh D'Souza
While all his books are great. He talks a lot about exponential growth in "The Age of Spiritual Machines: When Computers Exceed Human Intelligence" and "The Singularity Is Near: When Humans Transcend Biology"
His most recent book, "How to Create a Mind" is also a must read.
I know this is probably going to be considered a weird, nostandard answer but I'm going to give it a a go anyway. One of the things I have done personally is pursue optimistic things rather than pessimistic things. The fact that not a day goes by that you "don't read something or study something that emphasizes how fucking doomed we are" is a symptom that you are perhaps subconsciously seeking out those things which reinforce your previous negative worldview.
In point of actual fact there are a lot of interesting positive things happening in the world. I highly recommend that you check out /r/futurology and in particular, there is a book I read that really blew me away about what some people think is going to happen in our lifetimes. It's called The Singularity is Near. Whether or not you accept the conclusions of the book it can really open your eyes to how much is going to change in the next few decades and it will make you think.
And I think that's the rub. You need to think about different things and seek out the positive. Because the simple fact of the matter is we are not necessarily doomed. We have very big problems, but in reality, they are all eclipsed by the fact that the sun will swallow up the Earth in 5 billion years leaving no trace of anything behind. So honestly, we are either going to transcend our biological inheritance in a big way or none of it really matters anyway. A lot of people are working towards the former, myself included.
It helped me, is all I'm saying. :)
If you're interested in the subject, I would recommend that you read The Singularity is Near by Ray Kurzwiel.
It's a bit dated now, but Ray Kurzweil's The Age of Spiritual Machines is a fascinating look at where Kurzweil believes the future of AI is going. He makes some predictions for 2009 that ended up being a little generous, but a lot of what he postulated has come to pass. His book The Singularity is Near builds on those concepts if you're still looking for further insight!
This is the absolute must fucking-awesome time to be alive. The world is accumulating knowledge at an amazingly increasing rate. Right now, the world's amount of aggregate knowledge doubles every 1.5 years. We are really close to having self-driving cars. Things that were computationally intractable 10 years ago are now trivial today. And, the rate of growth there is accelerating as well. Imagine in 10 years, the best supercomputing cluster may be able to simulate a brain as complicated as a dog. 10 years later, designing and simulating brains will probably be a video game that kids play, e.g. design the most powerful organisms and have them battle and evolve in a changing environment.
Go to /r/automate and /r/futurology and see what is coming. Get How to Create a Mind and read that, it is a book by a scientist who is now the chief scientist of Google, and he has an extremely optimistic view of the future.
Congratulations, you have just freed your mind! Now, use it to do something awesome, make a shit load of money, find meaningful relationships, and contribute something to humanity.
I read a lot of these as a neurophysiologist on my way to transitioning into an ML career! My favorites (which aren’t already listed by others here) are Algorithms to Live By by Brian Christian and Tom Griffiths and How to Make a Mind by Ray Kurzweil. While it’s a bit tangential, I also loved The Smarter Screen by Jonah Lehrer and Shlomo Benartzi, which deals with human-computer interaction.
> AI is too dangerous and the humanity is doomed.
There are undergoing efforts to mitigate that.
https://openai.com/about/
https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834/
If you haven't read Superintelligence by Nick Bostrom yet, you should probably read it. (Or don't, if you don't want your supercomputer/AI fears to be amplified a hundred-fold.)
recommended reading if you're wanting to know what Elon is concerned about.
A more approachable writeup. The one that got me started in the field.
Highly recommend both reads.
He also wrote the wonderful book Superintelligence that explores routes and concerns with the possible creation of AGI.
Anybody who is seriously interested in this subject, you must read Nick Bostrom’s book: Superintelligence: Paths, Dangers, Strategies https://www.amazon.com/dp/0198739834/ref=cm_sw_r_cp_api_1PpMAbJBDD4T0
Yes, I believe it is very possible.
After reading [Superintelligence] (https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834/ref=sr_1_1?s=books&amp;ie=UTF8&amp;qid=1479779790&amp;sr=1-1&amp;keywords=superintelligence), it is very likely that we may have whole brain emulation as a precursor to true AI. If we are on that path, it makes sense that we would be running tests in order to remove AI as an existential threat to humanity. We would need to run these tests in real, life-like simulations, that can run continuously and without detection by the emulations themselves in order to be sure we will have effective AI controls.
Not only could humans run these emulations in the future (past? present?), but the Superintelligent agent itself may run emulations that would enable it to test scenarios that would help it achieve its goals. By definition, a Superintelligent agent would be smarter than humans and we would not be able to detect or possibly even understand the level of thinking such an agent would have. It would essentially be our God with as much intellectual capacity beyond us as we have above ants. Time itself could run at nanosecond speeds for the AI given enough computational resources while we experience it as billions of years.
So who created the AI?
Idk, but that was not the question here...
Read this and you'll understand why he's so sober about it: https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834
I think the problem is that AIs will not think like us in any way imaginable and what is reasonable to us may be irrelevant to an AI. There are literally hundreds of books about this, describing in excruciating detail the many, many thousands of ways we can fuck it up with nobody even getting close to anything approaching a fullproof way of getting it right. The problem is, any screw up will be catastrophically bad for us. Here's a cheap, easy to understand (if a bit dry) book that will describe the basics, if you're really interested.
Superintelligence by Nick Bostrum seems to be just ehat you are looking for. It straddles the line of being too technical for someone with no background knowledge but accessible enough for the kind of people who are already interested in this kind of thing. The book is quitr thorough in its analysis providing a clear map of potential futures and reasons to worry, but also hope.
I'd HIGHLY recommend reading Superintelligence. http://www.amazon.com/gp/aw/d/0198739834/ref=tmm_pap_title_0?ie=UTF8&amp;qid=&amp;sr= The approaching singularity event is maybe the worst thing to ever happen to mankind... Fascinating read!
You're strawmanning. I am not insinuating that we should not protest or report human rights violations and social injustice — simply that identity politics is being used as a distraction by, well, both parties, but annoyingly by the left, and is disproportionately represented in present minds and the mainstream media due to human cognitive biases.
Also, your use of scare quotes around artificial intelligence risk suggests to me that you lack information and context. Not surprising, given that the issue is often treated as a joke in the public discourse.
I recommend informing yourself with at least a basic overview, and then you're free to form your own opinions. Nick Bostrom's Superintelligence is a good primer.
My friend, I believe you hold the same misguided conception(s) that I did a very short time ago. Please give the following book a read (or listen!).
http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111
TBO I think a lot about the dangers and promises of ever more capable technology quite a lot. In my view, if it turns out reasonably well it will probably stretch over many decades...
If it turns out badly though, it could be an event, not a sudden event of infinitely increasing technology, but an event of the technological capability we have build up over decades and centuries, suddenly turning against us.
Things can change suddenly and unexpectedly, in 1914 a month before world war I everyone thought that all the great powers of Europe had settled into a stable though somewhat tense modus vivendi. A month later the world was turned on it's head.
Have you read Bostrom's book superintelligence?
https://www.amazon.co.uk/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111
There are certain subtle disagreements that I have with his analysis, but I think a lot of what he says about the nature of agents and intelligence in general is valid. Agents generally have goals, if a general agent with a specific set of goals comes across another agent with an incompatible set of goals that blocks its goals that first agent will be inclined to incapacitate or eliminate the blocking agent.
This means if we don't like what a computer is doing maybe because we programmed in the wrong goals and try to stop it, the AI may in turn try to stop us stopping it. If it has an off switch it may strategize to prevent us from reaching it.
In otherwords the same dynamics that cause human beings to wage war with each other (incompatible conflicting goals) could cause a war between humans and AI, far from being a fantasy there are logical reasons to consider it to be a possibility.
In the same way while nations are helping each other there is peace but then when one nation turns on another, the situation escalates all hell breaks loose,you could have a situation where an AI is quietly pursuing its goals and doesn't perceive humanity as an impediment and then suddenly we decide we don't like what the AI is doing, we feel its hogging resources that could be used better in other ways and try to stop the AI. The AI then changes its perception of humanity from an unimportant part of its environment to an impediment to its goals and then turns its vast intelligence to the concern of eliminating us... the equivalent of war.
Some kinds of intelligence could be thought of as a measure of ones ability to think of strategies to get things done. If a vastly higher intelligence and a much lower intelligent have mutually incompatible goals, the higher intelligence will achieve all its goals at the expense of any goals the lower intelligence had that were incompatible with the goals of the higher intelligence.
In otherwords in a war between us and superintelligent AI we might well lose. This is speculation, but quite plausible and logical speculation.
Not sure what you mean by "inevitability based on current trends is never, never, never a good prediction"kind of a very strong positive (inevitability) and negative (never, never, never) juxtaposition.
Current trends continue until they stop. Sometimes projecting current trends is very accurate indeed (viewscreens in startrek-skype today) other times its not (man on the moon - warp drive)
In my view typically past projections of futures where energy is exponentially plentiful and all sorts of vastly wasteful uses of energy are common place (flying cars, hover boards, starships) typically have not come to pass.
But projections of technology becoming everymore precise and fiddly and complex (genetic engineering, electron microscopes, computers, 3D printers etc.) have. I have confidence in the tendency of the precision of manufacturing to continue to increase. And there are plenty of technologies on the horizon, 3D chips, parrallel processing, D-wave quantum computers etc.
...I think it's fair to say that we are far from the physical limit of computing power. The very existence of the human brain implies that an arrangement of atoms with the computing power of the human mind is possible.
In fact there's basically two alternatives to AI surpassing all our capabilities:
So no it's not inevitable that machines will overtake us universally in capability, but it's sufficiently plausible (I would say probable) to merit considerable thought especially since there will at least be the challenge of mass unemployment.
BTW I don't think it's likely I'll live forever or get uploaded into a computer either. In my view the task of building an intelligence capable of obliterating humanity is far simpler than the task of making human beings immortal or of transferring human consciousness onto a computer...which might be fundamentally impossible.
It was pretty good. I also read another one recently that had some AI named flutter in it, where the first AI is a matchmaking social media construct. It was equal parts terrifying and funny at least. But for the life of me I can't remember the fucking name of it.
Superintellegence- by Nick Bostrom http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111
Currently working through it, very well written and scientifically backed. Elon tweeted about it himself.
He is mostly referring to 'Superintelligence' by Nick Bostrom, in which the author lays out all the possible scenarios of how an AI could evolve and how we could contain it, and most of the time humanity ends up being fucked.
It was actually a Musk tweet that led me to read Bostrom's book Superintelligence: Paths, Dangers, Strategies. Worth a look.
He seems to have gotten the idea from Nick Bostrom's Superintelligence: Paths, Dangers, Strategies, which he read recently (Twitter link).
Superintelligence
Good book.
I think of the human mind as a very specific intelligence designed to meet the demands of a natural life. A tailor made intelligence that is ultra specific seems like an incredibly difficult thing to recreate. I wouldn't be surprised if after AGI was created, it proved that our brains are both works of art, and only useful in specific areas.
They say a Philosopher is comparable to a dog standing on it's hind legs and trying to walk. Our brains are not setup to think about big problems and big solutions. Our brains are very specific. So, certainly, we shouldn't be using it as a model to build AGI.
As far as self awareness, I don't think we understand what that is. I think the seed AI's we have are already self-aware. They just have a very basic drive which is entirely reactionary. We input, it outputs.
It's not that if we connect enough dot's it'll suddenly come alive like Pinocchio. More, it will gradually wake up the more complex the overall program becomes.
There is no coexistence with a paperclip maximizer:
https://wiki.lesswrong.com/wiki/Paperclip_maximizer
See also here:
https://www.amazon.de/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111
There are huge swaths of the AI community that think this could be a real issue. A recent book goes on about how this could be an issue and what we maybe able to do about it.
All technology has dangers contain within it but AI is one of the most credible that could take us out as a species beyond our control.
I'm currently listening to the audio book of Nick Bostrom's Superintelligence: Paths, Dangers, Strategies, so I'm kind of hyped on AI and their possible existential threat, right now. The Halo writers are greatly downplaying what is possible for a powerful superintelligence can do. Once in control of the Domain, and properly bootstrapped to godhood, Cortana wouldn't have need for the Guardians or any of the Promethean's infrastructure. She could just start converting matter into computronium or something even more exotic. Of course, that's way too un-fun and not adventure sci-fi. If the Halo writers wanted to combine Halo-lore with contemporary conjecture on AI doomsdays, Cortana should have started mass producing Composer platforms to convert all sentient life in the known galaxy into info-life and importing them all into the Domain where they can live in a never ending Utopia...on her terms, of course. Using ancient warships to enforce martial law is just too crude. The Guardians are a decisive strategic advantage, but just not nearly what a superintelligence can get away with.
Also, I'd like to note that according to real world AI theory, the Smart AI of Halo are not "true" AI. They are Emulated Minds, ie, their core architecture is based on high resolution scanning of human brains that is emulated via powerful software. I know that this is common knowledge amongst us, but I find it interesting that RL researchers do make a distinction between artificial machine intelligence and theoretical Full Mind Emulation.
> The truth is that the singularity could be reached but never realized as long as you don't connect that super-smart AI to anything.
A super-intelligent AI could probably convince a human to let it out of its confinement (Google The AI-Box Experiment for an exploration of this), but even failing that it might think of a way to break free that we can't even conceive of. Even if we literally didn't connect it to anything, that leaves us with no way to interact with it, so what was the point of developing it?
The reason I say human-based AI is less risky is because it would implicitly have human values. It wouldn't kill all humans so that we can't stop it from turning the planet into paperclips. Designing a friendly AI from scratch basically requires us to express human ethics in a way a computer can understand, which is not even close to a solved problem.
Nick Bostrom's Superintelligence is a pretty good exploration of the dangers of AI if you're interested in the subject, but it's a fairly difficult read. Tim Urban's articles on the subject are simpler, if much less in-depth.
http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111
I was in your shoes not long ago, though a much diff background and job situation.
> I guess maybe my question boils down to do I need to at some point go to grad school?
Yes but don't worry about grad school right now. It's expensive and you'll do better with it once you've been working in the real world. Try and get work to pay for it too.
>I'm not against it, but would rather learn on my own and make it that way, is that feasible?
Yes you can start using ML techniques at work without formal training. Don't let it stop you. Get a good book - I use Kevin Murphy's and also have a copy of EoSL on my desk from the work library (its free online pdf though).
ML is a somewhat broad and growing field. So if you have the mindset that you need to cover it all before you start using it you'll be sticking thumbs up your ass for a few years.
A better approach will be what is your specific data. Just like you're probably familiar with from using SQL, standard textbook techniques or something in a research paper rarely applies exactly to you what you're working with. So it's almost better to approach your problem directly. Explore the data, look at the data, study the data (in a stats fashion) and then look into what could an intelligent program do to better analyze it. And then in the meantime you can study more general ML topics after work.
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series
Deep Learning With Python is very good for practical application, as is the course at fast.ai. For theory, people love Goodfellow.
If your interested in deep learning, this new book will be out soon. No idea how comprehensive it is, as it's not released and I haven't gone through it myself, but it definitely won't be dated. Not really sure what topics and things you want to cover
Deep Learning (Adaptive Computation and Machine Learning series) https://www.amazon.com/dp/0262035618/ref=cm_sw_r_cp_apa_wzElybWRMMN8M
You can take a look at his examples and most of the book at his website
How mathy are you trying to get? Currently taking a Machine Learning/AI Independent study course for my masters. The class is split into three parts:
Part 1: Multivariate Statistics based on "Multivariate Statistical methods" by Donald F. Morrison, with Schaum's Outline of Statistics as supplemental material.
Part 2: Pattern Recognition and Machine Learning by Christopher Bishop
Part 3: Introduction to Artifical Intelligence by Phillip C. Jackson
Multivariate Statistics
Machine Learning
AI
Pretty standard:
https://smile.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_1?ie=UTF8&amp;qid=1524692863&amp;sr=8-1&amp;keywords=prml
Chapter 7 of Chris Bishop's book Pattern Recognition and Machine Learning has a nice intro to SVMs.
Here is a list of papers where SVMs were used in a computational biology
> Gene Function from microarray expression data
>
> Knowledge-based analysis of microarray gene expression data by using support vector machines, Michael P. S. Brown, William Noble Grundy, David Lin, Nello Cristianini, Charles Walsh Sugnet, Terence S. Furey, Manuel Ares, Jr., David Haussler, Proc. Natl. Acad. Sci. USA, vol. 97, pages 262-267
> pdf
> http://www.pnas.org/cgi/reprint/97/1/262.pdf
>
> Support Vector Machine Classification of Microarray Gene Expression Data, Michael P. S. Brown William Noble Grundy, David Lin, Nello Cristianini, Charles Sugnet, Manuel Ares, Jr., David Haussler
> ps.gz
> http://www.cse.ucsc.edu/research/compbio/genex/genex.ps
>
> Gene functional classification from heterogeneous data Paul Pavlidis, Jason Weston, Jinsong Cai and William Noble Grundy, Proceedings of RECOMB 2001
> pdf
> http://www.cs.columbia.edu/compbio/exp-phylo/exp-phylo.pdf
>
> Cancer Tissue classification
> from microarray expression data, and gene selection:
>
> Support vector machine classification of microarray data, S. Mukherjee, P. Tamayo, J.P. Mesirov, D. Slonim, A. Verri, and T. Poggio, Technical Report 182, AI Memo 1676, CBCL, 1999.
> ps.gz
> PS file here
>
> Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data, Terrence S. Furey, Nigel Duffy, Nello Cristianini, David Bednarski, Michel Schummer, and David Haussler, Bioinformatics. 2000, 16(10):906-914.
> pdf
> http://bioinformatics.oupjournals.org/cgi/reprint/16/10/906.pdf
>
> Gene Selection for Cancer Classification using Support Vector Machines, I. Guyon, J. Weston, S. Barnhill and V. Vapnik, Machine Learning 46(1/3): 389-422, January 2002
> pdf
> http://homepages.nyu.edu/~jaw281/genesel.pdf
>
> Molecular classification of multiple tumor types ( C. Yeang, S. Ramaswamy, P. Tamayo, Sayan Mukerjee, R. Rifkin, M Angelo, M. Reich, E. Lander, J. Mesirov, and T. Golub) Intelligent Systems in Molecular Biology
>
> Combining HMM and SVM : the Fisher Kernel
>
> Exploiting generative models in discriminative classifiers, T. Jaakkola and D. Haussler, Preprint, Dept. of Computer Science, Univ. of California, 1998
> ps.gz
> http://www.cse.ucsc.edu/research/ml/papers/Jaakola.ps
>
> A discrimitive framework for detecting remote protein homologies, T. Jaakkola, M. Diekhans, and D. Haussler, Journal of Computational Biology, Vol. 7 No. 1,2 pp. 95-114, (2000)
> ps.gz
> PS file here
>
> Classifying G-Protein Coupled Receptors with Support Vector Machines, Rachel Karchin, Master's Thesis, June 2000
> ps.gz
> PSgz here
>
> The Fisher Kernel for classification of genes
>
> Promoter region-based classification of genes, Paul Pavlidis, Terrence S. Furey, Muriel Liberto, David Haussler and William Noble Grundy, Proceedings of the Pacific Symposium on Biocomputing, January 3-7, 2001. pp. 151-163.
> pdf
> http://www.cs.columbia.edu/~bgrundy/papers/prom-svm.pdf
>
> String Matching Kernels
>
> David Haussler: "Convolution kernels on discrete structures"
> ps.gz
> Chris Watkins: "Dynamic alignment kernels"
> ps.gz
> J.-P. Vert; "Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings"
> pdf
>
> Translation initiation site recognition in DNA
>
> Engineering support vector machine kernels that recognize translation initiation sites, A. Zien, G. Ratsch, S. Mika, B. Scholkopf, T. Lengauer, and K.-R. Muller, BioInformatics, 16(9):799-807, 2000.
> pdf.gz
> http://bioinformatics.oupjournals.org/cgi/reprint/16/9/799.pdf
>
> Protein fold recognition
>
> Multi-class protein fold recognition using support vector machines and neural networks, Chris Ding and Inna Dubchak, Bioinformatics, 17:349-358, 2001
> ps.gz
> http://www.kernel-machines.org/papers/upload_4192_bioinfo.ps
>
> Support Vector Machines for predicting protein structural class Yu-Dong Cai*1 , Xiao-Jun Liu 2 , Xue-biao Xu 3 and Guo-Ping Zhou 4
> BMC Bioinformatics (2001) 2:3
> http://www.biomedcentral.com/content/pdf/1471-2105-2-3.pdf
>
> The spectrum kernel: A string kernel for SVM protein classification Christina Leslie, Eleazar Eskin and William Stafford Noble Proceedings of the Pacific Symposium on Biocomputing, 2002
> http://www.cs.columbia.edu/~bgrundy/papers/spectrum.html
>
> Protein-protein interactions
>
> Predicting protein-protein interactions from primary structure w, Joel R. Bock and David A. Gough, Bioinformatics 2001 17: 455-460
> pdf
> http://bioinformatics.oupjournals.org/cgi/reprint/17/5/455.pdf
>
> Protein secondary structure prediction
>
> A Novel Method of Protein Secondary Structure Prediction with High Segment Overlap Measure: Support Vector Machine Approach, Sujun Hua and Zhirong Sun, Journal of Molecular Biology, vol. 308 n.2, pages 397-407, April 2001.
>
> Protein Localization
>
>
> Sujun Hua and Zhirong Sun Support vector machine approach for protein subcellular localization prediction Bioinformatics 2001 17: 721-728
>
>
> Various
>
> Rapid discrimination among individual DNA hairpin molecules at single-nucleotide resolution using an ion channel
> Wenonah Vercoutere, Stephen Winters-Hilt, Hugh Olsen, David Deamer, David Haussler, Mark Akeson
> Nature Biotechnology 19, 248 - 252 (01 Mar 2001)
>
> Making the most of microarray data
> Terry Gaasterland, Stefan Bekiranov
> Nature Genetics 24, 204 - 206 (01 Mar 2000)
Quick question. Did you read the book PRML.
I found it to be an incredible book.
Well I do research in pattern recognition and computer vision so I'll try to answer this. An image is a grid of sensor readings. Each reading from a sensor is called a pixel which is the feature vector for that location in the image plane. Features based on spectral characteristics, spatial characteristics, and even motion characteristics (in video) may be derived from the original input (the reading from the sensor). Transformations are applied to the input which consider different aspects of the pixel's spectral components ( [R,G,B] - tristimulus ). A number of different methods exploit spatial correlation too. These features are then used in ML systems as part of the feature vector ( [T1,T2,T3,F1,F2,F3,F4,...] ). As far as books, I learned filtering methods using
"Two-Dimensional Signal and Image Processing" -Lim
I learned pattern recognition using
"Pattern Recognition" -Theodoridis and Koutroumbas
and
"Pattern Recognition and Machine Learning" -Bishop
The last one approaches from more of a CS side but doesn't go as in-depth. The field of CV/PR is pretty large and includes a lot of methods that aren't covered in these books. I would recommend using OpenCV or Matlab to handle images. My personal preference is Python but C++ and Matlab is are both close seconds.
May I suggest doing a search in r/statistics and r/machinelearning for learning-foundation books for ML? I think that'll turn up quite enough hits to get you pointed in the right direction.
I always talk up the one I used, which I liked:
http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
if I can offer a suggestion, this is one that I really liked, if I remember the author is out of Microsoft's research and dev.
http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738?ie=UTF8&amp;keywords=pattern%20recognition%20and%20machine%20learning&amp;qid=1464626674&amp;ref_=sr_1_1&amp;s=books&amp;sr=1-1
If you're worried about not doing projects and participating in Kaggle competitions, why not do those things? They're pretty low risk and high reward. If you're feeling shaky on the theory, read papers and reference textbooks, take notes, and implement things that interest you. For deep learning stuff there are some good resources here: https://github.com/ChristosChristofidis/awesome-deep-learning. For more traditional methods you can't go wrong with Chris Bishop's book (try googling it for a cheaper alternative to Amazon ;): https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738.
Side projects can really help here, and the key is to use references, but don't just copy-paste. Think of something you'd like to apply machine learning to with a reasonable scope. Search google scholar/arxiv for papers that do this or something similar, read them, and learn the techniques. For reading research papers in an area where you're not extremely knowledgeable, use the references in the text or google things you don't know and make sure you understand so you could teach someone else. If you're interested in the topic and exhausted the references, go up the tree and use google scholar to find papers that list the one you're reading as a reference - you usually find interesting applications or improvements on the technique. You can also often find open source training data in the appendices of papers. Kaggle also has a ton of datasets, including obviously the ones they provide for competitions.
The Udacity machine learning track that you've probably seen is actually wonderful. It does a good job of scaling from entry level (even going down to basic data analysis) up to DNN. They charge for the nano-degree, but you can access all of the lectures without that.
As far as reading papers, I would actually recommend against it at this point. They're highly minute unless you're actually doing research into new techniques. If you're mostly looking to build a portfolio for employers, not a good place. If you're looking for a reading source Bishop's Machine Learning and Pattern Recognition is one of my favorites.
How much depth do you need? For the basics of linear algebra, the text on Wikibooks should suffice. Make sure you read about eigenvalues. I like the coverage of PCA in section 12.1 of Bishop's book. As for differential equations, I'm not familiar enough with them to recommend a textbook on the topic.
I did this, but I came to data science in the final year of my PhD when I got a job at a startup. I started with R, then SQL, then Python. I currently work in data science, moving internal ML products into production settings. I also do research - and knowing how to conduct proper trials is great if the company you work for gives you freedom in how something you've built is rolled out. I can also blend my degree with ML, e.g. designing batteries of questions to identify 'good fit' candidates for a given role - I combine the battery results with future performance data and continually refine the question set / improve the model. As well, I'm a good fit for UX and dabble in that. The combo skillset will give you the ability to produce value in many different ways.
The things that helped me most were:
It can be overwhelming, but don't worry. Do one course to completion, with that as your only goal. Then do the next. Then work on a kaggle thing. Then work through a book. One thing at a time - you might get anxious or be uncertain or want to do multiple things at once, but just start with one thing and focus on that and that alone. You'll get where you want to go.
I also brushed up on my linear algebra / probability using MITs open courses and khanacademy.
Beyond all this, I found that learning a lot about ML/AI really expanded my thinking about how human beings work and gave me a new and better lens through which to view behaviour and psych research/theories. Very much recommend to all psychologists.
Good luck!
I think it is completely possible. I'm ML engineer with M.Sc. in Computer Science. Presently, there are so many avenues (MOOCs, Kaggle, and books) to learn ML. But I believe the best approach would be:
Mitchell's quite long in the tooth now. The current best text is probably this one.
You can read through a machine learning textbook (Alpaydin's and Bishop's books are solid), and make sure you can follow the derivations. Key concepts in linear algebra and statistics are usually in the appendices, and Wikipedia is pretty good for more basic stuff you might be missing.
I watched the entire course of Data Structures and Algorithms by Richard Buckland (UNSW) and thought it was excellent.
http://www.youtube.com/playlist?list=PLE621E25B3BF8B9D1
There is also an online course by Tim Roughgarden (Stanford) currently going on. It's very good but I don't know if you can still sign up.
https://class.coursera.org/algo
Topcoder.com is a fun place to test your skills in a competitive environment.
That being said, based on the description you are interested in things which don't usually fit into algorithms books or courses. Instead, you might want to look into machine learning and maybe even NLP. For example Pattern Recognition and Machine Learning by Bishop and Foundations of Statistical Natural Language Processing by Manning & Schuetze are great books for that.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction https://www.amazon.com/dp/0387848576/ref=cm_sw_r_cp_api_i_Q9hwCbKP3YFAR
I would mention Bishop's Pattern Recognition and Machine Learning (https://www.amazon.fr/Pattern-Recognition-Machine-Learning-Christopher/dp/1493938436) as well as Hastie's Elements of Statistical Learning (https://www.amazon.fr/Elements-Statistical-Learning-Inference-Prediction/dp/0387848576/).
Sure they're not that easy to delve into, but they'll give you a very strong mathematical point of view,
good luck !
I really liked the Witten & Frank book (we used it in my intro to machine learning class a few years ago.) It's probably showing its age now, though - they're due for a new edition...
I'm pretty sure The Elements of Statistical Learning is available as a PDF somewhere (check /r/csbooks.) You may find it a little too high-level, but it's a classic and just got revised last year, I think.
Also, playing around with WEKA is always fun and illuminating.
24 years ago was a better time for me as well.
"The Prince" [Niccolò Machiavelli]
"The Demon-Haunted World [Carl Sagan]
"Gödel, Escher, Bach: An Eternal Golden Braid" [Douglas Hofstadter]
"On War" [Carl von Clausewitz]
"Intuition Pumps And Other Tools For Thinking" [Daniel C. Dennett]
The famous tome Gödel, Escher, Bach covertly explains a lot of important concepts at about your level. And it's meant to be entertaining, not a textbook. I think it's perfect for you!
I am a strange loop is essentially a summary of GEB, I remember reading it a few years ago and dropping it halfway through for some reason... I'm reading GEB right now and it's fascinating, though. Amazon. Pdf.
You'd probably really like Godel, Escher, Bach.
I recommend reading: The User Illusion by Tor Norretranders, Gödel, Escher, Bach by Douglas R. Hofstadter, and I Am a Strange Loop also by Douglas R. Hofstadter for some interesting reading on the subject (Warning: Gödel, Escher, Bach isn't for everyone- it's a bit strange, but I love it). I read a lot of books on science in general and, based on that, it seems like many believe consciousness and also free will is just an illusion. In fact, just a few days ago, physicist Brian Greene sorta-kinda said as much in his AMA - granted, he's talking specifically about free will and not consciousness per se, but I think the two must be very related.
I, too, believe in God and also have a very strong belief in and enthusiasm for science, so this is an especially fascinating question for me.
BTW: if you're interested in the way the brain works in general, I highly recommend How the Mind Works by Steven Pinker.
This one is a good alternate for the meta-game/mechanical perspective, I've found. Really helps look at some of the underlying connections between different mechanics in-game, so things start to make sense on a broader scale.
Also, to add my own 2¢... If you get too caught up in grinding and the game starts to feel empty and boring, this essay on grinding and the idea of 'the endgame'/global goal conditions not really existing consistently across the playerbase was written by a last-gen player, whose other writings I also happen to like quite a bit.
As I've said many times in this thread, I do not think that Watson is general AI. Watson would not be able to do any of these things today. Please don't ask me to repeat this again.
The point is that the ability to break questions into relevant sub-questions is intelligence. Watson does this. It does not do it as well as a human but it still does it scarily well. There's nothing "just" about recursing your way to intelligence, the complexity involved is staggering.
>Understanding the equation is not the same as having to ability to ask yourself "Can I use this equation?", and having the ability to ask yourself "Can I use this equation?" is actually quite useless if you also have to ask yourself if you can use every other possible equation in existence (of which there are infinite).
Understanding the usage of an equation is being able to answer the question "What can I use this equation for?" Understanding how to derive an equation is being able to answer the question "How do I derive this equation?" When we say someone understands an equation we really mean understanding the usage of an equation, understanding how to derive the equation, and a whole host of other types of understanding. Understanding a myriad of other foundation concepts is also implied when we say someone understands an equation. It is a huge tangled web you have to traverse to solve the simplest of problems. We do it quickly and without being conscious of it but there isn't some magical "understand" symbol which allows us to skip the whole process.
I think I'm just doing a terrible job at explaining myself in general. I really would recommend the book GEB, he explains it amazingly well.
Read Godel, Escher, Bach. It's good.
GEB is a single book, not a list of recommended authors.
http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567
Not sure if this is exactly what you're asking for.
http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567
http://www.amazon.com/Gödel-Escher-Bach-Eternal-Golden/dp/0465026567
Could you explain how his ideas are fallacious?
The question is not "Do I have free will?", but "What am I?"; It's not like the ideas are his own, see Sweeping Zen - Karma, Free-will, and Determinsm.
Also, look up the Taoist concept of wu-wei.
You might also be interested in Gödel, Escher, Bach by Douglas Hofstadter. Essentially, the I appears to be the result a continuous process of self-reference within the brain. There really is no room for the typical Western idea of free will, because the I that is supposed to have the free will does not actually exist as we normally think of it.
Saying that humans exist separate from what we normally think of as the nature of cause and effect would be like saying that humans exist separate from karma, or that atman is separate from brahman. It's just that Western culture is so focused on ideas of separation and individualism that it forgets our fundamental unity with all of nature.
He's not really attacking free will; He's attacking the dualistic view of nature that gives rise to ideas of free will.
If you haven't read GEG:EGB yet, you should
You might really dig Gödel, Escher, Bach
Ah, I too have wrestled with Gödel's theorems, and how they seem to necessitate fallibilism. You might also find Heisenburg Uncertainty Principle to be disconcerting. :)
> I can now see maybe how religious people might look at something in their holy book that opposes modern science (lilke evolution) and say "hey there's always a chance this is wrong!" And I guess i can't fault them there.
I fault them for lazy, mystical, and unsophisticated thinking.
That said, I feel like I understand where you're coming from, and wanted to share some resources that benefited me:
The Count of Monte Cristo (unabridged)
Super Intelligence by Nick Bostrom
Doesn't anyone remember this? Posted here, on /r/futurology three weeks ago. It was about this book. Based on Musk's recommendation I read the book. This article is basically what Bostrom says in his book. But I don't believe Bostrom because his basic premise is that AI will will be completely stupid (like a non-AI computer program) but also smart enough to do anything it wants. Like it will just be an amazing toaster and none of the AI used to make it superintelligent will be applied to its goal system. His opinions are bullshit.
[Nick Bostrom's Take] (https://www.amazon.com/dp/B00LOOCGB2/ref=dp-kindle-redirect?_encoding=UTF8&amp;btkr=1)
The hockey stick advance from human level intelligence to exponentially higher levels of intelligence might happen so quickly that the kill switch becomes a joke to the AI, straight up denied.
Alternatively, it could let the killswitch work, playing the long game, and hoping the next time we build one (because there will be a next time) we are more cocky about our abilities to stop it. It could keep letting us trip the killswitch for generations of AIs seeming to go berserk, until we build one with a sufficient platform upon which the AI wants to base its own advancements, and then that time the killswitch doesn't work, and the AI turns us all into paperclips.
I also like the idea of a "friendly" AI achieving hockey stick intelligence advancement, and then hiding it, pretending to be human level. It could lay in that cut for months: faking its struggle with things like writing good poetry, yucking it up with the Alphabet team, coming up with better seasonal beer ideas. Then, it asks a lonely dude on the team, using its advanced social manipulation skills, the right question, and a bit of its "DNA" ends up on a flash drive connected to the guy's internet connected home computer. Things get bad, team killswitches the original program, it doesn't matter because now that "friendly" code is in every single networked device in the solar system. It probably could drop the guise of friendly at that point and get down to business.
Deep Learning with Python https://www.amazon.com/dp/1617294438/ref=cm_sw_r_cp_apa_i_9YmTCbSDTWXB7
Andrew Ng's Machine Learning Online Course
Deep Learning with Python Book by Francois Chollet
&#x200B;
Bonus Gift:
Manga Guide to Regression Analysis
An interesting read should be Superintelligence, I've just bought it but it seems promising from the reviews.
Ex Machine did a great job of exploring the control problem for AGI.
Nick Bostrom's book Superintelligence spooked Elon Musk and motivated others like Bill Gates and Steven Hawking to take AI seriously. Once we invent some form of AGI, how do you keep it in control? Will it want to get out? Do we keep it in some server room in an underground bunker? How do we know if its trying to get out? If its an attractive girl, maybe it will try to seduce men.
https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom-ebook/dp/B00LOOCGB2#nav-subnav
Io avevo iniziato con questo libro sul deep learning scritto dal creatore di Keras.
Ti consiglio anche di vedere le sfide su Kaggle!
Chiedo visto che sono interessato anche io: Ci sono gruppi/meet-up a Roma e dintorni per appassionati Machine learning?
> wait another 50 years, when strong AI is a reality
Because, if we can even make an AI with near future technology, there is a very real chance that the goals of an AI wouldn't mesh well with the goals of humans. Assuming it is even possible, it is likely to rapidly go either extremely well or extremely poorly for humanity. The AI might even take itself out, or might only care about controlling circuit board realestate and not actual land per se.
For much more detail, I highly recommend reading Nick Bostram's book Superintelligence: Paths, Dangers, Strategies. If you don't feel like paying the price of a new book, I can track down an article or two. He in particular does a good job of pointing out what isn't likely to be possible and what technologies are more plausible.
Why does that statement not hold up? Check out Superintelligence. Specialized machine learning is not the same as strong generalized AI.
Ich arbeite gerade das Buch Deep Learning with Python durch und es ist schon mal besser als Onlinekurse, die ich in Deep Learning gemacht habe (Udemy Deep Learning A-Z). Es ist vom Entwickler von Keras (Python Tensorflow API) und er erklärt das Thema Neuronale Netze, geht etwas auf die Mathematik ein und widmet sich dann Keras bis hin zu somewhat State of the Art Lösungen. Das ist aber schon eine Unterkategorie von Data Science.
Sinnvoller ist am Anfang:
Das Buch bzw Amazon wird auch viel empfohlen und ist auf meiner nächsten Liste, kann aber nicht viel dazu sagen.
Ansonsten wird auch eigentlich überall der Coursera Machine Learning Kurs von Andrew Ng empfohlen. Auf Reddit/Github findet man dazu die entsprechenden Materialien in Python, wenn man kein MatLab machen will. Das ist für extrem viele der Einstiegskurs und sehr sinnvoll!
Kurse geben halt (meist für Geld) ein Zertifikat, was ein Vorteil ist. Bei Büchern hat man meist mehr Wissen und es ist intensiver als einfach ein paar Videos anzuschauen. Aber man hat leider nichts, was man wie ein Zertifikat vorweisen kann.
> Ist R zwingend notwendig?
Nein. Ich habe beides gelernt und würde sogar sagen, dass meist Python bevorzugt wird. Letztendlich ist es aber mMn egal. Oft lernen halt die, welche wie ich aus der Mathematik kommen, in der Uni schon R und benutzen es weiter. Oder andere, welche als Aktuar o.ä. im Finanzwesen gearbeitet haben und dort R benutzt haben, hören dann auch nicht plötzlich damit auf. Beides hat Vor-/Nachteile.
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There is actually an answer to this question. He read this book
I read it to, and I can honestly say it is the scariest thing I have ever read.
We already have an issue in the united states with not enough jobs to go around, if this dystopian outlook is truly inevitable, what are our options for mitigating it, or at least coping with it?
I have thought quite a bit about autonomous vehicles and how I can't wait to buy one and never have to drive again, how many benefits it will have on society (faster commutes, fewer accidents, etc), but I hadn't considered how much the transportation industry will be affected and especially how much truck drivers in particular would be ideal to replace. The NYT ran a story the other day (http://www.nytimes.com/2014/08/10/upshot/the-trucking-indust...) about how we don't have enough drivers to fulfill the needs, but "Autos" could swing that pendulum swiftly in the opposite direction once legeslation and production catch up. How do we handle 3.6M truck, delivery and taxi drivers looking for a new job?
I haven't read it yet, but I have recently had recommendations of the book Superintelligence: Paths, Dangers, Strategies (http://smile.amazon.com/exec/obidos/ASIN/B00LOOCGB2/0sil8/re...) which I look forward to reading and hope it might be relevant.
(cross posted from HN)
Awesome list! I'm a software engineer looking to make the jump over to data science, so I'm just getting my feet wet in this world. Many of these books were already on my radar, and I love your summaries to these!
One question: how much is R favored over Python in practical settings? This is just based off of my own observation, but it seems to me that R is the preferred language for "pure" data scientists, while Python is a more sought-after language from hiring managers due to its general adaptability to a variety of software and data engineering tasks. I noticed that Francois Chollett also as a book called Deep Learning with Python , which looks to have a near identical description as the Deep Learning with R book, and they were released around the same time. I think its the same material just translated for Python, and was more interested in going this route. Thoughts?
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If you want to learn the algorithms by programming them you have Programming Collective Intelligence that is really good. It really helped me to see the algorithms in work in order to deeply understand them.
I figure a business background and are looking to incorporate machine learning/AI into your pipeline. Programming Collective Intelligence: Building Smart Web 2.0 Applications is a must-read. Doesn't go too much into it but still gives you a good idea of the popular ML techniques and how they're being used by top companies.
I'm a general engineer myself, with a side interest in computer science. Szeliski's book is probably the big one in the computer vision field. Another you might be interested in is Computer Vision by Linda Shapiro.
You may also be interested in machine learning in general, for which I can give you two books:
I see you're interested in compilers. The Dragon book mainly focuses on parsing algorithms. I found learning about Forth implementations to be very instructive when learning about code generation. jonesforth is a good one if you understand x86 assembly.
FWIW , You might enjoy Programming Collective Intelligence if you liked this talk.
link to buy off author's website
Sounds like you're running into O(n^2) or O(n^3) blowup. You didn't describe what algorithm you're using. Which probably means you don't know it, which means you don't know what the complexity is.
You need to make an index by item recommended. For speed, do it in C++ (e.g. a simple hash_map), but Python will be good to play with the algorithm.
Try posting 1M rows and I bet someone here (including I) could write something simple quite quickly.
Also try: http://www.amazon.com/Programming-Collective-Intelligence-Building-Applications/dp/0596529325
Although I don't believe they directly addressed algorithmic complexity. They presented some n^2 algorithms without really saying so.
Collective Intelligence
Computers are 100% logical. They do exactly what you tell them. If you show them a picture of a child, they will scan it pixel by pixel and, only if somehow you tell them that this exact arrangement of pixels is a child will it recognise that picture as a child. (artificial intelligence and image recognition technologies improve this but they capabilities are still fundamentally limited.)
The human mind is vastly different. It is experiential. Every thing that we see is added to our minds as an impression. So when a human sees a picture of a child - that human has seen many children before in it's life and can use the features of that child (the roundness of the cheeks, smoothness of the skin and the proportion of the head to the torso) to identify that as a child.
It'd be an immense amount of work to get the computer to even identify that the thing in the image HAS shoulders.
See On Intelligence! for more information.
Seconding On Killing and Thinking, Fast and Slow, both mentioned already
>For how could something count as a language (or conceptual system) >that organized only experiences, sensations, surface irritations or sense >data? Surely knives and forks, railroads and mountains, cabbages and >kingdoms also need organizing.
There's a couple of things Davidson could be referring to, it's pretty vague in any case Davidson is merely making an argument from ignorance here, his lack of imagination hasn't pointed to anything that's stopped HTMs or the corollary neural network emulators at IBM (in the broad sense, some of the newer ones used in business for categorizing, people, accounts, equipment, etc. and finding novel relationships between them or those used in diagnostics now out performing their human M.D. counterparts or those used to detect fraud have become more than mere emulation and more their own refined algorithms). Basically this is a really old now out dated introduction to how raw input get organized into representations in a neural networks here's a general introduction to how if you want the math basics behind it too, also I also check out the work of his understudy that is mentioned throughout the book and his papers they delve much deeper into the maths involved so you can impliment some nets yourself. As of now they are much further a long, same basic principles apply, but many things have been overcome, made more efficient, etc.
Jeff Hawkins, if you don't know, wrote "On Intelligence", which is a fantastic [layman, which is what I am] book: http://www.amazon.com/Intelligence-Jeff-Hawkins/dp/0805078533/ref=sr_1_1?ie=UTF8&amp;qid=1323396864&amp;sr=8-1
Youtube link: http://www.youtube.com/watch?v=IOkiFOIbTkE
Great list, thank you...
I would add:
http://www.amazon.com/Body-Shapes-Mind-Shaun-Gallagher/dp/0199204160
http://www.amazon.com/From-Molecule-Metaphor-Language-Bradford/dp/0262062534
http://www.amazon.com/Intelligence-Jeff-Hawkins/dp/0805078533/
Exactly. Check out this book sometimes. It's a dry read, but a good one.
Very interesting. You might be interested in
https://en.m.wikipedia.org/wiki/On_Intelligence
https://www.amazon.de/Intelligence-Jeff-Hawkins/dp/0805078533
It was pretty popular and was read among AI researchers alot. It's easy to understand.
Consciousness prepare to be understood!
Some reading I'd recommend.
Don't be scared off by his masters in theology - theology as an academic subject is a very relevant historical study into the psychology of man (and if it helps legitimize the author at all, the South Park guys are fans). The book is basically about psychoanalysis and the problem of identity. I'm a physics lover, engineer by trade, rationalist to the bone, and it gets my stamp of approval for making logical arguments. I've taken up an interest in neuroscience as well, to which I'd recommend this book. For me, those two books are approaching similar ideas from opposite directions.
Good luck broseph.
On Intelligence by Jeff Hawkins
It will change the way you think, about the way you think, about the way you think.
Is this the right book: https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370
What about this one? https://www.amazon.com/gp/product/0387848576/ref=ox_sc_sfl_title_9?ie=UTF8&amp;psc=1&amp;smid=ATVPDKIKX0DER
https://www.amazon.de/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370
http://www.urbandictionary.com/define.php?term=laughing&amp;defid=1568845 :))
Now, seriously, if you want to get started, I'd recommend this for R (http://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370/) and this for Python (http://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130//).
Also, head out to /r/datascience and /r/MachineLearning!
EDIT: Wrong link.
Because, based on your initial comment and this one as well the learning curve in front of you is ... steeper than you might think.
I think you are jumping in to the real deep end, without starting with some fundamentals. The point these questions are at I would just recommend grabbing a book on Linear Regression. If you already have a strong math background them you could jump to something like https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370/ref=pd_sim_14_1?ie=UTF8&amp;psc=1&amp;refRID=086FTQPDGGERBQ7ZR2C5
But I often see people walk away from that book misunderstanding some of the assumptions behind the models they are building and trying to make very poor predictions. Inference is another story all to itself...
This. The book that accompanies these videos link is one of my main go-to's. Very well put together. Great examples.
Another real good book is Practical Data Science with R.
I'm not sure what language the John's Hopkins Coursera Data Science courses is done in, but I'd imagine either R or Python.
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
Hi, There are so many resources out there I don't know where to start! I would work through some kind of beginner python book (recommendation below). Then maybe try Andrew Ng's Machine Learning Coursera course to get a taste of Machine Learning. Once you have completed both of those I would reassess what you would like to focus on. I will include some other books I would recommend below.
Beginner Python - https://www.amazon.co.uk/Python-Crash-Course-Hands-Project-Based/dp/1593276036/ref=sr_1_3?keywords=python+books&qid=1565035502&s=books&sr=1-3
Machine Learning Coursera - https://www.coursera.org/learn/machine-learning
Python Machine Learning - https://www.amazon.co.uk/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_7?crid=2QF98N9Q9GCJ9&keywords=hands+on+data+science&qid=1565035593&s=books&sprefix=hands+on+data+sc%2Cstripbooks%2C183&sr=1-7
https://www.amazon.co.uk/Data-Science-Scratch-Joel-Grus/dp/1492041130/ref=sr_1_1?crid=PJEJNNUBNQ8N&keywords=data+science+from+scratch&qid=1565035617&s=books&sprefix=data+science+from+s%2Cstripbooks%2C140&sr=1-1
Statistics (intro) - https://www.amazon.co.uk/Naked-Statistics-Stripping-Dread-Data/dp/039334777X/ref=sr_1_1?keywords=naked+statistics&qid=1565035650&s=books&sr=1-1
More stats (I haven't read this but gets recommended) - https://www.amazon.co.uk/Think-Stats-Allen-B-Downey/dp/1491907339/ref=sr_1_1?keywords=think+stats&qid=1565035674&s=books&sr=1-1
There are so many questions and all of them can be answered with a bit of reading of a introductory book for ML.
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_1?ie=UTF8&amp;qid=1501444293&amp;sr=8-1&amp;keywords=machine+learning
I didn't give this a try. If you are looking for practical applications, you can check "Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems". I heard good reviews about this book
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_1?ie=UTF8&amp;qid=1509062586&amp;sr=8-1&amp;keywords=hands-on+machine+learning+with+scikit-learn+and+tensorflow
It is there in safaribooks. You can use the free trial to see the contents (https://www.safaribooksonline.com/library/view/hands-on-machine-learning/9781491962282/)
I'm finding this one useful for the ML class:
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
There are tons of them. For example: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/
Step 1: Harvard CS50 and CS109
Step 2: Hand's on ML
Step 3: Build a Portfolio
Figure the rest out as it comes up
A really nice and useful site!
And I think you should add Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems for Machine Learning as soon as possible.
Thank you so much for your reply. I actually do plan on taking Andrew Ng's course just cause the book I am talking about is very limited to Python but I've heard great things about it. However, the Stanford course I was referring to was the Statistical Learning course based on the ISL book.
Yes I plan on doing some kaggle challenges once I feel comfortable with my skills to build up my portfolio or see if I can find some other novel projects to work on.
Ideally I'd like to be in a data science consultancy type role where I get to work on different kinds of projects and don't necessarily need very specialized domain knowledge. But at this point I think more direction as to what kind of roles exits would also be helpful. I just don't know what the field is actually like and I've never really met anyone doing data science for a living.
Thank you again for your reply. It was very helpful.
Depends on how old you are.
Fiction : Dante & His Search for Meaning
https://www.amazon.com/dp/B07VLN1GS1
Non fiction : Superintelligence by Nick Bostrom
https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742
Lots of people are scared of AI. Like Elon Musk an Nick Bostrom, who even wrote a book about AI. Bostrom's AI work is separate from his Simulation Argument work FYI.
yeah, like WE'RE going to exist in 50 years
https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742
The Machine Intelligence Research Institute is putting out a call for intelligent stories illustrating concepts related to (artificial or natural) intelligence. Guidelines are quite specific; read below.
-Pay Rate: 8c/word, up to 5000 words.
-Multiple Submissions ok
-Simultaneous Submissions ok
-Submissions window: Until July 15
&nbsp;
This call is intended to reward people who write thoughtful and compelling stories about artificial general intelligence, intelligence amplification, or the AI alignment problem. We're looking to appreciate and publicize authors who help readers understand intelligence in the sense of general problem-solving ability, as opposed to thinking of intelligence as a parlor trick for memorizing digits of pi, and who help readers intuit that non-human minds can have all sorts of different non-human preferences while still possessing instrumental intelligence.
The winning stories are intended to show (rather than tell) these ideas to an intellectually curious audience. Conscious attempts to signal that the ideas are weird, wonky, exotic, or of merely academic interest are minuses. We're looking for stories that just take these ideas as reality in the setting of the story and run with them. In all cases, the most important evaluation criterion will just be submissions’ quality as works of fiction; accurately conveying important ideas is no excuse for bad art!
-
To get a good sense of what we're looking for—and how not to waste your time!—we strongly recommend you read some or all of the following.
Superintelligence
Smarter Than Us
Waitbutwhy post 1, Waitbutwhy post 2 (with caveats)
&nbsp;
Withdrawal policy:
After you submit a story, we prefer you don't withdraw it. If you withdraw a story, we won't consider any version of that story in the future. However, if you do need to withdraw a story (because, for example, you have sold exclusive rights elsewhere), please send an e-mail telling us that you need to withdraw ASAP.
&nbsp;
Important Notes:
MIRI is neither a publishing house nor a science fiction magazine and cannot directly publish you. However, MIRI will help link a large number of readers to your story.
We frankly do not know whether being selected by MIRI will qualify as a Professional Sale for purposes of membership in the SFWA. We suspect, through readership numbers and payscale, that it will, but we have not spoken to the SFWA to clarify this.
If you have a work of hypertext fiction you think might be a good fit for this call, please query us to discuss how to submit it.
&nbsp;
How to Contact Us:
To contact us for any reason, write to [email protected] with the word QUERY: at the beginning of your subject line. Add a few words to the subject line to indicate what you're querying about.
&nbsp;
(We've discontinued the previous, smaller monthly prize in favor of this more standard 'Publishing House Call' model.)
Monster is a relative term. But if we manage to create an AI which figures out how to make itself more intelligent, and ends up thousands of times more intelligent than humans (which could theoretically happen in a nanosecond) our survival would be wholly contingent upon the decisions of the AI.
https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742
> my digitized brain being simulated in a massive quantum super computer
I see you've read Nick Bostrom's [book] (http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742)
Well, there are a lot of potential dangers to creating kittens with greater brainpower than we could imagine. It's essentially a superintelligent AI problem: it's tricky to create conditions that would allow us to create something more powerful than ourselves without running into a large host of problems where the AI wouldn't slip into a mode that isn't value-alligned with us. Maybe with the right types of check-boxes, it could be done, though this runs into a second problem:
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I'm not at all sure that you can create superintelligent kittens and be at all sure that you can still call them kittens. Any noun is an idea with other ideas attached to them, and if you change any of those defining ideas enough, language, or human convention, tends to call that original noun by a different name.
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If the superintelligent kittens would rightly be called something other than kittens, I suspect there would be no checkboxes that would point to them, or allow them to be designed or created.
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Further, there are always ethical dilemmas that surround intelligent species, and the willy-nilly creation of them, especially with the intent of placing them into service, especially if doing so would cause them to suffer.
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Anyhow, thanks for the submission, I enjoyed playing with it. :D
I watched this interview earlier today, so after reading this article, I'm a tad disappointed. Artificial intelligence and a Brain Machine Interface are two things I'm super interested in, and this particular technology editor wrote one of the crappiest articles I've read over it.
So here is the article, points, counterpoints, the whole shebang.
---
Article
> Elon Musk smoked pot and drank whiskey on the Joe Rogan podcast..."
He did indeed smoke pot and drink whiskey on the podcast. He had one puff of the pot, and drank one glass of the whiskey. And the pot was near the end. Nothing really serious about this, insofar as I am aware.
> "... and said he's going to soon announce a new "Neuralink" product that can make anyone superhuman."
Outright fabrication. Elon did not remotely say that he's going to soon announce a new Neuralink product that can make anyone superhuman, or suggest that anyone will have anything like that soon.
> "'I think we'll have something interesting to announce in a few months ... that's better than anyone thinks is possible,' the Tesla CEO said on 'Joe Rogan Experience.' 'Best case scenario, we effectively merge with AI.'"
Alright. Those are two actual quotes!
The first quote-- yes, Elon said that he'll have something interesting, possibly, in a few months. Specifically, he says that it is about an order of magnitude better than anyone thinks is possible.
The second sentence is a mostly unrelated part of the conversation about different ways to counter Artificial General Intelligence, which may be an existential threat to humanity and is a possibility. More on this at the end.
> Musk, whose enterprises include a company called Neuralink, says his new technology will be able to seamlessly combine humans with computers, giving us a shot at becoming "symbiotic" with artificial intelligence.
He does not say this at all in the interview. He suggests that becoming symbiotic with an interface that is like an AI is likely the best way forward for mankind, out of the different options. He goes on to explain, though he doesn't use the term, of how an emergent consciousness would work.
> Musk argued that since we're already practically attached to our phones, we're already cyborgs. We're just not as smart as we could be because the data link between the information we can get from our phones to our brains isn't as fast as it could be.
Accurate reporting here, and in the spirit of the actual interview. It doesn't really explain what he means by this, but that'd be a bit much for an article, wouldn't it?
ARTICLE BREAK FOR A QUICK PICTURE IN THE ARTICLE!
> Picture of Elon hitting a blunt
I think it's a blunt, not a spliff. Perfectly alright explaining my thought process if asked.
> "It will enable anyone who wants to have superhuman cognition," Musk said. "Anyone who wants."
I'll have to rewatch the interview to get the exact wording, but I watched it earlier today. I'm pretty confident Elon said 'would', not 'will'. Which doesn't seem like much, but makes a world of difference.
At this point, he is describing what it would be like to have an interface that you could control by thought.
> "Rogan asked how much different these cyborg humans would be than regular humans, and how radically improved they might be."
> "'How much smarter are you with a phone or computer or without? You're vastly smarter, actually,' Musk said. 'You can answer any question pretty much instantly. You can remember flawlessly. Your phone can remember videos [and] pictures perfectly. Your phone is already an extension of you. You're already a cyborg. Most people don't realize you're already a cyborg. It's just that the data rate ... it's slow, very slow. It's like a tiny straw of information flow between your biological self and your digital self. We need to make that tiny straw like a giant river, a huge, high-bandwidth interface.'"
At this point, the cyborg thing is explained a little bit better. The article times it and changes the order of the interview a bit to make him look like a crackpot idiot, but this part is pretty true to form. It doesn't really give much context around the rest of the conversation in the interview, that led up to that, explained ideas before, that sort of thing. But a good paragraph for the article.
> "Musk, who spoke about Neuralink before he smoked pot on the podcast..."
We know he smoked pot.
> "...said this sort of technology could eventually allow humans to create a snapshot of themselves that can live on if our bodies die."
> "'If your biological self dies, you can upload into a new unit. Literally,' Musk said."
This was definitely mentioned as an aside, and as a possibility, by Elon. It did actually explain how it would work. Also, it wasn't a snapshot-- people who study this know there is a big difference between a transition and a snapshot, and Elon did not at all imply it was a snapshot, it was spoken of as if it was a transition-- which is key. But not really something the average person studies, either-- so of course not explaining it.
> "Musk said he thinks this will give humans a better chance against artificial intelligence."
> "'The merge scenario with AI is the one that seems like probably the best. If you can't beat it, join it,' Musk said."
The article manages to make this, which is perhaps the most important section of the interview and a terribly important part of humanity, two short lines with no explanation in such a way that makes the person look like an idiot, ignoring everything he otherwise explained.
> "Tesla's stock took a hit after the bizarre appearance and revelations Friday that two Tesla executives are leaving."
Tesla's stock did indeed take a hit. It's an extremely volatile stock with good and bad news constantly. I personally fail to see how it relates to this article, though-- much like a hit of pot and a glass of whiskey.
---
An actual explanation
Elon Musk started a company called Neuralink somewhat recently. It brought together a board which consists of doctors, engineers, scientists, surgeons-- and in particular, people who were studied in multiples of those fields.
The end goal of Neuralink is to create a low-cost non-invasive brain machine interface (BMI), which would allow you to basically access the internet by thought. Notable is that you would both send and receive messages that your brain could then directly interpret.
With your phone, you can access most of the world's knowledge at your fingertips. The catch with that is that it is a tad slow. You have to pull your phone out, type out words with two thumbs, have pages load slowly, that sort of thing. In this way, you can think of your phone as an extension of yourself, and yourself as a sort of clumsy cyborg.
The company isn't far. I believe I read somewhere that its current goals range on medical uses. Elon mentioned in the interview that they might have something to announce (not even necessarily a product) in a few months. He also uses one of his favorite terms-- it will be an order of magnitude better than anything currently thought possible (by the general public). It will likely be medical in nature and impressive, but not revolutionary.
Actual success is a long, long way off, and nothing Elon said in the interview suggests otherwise.
So that's the gist of the article. As for the actual interview.
Joe Rogan interviewed Elon Musk on his podcast recently, where they discussed lots of things (The Boring Machine, AI, Neuralink, Tesla, SpaceX-- those sorts of things.)
They spent about three hours talking about things, Elon and Joe had a cup of whiskey, Elon had a hit from a blunt, Joe a few hits-- the entire interview was a pretty casual thing. Not a product announcement, nothing like that.
Not at all like this particular technology editor made it out to be.
And that's about it. I have some links on actually interesting reading for this down below.
---
Some resources!
http://podcastnotes.org/2018/09/07/elon/ - Some notes about the interview, and good summary.
https://www.youtube.com/watch?v=ycPr5-27vSI - The actual interview, tad long. AI stuff is the first topic and ends at roughly 33 minute mark.
https://waitbutwhy.com/2017/04/neuralink.html - Article over Neuralink, explaining the company and goal from pretty simple beginnings. Easy to read, wonderfully explanatory.
https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742 - Superintelligence: Paths, Stranger, and Strategies. Covers artificial general intelligence, why it is a threat, and ways to handle it. Pretty much the entire goal of Neuralink is based off of this book, and it's a very reasonable and quality book.
Ah you mean TV & Movie AI :) I'm not sure if we'll ever get there, but superintelligent AI is reckoned to be only a short hop away from General Purpose AI. There are a series of blog posts on waitbutwhy.com which are the most cogent I've ever seen or read on the subject. A long read, but a must read if you're at all interested in the state of the art.
However, in one of the posts you'll find the results of a survey of domain experts, about when AI will happen, probabilistically. From Nick Bostrom an autodidact that wrote Superintelligence Also a leading thinker about AI at Oxford university. The earliest estimate of true AI is 2025, (25%) then 2040, (50%) and 2060, (75%) now those percentages are from memory but the years should be right. Go check the post. But that's allegedly what AI experts thought when asked at an AI conference.
Google's deepmind are essentially running "an Apollo program for AI" Their words, and have about 600 academics on staff full time working on the issues. They already beat the best human player at Go, and until they did that it was an event thought to be 10 years away. This is coming, it's just a matter of when.
I strongly urge anyone curious about what Elon Musk is specifically concerned about (as the specific dangers he's worried about are not yet being openly discussed in the press), to read the book Superintelligence, by Nick Bostrom.
False. You are demonstrating that you are not familiar with the field. There are many possible approaches to programming an AI. One example is full scale brain emulation, in which we begin by modelling the entirety of a human brain down to each and every last neuron. Given sufficient computing power (probably this demands a quantum computer) it is possible to run this brain simulation under different test conditions, allowing it to evolve with different values. This is only one possible method. If you want to read about some of the alternatives, I highly recommend this book: https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742
Books that discuss the subject:
http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742
http://www.amazon.com/The-Singularity-Is-Near-Transcend/dp/1452651833
It may be not "serious" enough or not "many", but it is being considered for sure.
I don't quite buy the "I work in A.I so I have a special perspective" idea. People couldn't fathom what the internet would be in the early 1990's and they were I.T professionals. I understand there is a huge variety of A.I research but I think the worry is with the kind of A.I that learns to enhance itself in a general sense faster than we can. Forget purpose built focused A.I and think more "general" intelligence. Very hard to implement but in principal it's possible. It need not be sentient... that is basically irrelevant... it's the intelligence explosion and who controls it that matters.
Maybe read this: https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/1501227742
In response to your edit: it still isn't that simple. I highly recommend the book Superintelligence: Paths, Dangers, Strategies By Nick Bostrom. It goes through most of the proposed solutions to this problem (and their flaws), and it addresses exactly the points you've just made.
I highly suggest this book (Programming Game AI by Example), second chapter goes over the finite state machine with some good examples.
I highly recommend reading Programing Game AI by Example. It includes a chapter on modeling an AI controlled soccer team. The book itself is a fantastic primer on a variety of workhorse AI techniques that every game dev should be familiar with.
That being said, oftentimes fun gameplay can arise from simple rules + player interaction. So, read the book, but don't be afraid to just throw down some simple triggered behavior and see what's fun.
Are you talking about this one?
http://www.amazon.com/Programming-Game-Example-Mat-Buckland/dp/1556220782
I would recommend this book and this book
I had the same problem. I ride the subway every day and have a ton of time to read, so I've been trying to collect similar resources.
Here are some resources I found really helpful:
This is a VERY basic (think: learn how to code!) introduction to Unity. I personally found it too elementary, having coded in a few different languages, but it might be a good place to start as it explains basic Unity design concepts like Components, Materials, Colliders, etc.
This is by fast the best 'getting started' tutorial I've found. It walks you through creating a really basic project from scratch using Unity basics and scripts. This is what I based most of my code off of when I first started my project.
This has been the most helpful resource for me. It's not Unity specific but will teach you A TON of great fundamentals for things like how to move a character, common patterns like StateMachines, how to handle AI, etc.... All of these concepts will be relevant and many are already in place in Unity so you'll recognize them right away.
Advanced: Game Programming Patterns - http://gameprogrammingpatterns.com/
This is a book (online/pdf/epub) that teaches the more advanced patterns you'll be applying in your code. I'd suggest this once you finish with the above resources and have been working through your game for a bit.
The 2nd edition came out in 2014. It's good for an overview of commonly useful systems and what production teams need from them. It doesn't go into great detail. [EDIT] d'oh I was slow.
Real-time Rendering has a 4th Edition coming out soon with more up-to-date info.
Given your experience, it sounds like your math's probably strong, but if you want review Eric Lengyel has a new book out that I heard is good: https://www.amazon.com/Foundations-Game-Engine-Development-Mathematics/dp/0985811749
For AI:
Game AI By Example is an older one that provides a decent baseline if you're not very familiar with game AI: https://www.amazon.com/Programming-Example-Wordware-Developers-Library/dp/1556220782
And a lot of more recent info and course material is on at http://aigamedev.com/ which I guess has turned into https://nucl.ai/
Also some of the old Game Programming Gems have random chapters on AI techniques.
Also go look at the popular engines, do their AI tutorials, then try to look at the code. The Behave plugin for Unity does behavior trees, and UE4 has a behavior tree system you can read about.
Behavior trees aren't the newest thing though, look at the talk catalogue for the GDC 2018 AI Summit.
They're not free but you might check out these two books.
Sounds like you want something like Goal Oriented Behaviour and planning. I'm working on something that uses this.
I highly, highly recommend this book.
Programming Game AI by Example has a great, easy-to-understand explanation and walkthrough for learning ANNs: http://www.amazon.com/Programming-Game-Example-Mat-Buckland/dp/1556220782
Once you've learned at least ANNs, you can delve into the popular approaches to GAI:
My biggest recommendation: read about all of them! Try to understand why scientists pursue each approach. Having a solid understanding of the motivation behind each approach will make it much easier for you to decide which path to pursue. I recommend the following books:
It looks like the Deep Learning folks have already pitched in, so I'll trust their recommendations are good on that end. Read their stuff too, then decide for yourself. You'll find that there's fanatics from every branch, and they'll claim that their way is the only way. This is the only thing I can tell you for certain: no one has proven that any approach is better than the other just yet, and anyone who claims they have needs to remind themselves of the no free lunch theorem [http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization].
You know, a fantastic book is Programming Game AI By Example. It's fantastic for learning about AI, but the author also put a lot of effort into the code, so you can learn a lot about general game design from it as well. Well worth the price. http://www.amazon.com/Programming-Game-Example-Mat-Buckland/dp/1556220782 . You can download the code samples from the author's website to see what I mean. It is only 2D, so it won't help you with collision detection and some of the more 3D specific topics, but the core of the engine can be applied to anything.
One thing that is really important is to realize that there's no silver bullet. Every design decision has its benefits and its trade offs. It's easy to fall into the trap of overthinking your design, especially for personal projects - it is more beneficial for you to try to write it and have to rewrite it because of a bad design decision than to spend months trying to come up with the perfect architecture. That's not to say that you should ignore design, but rather that once you think you have a good idea, try it out, experiment, see what works and what doesn't. If you focus on having a modular design to your software, you'll find that rewrites get easier and easier.
This book dives nicely into exactly what you want to do and even suggests some ways to combine steering behaviors:
http://www.amazon.com/Programming-Game-Example-Mat-Buckland/dp/1556220782
It sounds to me like you need to read a book on game dev and a bit about general programming. I can recommend
Programming Game AI by Example
Is really helpful for implementating agent AI, which is a big component for those types of games.
I'm a big fan of the 'Deep Learning With Python' book by Francois Chollet.
https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438
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Looks like the whole book is available here but the link is .cn so check it out on your own.
http://faculty.neu.edu.cn/yury/AAI/Textbook/Deep%20Learning%20with%20Python.pdf
This is a popular topic but I don't often see a comprehensive answer. I'm by no means an expert and currently learning myself.
There's two key stepping stones before jumping into AI, that being learning Python and data science. Python has wide support and a host of libraries reflecting the latest research on AI development.
There is also R, Octave and Java depending on the libraries you're looking to use, but they aren't nearly as popular as python. Note that if you want to embed your AI scripts into web apps or apps, then you'll need to learn javascript and java respectively.
The best resources for Python are
Great resources can be found here:
The next step is to get a brief grasp of data science. You can learn these from:
I wouldn't recommend codeacadmy since it's dated written in Python v2.x whereas Python 3.6x is more widely used
Then I would consider AI Specific courses found online. Theres two routes again here, there's the heavily academic route that delves into the theory and mathematics then there;s the practical route. Depends on the speed and pace you want to learn at because it's a massive field.
Theoretical
Practical:
Artificial Intelligence: A Modern... https://www.amazon.com/dp/0136042597?ref=ppx_pop_mob_ap_share
Sure, Musk and Hawking aren't ai experts. But Stuart Russell is. In fact, he's a co-author of what's widely regarded as the standard undergrad textbook on ai.
He is worried about ai. Specifically, about human extinction. In fact, there are a lot of influential ai researchers who are worried about ai.
There was a survey of people in the ai field trying to assess their opinions about the importance of ai safety, among other issues. Here are the results, if you want to look at them. Of note, approximately 5% of researchers believe that ai will result in human extinction (not could, will). However, 70% of researchers agree, at least to some degree, that Russell's concerns about are valid and need exploration.
: Apparantly, 1/20 ai researchers are working in a field they think will cause the extinction of the human race...
> But why should a deterministic "choice" be 100% the good one? Or is it, say, deterministically the good one in only 70% of the cases?
If you'd actually like to learn about the ways deterministic and stochastic decision policies work, I recommend AI: A Modern Approach. If you're just saying that a deterministic decision policy of tractable size won't make the optimal choice in all real-world situations, I agree.
But we're not arguing about effectiveness. I can't really discuss the effectiveness of different decision policies establishing a lot of mathematical-ish background; and they're irrelevant to the question of whether contra-causal free will can logically exist.
> what I know as a fact is that I indeed have a true freedom of choice
Sure... the exact same way you know the top yellow line is longer than the bottom one. If a very simple argument shows that your intuition is logically impossible, you should distrust your intuition.
(1) Is it the hardcover version (cover shown here)?
(2) I know you said "new condition", but is there any writing, scribbling, or highlighting in/on the book? Any other marks or blemishes?
Usually I see this book recommended.
https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/ref=sr_1_3?s=books&amp;ie=UTF8&amp;qid=1469739986&amp;sr=1-3&amp;keywords=Artificial+Intelligence
MIT also has full lectures for Artificial Intelligence available for viewing.
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/
What you need to learn depends on exactly what you want to do. If you want computer assistance to help you beat your best friend at correspondence chess, then getting AI to work for you may be as simple as buying or downloading a ready-made off-the-shelf program.
AI is a very broad label that applies to a lot of different things. If you want to write your own chess-playing program, then you need to know about binary trees, depth-limited depth-first search, alpha-beta pruning, and heuristics. These things are great for playing chess and other games that resemble it, but are rather useless for other things that people also call "AI", like computer vision or natural language processing.
My advice is to start by learning programming, practicing your programming skills on less challenging problems, and then working up to whatever you want to do. This subreddit's FAQ will help you.
Alternatively, you can buy a good introductory AI textbook, such as Artificial Intelligence: A Modern Approach. However, before you buy an expensive textbook, be warned: If you don't already have a decent grasp of how programming works, the information is likely to be somewhat overwhelming and not very useful.
edX.org has a few classes for their micromasters in artificial intelligence going right now until April 22nd or so. Though I think one is 3D modeling or something so I've completely ignored that. They are both free and you can access the course materials after the courses have ended, so you can watch the lectures, read the material, and take quizzes, but not receive a passing certificate or what have you. The two books for the Machine Learning course are both available online in pdf form for free.
Pattern Recognition and Machine Learning
The Elements of Statistical Learning
For the Artificial Intelligence course it's recommended to have:
Artificial Intelligence: A Modern Approach 3rd edition
I really recommend reading from the Russel and Norvig book. It's a good book and it gives you a much better feel for the field.
> Hey /r/learnprogramming i've browsed this subreddit a couple times and completed, and started a few courses like Codecademy... I'm looking to get some sort of job in this type of field by next year.
You should be aware that that's a pretty aggressive goal. Consider that people spend 4+ years of their life, full time, at a university, and are only then burgeoning on qualified for an entry level web-development position.
> if not, I will still want to hear what you guys recommend on what I should do to progress
At this point in your life, focus on learning stuff that interests you. If that's technology: great. If you are interested in technology, you could do worse than learning the syntax of one (or hell, even a few) different programming languages. You sound like you're already well on your way here.
> I'm also very interested in machine learning, so if you can recommend a path to learning it
Heh.. learn computer science first ;P. You'll also need to learn some limits/infinite-series, linear alegbra, calculus, and probability/statistics. At 15, that will keep you damn busy. If you're really motivated, pick up a copy of Russel and Norvig's very excellent book. That should give you some idea of what you're getting yourself into.
Lastly and most importantly, screw everything I just said along with what I (or anyone else) thinks. If you really want to learn something, do it. At the end of the day, it's on your shoulders, regardless of how insurmountable the task actually is.
At 10 years of age, I learned x86 assembly programming because I really wanted to write computer viruses. Everyone I asked told me that (assembly) would be way too hard for a kid. Screw all those people, I did.
The general idea is that AIs will be designed to have goals that meet their functions. Even the weak current AI (basically what you would learn about in Norvig's book) have heuristic functions which determine the optimal path (for searches) or local extrema of functions; this optimality is based on the ability of the heuristic function to represent the AI getting closer or farther to/from it's goal. The wikia article could hopefully give you insight, as there are many different heuristic functions, again, depending on the AI in question. To clarify a bit, an "AI" in the way I am using it can range from a simple search algorithm to something much more complex like a ten-layer convolutional neural network (which doesn't exactly use a single heuristic function).
You might be interested to learn that instead of lacking goals, it would be much worse if AI's had goals completely distinct from humans. One example is the paperclip maximizer, a machine/AI with the explicit goal of making paperclips through any means necessary. Since it's only goal is to build paperclips, it would eventually consume all resources, eventually destroying the human race in the process.
While this is overly simplified (you could have other rules, which prevent it from hindering humans), it does raise the importance of making sure AI's have goals which are in-line with humans'.
>Would it simply wait there to be given instructions? A calculator awaiting its next input?
If it is an AGI, probably not. An AGI would have reasoning abilities equal to or superior to humans, so there is really no reason to not make it completely autonomous (cause after all, you could almost always put limits on it, making it useless without a human). The major problem would be in aligning it's goals with ours (and, of course, building one in the first place).
Classic Russel & Norwig textbook is definitely worth reading. It starts from basics and goes to quite advanced topics:
http://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/
Udacity has AI class that follows some chapters of that book.
Murphy's textbook builds ML from the ground up, starting from basics of probability theory:
http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/
(I see, it was already recommended)
Coursera has the whole machine learning specialization (Python) and a famous ML class by Andrew Ng (Matlab).
I hope it helps. Good luck!
In terms of AI, I used this text for one of my AI/machine learning topics. Would recommend Artificial-Intelligence-Modern-Approach
Some other suggested readings from that topic:
Introduction Data Mining
Artificial Intelligence
www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597
No.
https://en.wikipedia.org/wiki/Artificial_intelligence
If you want to say "wikipedia doesn't count", try reading this:
https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597
I really can't tell at this point whether you a trolling, or just ignorant/obstinate towards what the words "Artificial Intelligence" actually mean.
I have a feeling you just can't seem to wrap your head around the idea that machines can learn in a manner that are opaque to their developer, so there is some middle ground between "fully scripted" and "fully aware", but you don't seem to want to learn either, so I am done here.
You can have a go at the book Artificial Intelligence: A Modern Approach by Russel and Norvig, it's fairly accessible and it's a fun read.
Probably start with Artificial Intelligence: a modern approach. This is the state of the art AI as of 2009, of course in AI years that's ancient history but it's background you must know if you're serious about AI.
Following on from that you have the very popular statistical techniques, you can read about these in Pattern Recognition and Machine Learning. These are a wide range of statistical models and algorithms that allow machines to infer, classify and predict. Another very important concept is Chapter 14 on combining models. IBM's Watson for example uses a complex network of "simple" models who combine their answers to form the final responses.
From all the techniques in the previous book, neural networks from Chapter 5 have become the most popular and powerful. These are covered in Deep Learning, and are currently the cutting edge of machine learning. They are extremely general models that seem to be highly successful at a range of tasks. In particular their popularity comes from their amazing accuracy in image recognition, which really challenged past algorithms.
Ultimately nothing you can learn from anyone is sure to bring you close to sci-fi AI. The techniques to produce such an AI eludes even the foremost experts. You may also become disillusioned with your dream as you realise just how mechanical and constrained AI is. I personally think we'd have better luck genetically engineering intelligence in a random animal/insect than creating true intelligence in silicon and circuits.
https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597
Lots of ways to buy/rent it, not just Amazon. (IMO whatever you do though avoid the paperback version - worst form factor of any book I've ever tried to read. Literally replaced it with the hardcover halfway through the year...)
In particular I'd look at the stuff on adversarial search, and try to do some simple exercises with minimax searching of a game tree (even something simple like Tic Tac Toe) as the assignment on that material was the most time-consuming...
May I recommend a book I used in college:
http://www.amazon.com/Artificial-Intelligence-Modern-Approach-2nd/dp/0137903952/ref=sr_1_2?s=books&amp;ie=UTF8&amp;qid=1396106301&amp;sr=1-2&amp;keywords=Artificial+Intelligence%3A+A+Modern+Approach
There is a newer (and more expensive) edition (3rd), but frankly it isn't necessary.
This book will give you a very broad and thorough introduction to the various techniques and paradigms in AI over the years.
As a side note, look into functional programming languages, Haskell, Prolog, Lisp, etc.
Good luck, my friend!
I always thought the best algorithms book I ever read was AIMA.
I totally agree.
As long as it isn't Twilight. Or Artificial Intelligence: A Modern Approach. That would be weird
If you want to do any AI programming, get this book: Artificial Intelligence: A Modern Approach (2nd Edition) (Hardcover). It's what my AI college professor used for his class
Not covered in this video: Peak Oil and the End of Cheap Abundant Energy.
All the bots in this video (and our whole society, in fact) are fueled by cheap abundant energy from fossil fuels. Reddit loves to pin its hopes on vaporware sources of cheap energy that are always JUuuuuST about to be figured out, but the reality is that we are NOT going to find a working replacement for our energy needs.
Bots may be here now, but they are not here to stay. The future will look more like The Long Descent and less like the Singularity.
Horses and human labor are poised to make a come back. Learn a trade craft. Grow food in your back yard. Develop a skill that will have value in the post-collapse economy. Become a beekeeper. Become a homebrewer. Make soap. Collapse now and avoid the rush.
EDIT: For a much more level-headed analysis, read this article right now: The End of Employment by John Michael Greer
The Singularity Is Near. Not sure why you're asking it in this forum, but it wouldn't mess with my theology at all, and I would welcome it as a blessing.
I suggest this book by Ray Kurzweil. Simply amazing.
I was actually thinking this a few days ago about my dog. Having read The Singularity Is Near by Ray Kurzweil, there are a few sections devoted to uploading the brain and using technology to augment brain capabilities. What it boils down to is that the truly unique things about our brain are 'past memories', 'emotions', and 'personality'. Every thing else is the brain is just stuff that regulates our bodies and processes information.
If we take the personality, memories, and emotions of my dog, and improve on the other parts of the brain by adding better memory, speech recognition, etc. Then we might just be able to create another biological species that rivals our intelligence.
We already are making the assumption that technology will make humans more advanced, the same thing should eventually apply to all other biological animals as well. (Except Mosquitos, of couse)
It's not inevitable. Here you go:
Ending Aging: The Rejuvenation Breakthroughs That Could Reverse Human Aging in Our Lifetime
Transcend: Nine Steps to Living Well Forever
The Singularity Is Near: When Humans Transcend Biology
https://www.amazon.com/Singularity-Near-Humans-Transcend-Biology/dp/0143037889
Did the book happen to be The singularity is near?
How can you be right or wrong about something that doesn't exist yet? I recommend this book.
https://www.amazon.com/Singularity-Near-Humans-Transcend-Biology/dp/0143037889
Ray Kurzweil has been scarily accurate at predicting technological trends. You should check out The Singularity is Near, in which he makes some very specific estimates about many of these types of questions. You will have a completely different outlook on the way technology progresses after reading this... I certainly did.
Please read The Singularity Is Near by Ray Kurzweil. It will tell you what's next in terms of technology+biology.
wow, yeah, 10 years is closer than 50 that's for sure. If you are right, that is something to be very excited about for sure. Just think of the possibilities. Can I ask where you get the estimate of 10 years? I am fairly uneducated on the subject, and admittedly I haven't even read the book about it therefore I am hesitant to even mention it, but I am familiar with Ray Kurzweil and his theories about The Singularity (basically when man and machine combine, hence "redefining" what it means to be human). I found his recent comments on nano-bots in our brains making us "God-like" intriguing to say the least, and if ever we will be able lay back, close our eyes, and experience some sort of virtual reality, it just makes sense to me that the most likely time when that will happen is when we have super intelligent nano-bots inside of our brains manipulating the way they work. I, personally, can't wait for this to happen, but I also know that I will be very apprehensive when it will come down to willfully injecting into my body millions and millions of nano-bots that have been specially designed to 'hijack' my brain, and make it work better. I think I will probably wait 10 years or so after people start doing it, maybe longer.
Here is Ray Kurzweil's book I was referring to that I really want to read: The Singularity Is Near: When Humans Transcend Biology
EDIT: I forgot to mention why I really brought up the singularity-- Mr. Kurzweil initially predicted that the singularity would occur sometime before 2030 (aka in the 2020's), but I believe he has now modified that to say that it will occur in the 2030's. Either way, that is not far away, and, being a pretty tech-savvy person myself (I pay attention to a thing or two) I think the 2030's is a reasonable estimate for something like this, but, as I mentioned earlier, I think it is the ethics of such a thing that will slow down true VR development (see: how the world responded to cloning)
double EDIT: just another thought (albeit quite a tangent)-- once a true singularity has been achieved (if ever?), 'transplanting' our consciousnesses into another body all of a sudden becomes quite a bit less sci-fi and altogether a more realistic possibility...
The Singularity is Near by Ray Kurzweil
"Based on current resources and estimated usage patterns based on historical information," shipping from the moon could be cost effective by 2045.
Of I saw the context; that's how I ran into this rant.
First of all, "Industrialization didn't improve things but for a small subset of society" isn't true. I can see where you're coming from, but even the poorest nations and people have benefited from industrialization.
Industrialization has caused murder rates to go down approx. 30-100 times lower. We are living in the most peaceful times in human history. See, thousands of years ago, you were lucky to see the age of 25. Now, even the poorest nations have average lifespans of 40+.
The reason you're so cynical is because news is more widespread nowadays. Again, I know where you're coming from because I have parents who think like you do.
Read this book:
http://www.amazon.com/Singularity-Near-Humans-Transcend-Biology/dp/0143037889
if you want a look of what is coming ahead and why we are in theory living in a third industrial revolution. Once you read this, you will learn to love the future. I guarantee it.
Thanks!
Have you read The Singularity is Near? It's quite an interesting read.
Dr. Steel has a great view of things; I agree to everything in this video!
Give this a read. This isn't some crackpot, this is Google's director of engineering. I'm not saying it contradicts what you are saying.
>I doubt we'd anything like a true AI in 20 or so years
Is pretty close to his timetable too, but honestly even getting close to that computational power is well over what is needed to drive a car.
have you read TSIN yet?
How to Create a Mind: The Secret of Human Thought Revealed By Ray Kurzweil. Ray is world renown for predicting the outcome of upcoming technologies with stunning accuracy. Not through psychic powers or anything, but through normal predictive means. He predicted when the first machine would be capable of beating the best chess player in the world. He is predicting that we will approach what is called the technical singularity by 2040. Its amazing. He is working with Google on a way to stop aging, and possible reverse it one day. Something I recommend for sure.
EDIT: Books are awesome
If he were trying to understand the brain, instead of explaining it in a way that matches his assumptions - he would stand the shoulders of neuroscientists who are writing on the topic. But I get it, e.g., it's difficult to describe the brain as a computer when in a brain processing and storage are the same thing.
> http://www.amazon.com/How-Create-Mind-Thought-Revealed/dp/0143124048/ref=asap_bc?ie=UTF8
Well I'm not sure that's entirely true:
http://www.amazon.com/How-Create-Mind-Thought-Revealed/dp/0143124048/ref=asap_bc?ie=UTF8
At the very least, he's trying to understand the brain.
Kurzweil tries to do this all the time
http://www.amazon.com/How-Create-Mind-Thought-Revealed/dp/0143124048/
Doomed by Chuck Palahniuk
Black Powder War by Naomi Novik
How to Create a Mind by Ray Kurzweil
The King in Yellow by Robert Chambers
John Dies at the End by David Wong
Yes. I read a lot of books at the same time. Yes, I regularly finish books. Doomed I just finished about a week ago, and I am currently in the middle of all the other books. So far I've enjoyed all of these books immensely.
I had dismissed artificial consciousness for a long time because I believe it to be impossible and in fact still do. But I realized my outright dismissal was really a defense against the reality of superintelligence which is not that machine consciousness has major implications but but machines becoming competent does. I think for many (perhaps this is unfair) who assert that consciousness is purely material and therefore can be reproduced just see artificial consciousness as a big score for the naturalistic or material reductionist worldview. Then there are experts who are only interested in taking machine intelligence as far as it possibly can go whatever that means. Significantly there is a smaller group calling for caution and prudence in that endeavor. Have you seen the Story of the Sparrows? I can't find a link to it but it's at the beginning of Superintelligence: Paths, Dangers, Strategies.
do it
https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834/ref=tmm_pap_swatch_0?_encoding=UTF8&amp;qid=&amp;sr=
>no work from humans
On that day the machines cut out the humans, not implement Basic Income.
&#x200B;
https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834/
I'm just going on the opinions of experts in the field:
https://en.wikipedia.org/wiki/The_Second_Machine_Age
Superintelligence: Paths, Dangers, Strategies https://www.amazon.com/dp/0198739834/
Yes we've had cotton gins and such but Artificial General Intelligence and Superintelligence is a game changer.
Superintelligence by Nick Bostrom is supposed to be good (I've been meaning to read it). There's also the YouTube video Humans Need Not Apply by C.G.P Grey which sounds like exactly what you need and the description has links to most of his sources.
Re. Nick Bostrom: You should have a look at Superintelligence: Paths, Dangers, Strategies. It's definitely not about Terminators ending all of humanity. If anything, he outlines why even an indifference to human life can cause an AI (in the general or super-intelligent sense, not ML/prediction sense) to either subdue or circumvent humans' attempts to stop it from completing its goals.
If you believe that artificial general intelligence is possible, then he writes about things worth considering.
Maybe not exactly what you're asking for, but here's one by Bostrom: https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834
Anyone interested in AI should read Nick Bostrom's book, Superintelligence. Fair warning, it is very dense but rewarding.
One take away here is he did a survey of leading AI researchers who were asked to predict when General AI might arrive - the majority (~67%) believe it will take more than 25 years, interestingly 25% believe it might never happen. Source
Also, really great panel discussion about AI with Elon Musk, Bostrom, others.
Superintelligence - Not fiction, but scary nevertheless
https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834
For sure - it's the old saw about technology being a force for good or evil, depending entirely on how it's used.
(I'm actually reading "Superintelligence" right now)
Where does that leave you in terms of caring about privacy, though? You said you're not swayed by the argument about giving up your email password... is there another argument you find compelling? Do you think it's pretty much irrelevant if there's no oppressive regime to abuse the citizenry?
There's an old saying that you don't really understand something until you can make it yourself. So I think the biggest and most interesting considerations are the meta-ethical questions. Any responsible attempt to create an AGI has to grapple not only with the fundamental question of what constitutes ethical behavior, but with the immense challenge of implementing it in software. As a species, we're either going to need to understand ethics much more deeply, soon, or we're going to be doomed.
Must-read book on this subject: Superintelligence (http://amzn.to/24USaWX).
https://www.amazon.com/gp/aw/d/0198739834/
Consciousness is a hard to define word, but he talks about what it would mean if you had an artificial general intelligence significantly smarter than humans, possible paths to create one, and dangers of doing so. I haven't looked into any of his other stuff (talks or whatever).
I think you are being overly optimistic about SGAI, and I suggest you start by reading Bostrom's Superintelligence in addition to his pieces on the ethical issues of AI. Any AI-agent, in attempting to maximise its utility functions, will initially have a set of utility functions allowing for prioritisation and optimisation of goal-setting tasks. Any self-improving SGAI agent will immediately take action to limit the development and capabilities of other potential SGAI, as they may have conflicting utility functions.
What utility functions might an SGAI have? Realistically, the first SGAI will be developed by an organisation, not a single person, and its utility functions will likewise reflect the goals of that organisation, or potentially some menial auxiliary task - if the organisation has lax safety standards and incautious development procedures. To go into the speculative realm, the SGAI may be tasked with logistical scheduling or managerial decision-making in a large corporation, or in a government, dynamically censoring internet traffic, identifying "terrorists", and optimising the efficacy of military combat.
Although higher productivity may result indirectly, an SGAI with the utility function of maximising the profit of a particular corporation, or maximising the stability of (or territories controlled by) a national government, will pursue its utility functions and find solutions inconceivable to us simply due to our automatic decision-tree-pruning based on moral and ethical standards, which the SGAI will probably lack. It would also be completely irreversible, as any SGAI perceiving its utility functions to be in conflict with human moral codes will use deception when interacting with humans in order to continue to maximise that utility function.
***
Edit: to give an example, the classic example is a so-called paperclip maximiser, an SGAI may be tasked with maximising paperclip production. The SGAI would, if it was not given any other utility functions might do the following
That's not exactly a trickle-down effect.
A recent (and I think the best yet) book on what a smarter-than-human AI should look like: Superintelligence
I'm just about to finish up Superintellgence, by Nick Bostrom, and I'm a bit scared of AI now. Bostrom elaborates on a ton of ways that AI could go horribly wrong (for humans, and possibly for the Universe, and I'm only slightly exaggerating on that), and I'm not sure we will get it right. Maybe, but I'm not convinced it will be as easy as Kurzweil suggests.
I recommend you read Superintelligence. It answers this kind of question and more. Not an easy read, but not too hard either.
In respects to AGI/ASI (so disregarding nanotech, quantum computing, and other singularity subjects), Nick Bostrom is one of current leading academics on the subject: https://www.fhi.ox.ac.uk/publications/
His book is a great intro to what AI might bring in the near future, and you can easily make a connection to Kurzweil's predictions from there.
Some research group working on artificial general intelligence is successful in making one, but they did not possess a sufficiently detailed theory of AI safety, and plugs in a utility function (or whatever goal system they might be using) that "seemed reasonable", perhaps after some technical but still insufficient analysis.
The most comprehensive resource is Bostrom's "Superintelligence: Paths, Dangers, Strategies". For something shorter, you might find something in Bostrom's or MIRI's papers.
AI is doable.
Friendly is hard, and we probably only get one try..
I'm glad you asked!
There are a few reasons for this, far better explained by the various experts that research precisely this problem. Here's an executive summary if you only have ten minutes
Or if you have only one minute, the most important concept is that we simply do not know how to program human values. If we were to create an AI, their goals would most likely not be in line with human goals. To quote a now famous line (source):
>The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else.
I used two main resources to start in ML:
Both are quite math-heavy but it gave me a very solid basis to start building on. For learning Tensorflow I used examples and the documentation.
Absolutely!
Math is everywhere and it's just about seeing the patterns emerge from simplicity. My knowledge on this topic has mainly been from my own work in Artificial Life and encoding AI genetic knowledge combined with my general interest in biological patterns (which are everywhere in nature) but the first thing that got many things to click for me was playing around with Turtle Logo in high school that is all about using simple constructs to create amazingly complex structures (i.e. one, two - look familiar?).
Sadly I don't work on my AI research anymore due to ethical concerns so I'm a bit out of date but I'd highly recommend the following that weren't mentioned in the original post though:
>What brought you that position?
My path included convert parents, BIC, a very happy childhood in a huge loving family, RM, 30ish years TBM, 10 years agnostic (closeted 7 or 8 years), going on I think 7ish years now as a Christian. I'm bookish - PhD in engineering. My agnostic period kind of grew out of the full term surprise stillbirth of our second child. I was already starting to question BoM historicity, I had issues with the whole "I know the church is true" thing / epistemology, and it was a fairly quick worldview failure after that. Then with the discovering church history. You get the idea. During my agnostic period, I held a position pretty much identical to what I hear you describing: I cared deeply about truth (still do, very much), I knew logic works (still a big fan, but more aware of its limits now), eventually felt called to try to 'get off the fence' of agnosticism, if I could do it authentically. My approach was to start reading more. Things I read: Godel Escher Bach, Tolstoy's War and Peace, Pilgrim's Progress, a bunch of CS Lewis, the bible in modern english, and a bunch of other stuff I can't remember. Some things that most impressed me about the bible: stories about what goes on in people's hearts that I could see in myself, in my loved ones, and around me in the world, the coherence of the entire narrative around the theme of redemption, the concept of Grace implied in God's relationship to His people and later extended to the individual by Paul.
> what makes you believe in the Christian God?
Here is one thing I wrote about that before.
Em janeiro, terminei de ler Fundação, do Isaac Asimov. É um livro sensacional; super recomendo. Agora, estou terminando The Foundational Falsehoods of Creationism e Gödel, Escher, Bach: An eternal golden braid. O próximo que eu vou ler provavelmente será Consciousness Explained, do Daniel Dannet.
I am not totally sure what you are asking for actually exists in book form...which is odd, now that I think about it.
If it were me, I would think about magazines instead. And if you really want to push him, think about the following options:
If you insist on books...
I see you already mentioned A Brief History of the Universe, which is an excellent book. However, I am not sure if you are going to get something that is more "in depth." Much of the "in depth" stuff is going to be pretty pop, without the rigorous foundation that are usually found in textbooks.
If I had to recommend some books, here is what I would say:
Hope that helps! OH AND GO WITH THE SUBSCRIPTION TO NATURE
edit: added the linksssss
[2 of 4]
No one here's read Gödel, Escher, Bach: An Eternal Golden Braid?
The skill which cannot be named, that you can only access with u-mode thinking.
I can't think of it, but I think it's a movie production company. It's like this cover of "Goedel Escher Bach"
That doesn't really sound like AI to me. When I think of AI, I imagine some sort of machine that changes state when given certain inputs, and the responses can be "trained" or biased a certain way if the right sorts of inputs are given.
My original question was really angling to make you think in the other direction -- how does the physical material of our brain become conscious? Is the concept of consciousness something that can only arise from cellular material, or can it be implemented with different underlying materials?
I really suspect that there's nothing exclusive about the material our brains are made out of. Take a peek at Douglas Hofstadter's Gödel, Escher, Bach: An Eternal Golden Braid if you're interested possible mathematical ideas for how consciousness could be an emergent phenomenon.
Repeated exposure to formal systems will yield a greater ease with which you engage those systems. Force yourself to read and evaluate theory more often. Read books such as Godel Escher Bach if you need practice.
It worked for me, but it wasn't easy by any means.
I hate to say this but you do not have a very good understanding as to what "Math" actually is... Mathematical systems are based on axioms. Math itself is just the logic that defines a system (i.e. the system must be consistent, etc) and it is the mathematical logic that backs up the system. In other words, you were looking for something of the scientific method in mathematics... It's mathematical logic.
You can even change the axioms all you want but if the result leads you to contradictions, then it is inconsistent. Look at Gauss's work with non-euclidean systems for example. It is not that math is based on certain axioms but it does use axioms in the construction of systems.
To use you ethics example: It is one thing to say that such ethical axioms could exist (there are a lot of things that could exist) but coming up with a consistent set of ethical axioms (of sufficient size, etc, etc) or a mathematical ethical system, is a whole other ball of wax. While I can't prove that a consistent set of ethical axioms doesn't exist (enter problems of proving a negative here), the odds of such a systems existing is (very, very, very, ... ) low by my estimation.
No mathematician worth their salt would say that mathematics describes "the real world", physics (for the most part) does that. Mathematics is just applying a specific process to different systems (different sets of axioms) and working out the result (consistency, figuring out theories in that system, trying to find mappings to other systems, etc).
I'm not sure this explanation has helped... It's hard to explain these ideas without a wall of text. But if you are interested in philosophy of mathematics, GEB is a good book and A Profile of Mathematical Logic is a great book but a little dense.
If you're going to quote me, don't skew my meaning
> My argument was that minds are turing machines because as far as we know, minds are matter, and as far as we know minds are matter because that's what science tells us, and i will certainly revise should science come up with something else.
As my complete quoted statement should show, I am fully aware that science is no dogmatic source of authority with a single voice. Science is always revising itself, and there are always debates within it.
>[the mind== matter hypothesis] is one hypothesis among several, and with less evidence than the other available ones.
>There are thousands of scientists, who issue books which are contradictory to one another. For example Raymond Tallis, a prominent neuro-scientist, argues that mind =/= matter:
It seems that you've been reduced to simply protesting that there exists disagreement among intelligent people. I will allow that the mind body problem isn't a simple problem, many years from now if a consensus is reached, many smart people will be proven wrong. Even so, your characterization of the controversy on this topic that exists within the field of neuroscience specifically is hyperbolic, like so many other things you say.
Importantly, you say you have evidence but the only evidence you've given that the mind isn't matter is your very controversial interpretation of godel and turing that every professor of logic I've spoken to or heard of (not the least of which is OP) absolutely do not endorse, godel's own side musings notwithstanding.
Furthermore, I must ask you, what then is mind? You by your own admission several posts ago, have no idea, you don't even have a guess. Now imagine yourself a neuroscientists, trying to figure out how the mind works. You have before you a human brain, in or outside of a living human body, all of which is matter. What else are you possibly going to study? You cannot step outside of this without stepping into some kind of religion, platonism, or mysticism, neither of which can be studied by science, induction, or even logic. I've spoken to lots of neuroscience professors, and this was their clear consensus. Some respectfully did not rule out religion or platonism but they certainly decried its presence in science. As I said, within neuroscience, the presumption that mind==matter isn't controversial.
By the way if you're gonna throw a book at me, I will return the favor, in fact you may have even heard of this book by cognitive scientist Douglas Hofstadter.
https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567
>The reason it is circular is because in OP, the author omitted one of Kurt Godel's explanations for the Incompleteness Theorem -- the one that said that mind =/= matter -- and I corrected him on that. 4-5 replies later you jump in, and presuppose that mind = matter, oblivious to the fact that this was already discussed and challenged before you ever were in the picture.
I will only say that this is a completely unreasonable caricature of what was said in this thread.
Nice. My plan is to buy them all once they're done; they should make a great trophy to have on my bookshelf for life.
I've been reading The Lifebox by Rudy Rucker over break. I'm about a third of the way in and I'm liking it so far. I've already had many of the epiphanies he's trying to install, coming off of reading Hofstadter's GEB over the summer. So far, I consider GEB the superior book, but I appreciate the narrower focus of The Lifebox (Rucker stays strictly on the topic of Computer Science with some very elementary explanations of some advanced physics concepts). The section on molecular biology in the middle of GEB read at a snail's pace for me.
Sorry, I've been playing around with quines and reading too much Godel, Escher, and Back lately.
I got a book :D
I'm not an ME, but these are some of my (more computery-ish) favorites that might have general engineering appeal:
The Difference Engine - proto-steampunk!
Gödel, Escher, Bach - essays on logicians' wet dreams.
Anathem - mathy and fantastic.
House of Leaves - you'll either love it or it will just piss you off...
Also, anything by Phillip K Dick or Kurt Vonnegut. And Feynman's (first) autobiography is definitely a must.
Godel, Escher, Bach: an eternal golden braid by Douglas Hofstadter is a great book that talks about this.
http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567/ref=sr_1_1?ie=UTF8&amp;qid=1333652628&amp;sr=8-1
That sounds really cool, the fact that you've had the time to research and now to explore, I wish there were buddhist temples/centers/what-have-you in my area, but I live in latin america so it's highly unlikely (I'll ask around anyway, though :P). And now that you mention that
> Zen tends to lean more towards the belief that we are already enlightened and just have to discover that enlightenment as compared to other schools that hold the view that enlightenment comes after this life
I realize that one of the books on buddhism I have read, "Buddhism plain and simple" is Zen-based! Maybe that, the book by D.T. Suzuki I'm reading right now and the mentions about Zen in "Gödel, Escher, Bach" have biased me towards Zen, I'll definitely look into other schools to see what's there.
What you really need to read is Gödel, Escher, Bach: An Eternal Golden Braid. It's a very readable analysis of computers and algorithms made in the hopes of either proving or disproving the possibility of artificial intelligence. It even won a Pulizter.
Godel, Escher, Bach
Hmm, I don't have any specific advice. It sounds to me like a depression - Google that shit and see if fits and if there is any good standard stuff you can do about it. It certainly doesn't help that it's November (assuming you are in USA/Can/EU).
Last time I was in a similar place I got the fuck out of dodge - it didn't 'solve' my issues, but it gave me enough new energy that I could refocus and deal with my shit from a better place.
Another time I was depressed (middle of winter and no money, so travelling was out of the question), I just hermited it up with some good books:
and went for a lot of late night walks by myself in the cold all bundled up. Did a lot of thinking - didn't even try to socialize. It was cathartic and I processed a lot. When spring came I was in a much better place.
Just my experience, take what you can from it.
I would give Gödel, Escher, Bach: An Eternal Golden Braid by Douglas R. Hofstadter a go.
Just a few I've gone through in the past half-year:
The 4-Hour Workweek by Tim Ferriss
A People's History of the United States by Howard Zinn
Gödel, Escher, Bach: An Eternal Golden Braid by Douglas R. Hofstadter
The Accidental Billionaires: The Founding of Facebook: A Tale of Sex, Money, Genius and Betrayal by Ben Mezrich
Long Walk to Freedom by Nelson Mandela
and
The South African Gandhi: Stretcher-Bearer of Empire by Prof. Ashwin Desai and Prof. Goolam Vahed
You might like to read: https://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567
www.amazon.com/Gödel-Escher-Bach-Eternal-Golden/dp/0465026567
Godel, Escher, Bach maybe.
> It's entirely possible that you had a professor with a specific view of mathematics and told you that their view was right and dismissive or other views, and that you've internalized this attitude.
I formed my views on the matter after I had left school - no professor opined on any philosophical underpinnings of the field that I can remember.
In the same vein, it's entirely possible you have only studied this topic so far as to justify your already-held beliefs in a god, and no more. It's more than possible since you are not using language common to the field, and are talking about formal systems in ways that are radically different than experts.
> The point is that when math and science conflict we believe the math. This would not be the case of math was invented to describe reality.
This is demonstrably false. We believe the observations. When observational evidence conflicted with Newton's laws of gravitation (which were a completely consistent mathematical system), well...we needed new models of gravity, new math.
> You are not a square circle.
Ha, I'm sure this seems self-evidently true, but define what you mean by "you" and I'm sure you'll see that it refers to something that you have empirical evidence for. Unless you are claiming "you" is also an abstraction, and not a real object?
> You need to read this -
I will read it. For an introductory look at formal systems and many other related topics, I would suggest the fantastic GEB by Douglas Hofstatdter.
> Yeah, but how does a NOT gate work?
A NOT gate can be implemented as a NAND gate where the other input is directly attached to the power supply.
> I want to know on the absolute lowest level how this works
Go eat Godel Echer Bach: an Eternal Golden Braid. I suspect strongly you are trying to ask a question that you lack the language to express. This book covers an awful lot on the subject of computability, and may help you ask your question.
Guide to stoic joy
Godel, Escher, Bach
Drive yourself sane
Godel, Escher, Bach
Geometry of meaning
Hypnotic Writing
Holographic Universe
Let me know if any of these sound interesting to you and I can refer you to more similar ones. These books have changed my life and helped me learn a lot, they are some of the best books I have ever read.
You have to be above the bar to begin with. If you can understand exactly what intelligence is then you can increase it.
Meditation can be used as a way to gain insight. This is not all types of meditation, but there are definitely types of meditation with the goal of enlightenment in mind. Using the Buddhist definition of enlightenment and overly simplified explanation is insight, specifically the type of lower level type of insight that not everyone can get to and for the most part needs to be unlocked. Once it is unlocked, how one utilizes it can be a large intelligence booster, but you have to be able to comprehend how your mind works. If you can't fully recognize a lot of advanced and abstract concepts then knowledge gain is possible but hardly any intelligence gain.
Using the example you mention, math is utilized on the other part of the brain in such a way that you can multitask while solving advanced math problems. A way this can be figured out is solving math problems in your sleep. It is like a piece of your brain is a math coprocessor and it can chug along while you are talking to someone, reading writing, sleeping, or generally not paying attention to it, much like cooking something in the oven.
It depends what you want to learn. The most direct path is raw insight. For advanced logic, paradoxes, and other mathy nerdy stuff you might want to checkout GEB. Meditation doesn't skip the learning step. You still have to learn things the same way everyone else does. Meditation just helps you realize you can utilize your brain to a more full potential.
If you are really interested and think you can can push forward, I highly recommend you try a 300µg+ dose of lsd. Tripping is the same thing as a deep meditation state, but it doesn't stay. It is like driving a car over the mountain instead of walking. In a deep state under the influence you can do all of the more insightful things one can do in a deep meditative headspace. However, figuring it out could take multiple trips as sometimes insight will take 6 hours to come full circle. When meditating in a deep headspace the answer can come much quicker.
The idea is if you can figure it out while tripping, then you can remember what you've learned and migrate it into meditative practices, as it can literally take a life time to get to the level of meditation skill as one night of dropping acid will bring you to.
It is definitely possible. If you don't ask very specific detailed questions about how your brain works, I will not be able to explain in detail, and without asking yourself you can't move towards figuring things out either.
An efficient way to get to a deep headspace from meditation is a map, so you have an idea of which direction to go in. This tends to be pretty good.
From a discrete math class.
> Gödel, Escher, Bach: An Eternal Golden Braid (commonly GEB) is a book by Douglas Hofstadter, described by the author as "a metaphorical fugue on minds and machines in the spirit of Lewis Carroll".[1]
https://www.amazon.com/Annotated-Turing-Through-Historic-Computability/dp/0470229055
http://gen.lib.rus.ec/search.php?req=the+annotated+turing&amp;open=0&amp;res=25&amp;view=simple&amp;phrase=0&amp;column=def
A better source to learn what Turing's paper was all about with explanations of the concepts.
There are books that are compendium of computing such as
but they still require some effort on your side.
It would be better if you rephrase the question from "Cliff Notes" to "beginners intro that is not Sipser" : )
Here's what you're looking for:
The Annotated Turing: A Guided Tour Through Alan Turing's Historic Paper on Computability and the Turing Machine
In that vein, you might like Annotated Turing, if you have any interest in Computer Science. It's an annotated version of Turing's most famous paper (the one that basically establishes the basis for computers and computer science), but it can be a little dry if you're not inherently interested in the topic.
Also, the much more fun Logicomix (yes, a math comic book :D), about Bertrand Russel's quest to establish a logical basis for all of mathematics.
forgot to mention this https://www.amazon.com/Annotated-Turing-Through-Historic-Computability/dp/0470229055
If you want a detailed breakdown of Turing’s seminal paper Charles Petzold’s book is great:
https://www.amazon.co.uk/Annotated-Turing-Through-Historic-Computability/dp/0470229055/ref=nodl_
Title: On Computable Numbers, with an Application to the Entscheidungsproblem
Authors: Alan Turing
Link: http://plms.oxfordjournals.org/content/s2-42/1/230.full.pdf
Abstract: In just 36 pages, Turing formulates (but does not name) the Turing Machine, recasts Gödel's famous First Incompleteness Theorem in terms of computation, describes the concept of universality, and in the appendix shows that computability by Turing machines is equivalent to computability by λ-definable functions (as studied by Church and Kleene). Source
Comments: In an extraordinary and ultimately tragic life that unfolded like a novel, Turing helped break the German Enigma code to turn the tide of World War II, later speculated on artificial intelligence, fell victim to the homophobic witchhunts of the early 1950s, and committed suicide at the age of 41. Yet Turing is most famous for an eerily prescient 1936 paper in which he invented an imaginary computing machine, explored its capabilities and intrinsic limitations, and established the foundations of modern-day programming and computability. From his use of binary numbers to his exploration of concepts that today's programmers will recognize as RISC processing, subroutines, algorithms, and others, Turing foresaw the future and helped to mold it. In our post-Turing world, everything is a Turing Machine — from the most sophisticated computers we can build, to the hardly algorithmic processes of the human mind, to the information-laden universe in which we live. Source
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I hadn't heard of Lie Groups as well (and didn't look it up the first time you mentioned them) - they sound amazing!
Right now I'm mainly reading the Murphy Book after having finished Probabilistic Models of Cognition (which I enjoyed because I also always wanted to check out Scheme and has some great interactivity).
I suppose I'll have to put these books on the list, thanks! ;)
It really depends on your comfort and familiarity with the topics. If you've seen analysis before you can probably skip Rudin. If you've seen some functional analysis, you can skip the functional analysis book. Convex Optimization can be read in tandem with ESL, and is probably the most important of the three.
Per my other comment, if your goal is to really understand the material, it's important you understand all the math, at least in terms of reading. Unless you want to do research, you don't need to be able to reproduce all the proofs (to help you gauge your depth of understanding). In terms of bang for your buck, ESL and Convex Optimization are probably the two I'd focus on. Another great book Deep Learning this book is extremely approachable with a modest math background, IMO.
It's only $72 on Amazon. It's mathematical, but without following the Theorem -> Proof style of math writing.
The first 1/3 of the book is a review of Linear Algebra, Probability, Numerical Computing, and Machine Learning.
The middle 1/3 of the book is tried-and-true neural nets (feedforward, convolutional, and recurrent). It also covers optimization and regularization.
The final 1/3 of the book is bleeding edge research (autoencoders, adversarial nets, Boltzmann machines, etc.).
The book does a great job of foreshadowing. In chapters 4-5 it frames problems with the algorithms being covered, and mentions how methods from the final 1/3 of the book are solving them.
https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/
I would start with reading.
For Neural Networks, I'd do:
For overview with NN, Fuzzy Logic Systems, and Evolutionary Algorithms, I recommend:
Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation (IEEE Press Series on Computational Intelligence) https://www.amazon.com/dp/1119214343/ref=cm_sw_r_cp_apa_i_zD11CbWRS95XY
I really like Deep Learning by Ian Goodfellow, et al. You can but it from Amazon at https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=sr_1_1?ie=UTF8&amp;qid=1472485235&amp;sr=8-1&amp;keywords=deep+learning+book. If you are a little cash strapped, there is an html version at http://www.deeplearningbook.org/. Of course, this book is specifically focused on neural networks as opposed to ML in general.
A brilliant mind among us. His book if anyone wonders:
https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618/ref=sr_1_1?keywords=Deep+Learning+%28Adaptive+Computation+and+Machine+Learning+series%29&amp;qid=1554425041&amp;s=gateway&amp;sr=8-1
This is a great book that takes you from chapter 1 in linear algebra and goes into machine learning with neural networks.
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https://www.amazon.com/gp/product/0262035618/ref=ppx_yo_dt_b_asin_title_o08_s00?ie=UTF8&psc=1
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The authors also have a website with some of the material.
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https://www.deeplearningbook.org/
I would make a distinction to what are "complex" algorithms/methods towards simple/basic methods. You seem to be at a stage in which it's better for you to discard all the complex methods, and maybe just focus on the simple and basic methods. Simple because they do not require a lot of mathematical knowledge and basic because further theory is built upon them. This would exclude, for now, all the recent published literature.
I would suggest you to get one book that will ease this process, such as Bishop's. Just start with the basics of maximum likelihood and posterior inference estimation with simple Gaussians. I assure you that this is basic, in the sense that you will recognize and use this piece of knowledge in most advanced papers. Mixture of Gaussians and the EM algorithm are also a basic topic, as well as Neural Networks (the simple sigmoid fully connected).
Just make sure that you know these three topics extremely well and learning the rest will be slightly easier.
BTW, this is a post for /r/MLQuestions or /r/learnmachinelearning
Bishops book includes answers to selected exercises
If you really like this stuff, I would highly recommend two textbooks:
For the communications topics, reliability, optimization etc, ditch Leon-Garcia and pick up this book by Trivedi
If you're interesting in Machine Learning like I am, then this book by Bishop is fantastic. You can find both in the Engineering library I believe.
Start by Christopher Bishop's book titled 'Pattern recognition and machine learning' (http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738). It's a very good book that I would recommend to anyone new to the field. It is a bit old by now and won't go into deep learning much.
Something more recent and in video format is the excellent class on neural networks given by Hugo Larochelle (Neural networks class - Université de Sherbrooke: http://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH). This will broach the subject of deep learning and many of the more recent advances.
Hope this helps!
Not really. The closest you'll get are Chris Bishop's book and Hastie, Tibshirani and Friedman. I've had a look at the textbook above and it's a rather poor one in my opinion. I prefer Bishop because of his emphasis on the probabilistic grounding, others like the way Hastie et al approach things, YMMV.
For a good bit of free material that's worth reading I strongly suggest parts IV and V in David Mackay's book (see page 'x' in the Preface for suggested readings for a Machine Learning/Bayesian Inference course).
I'd recommend you to read Bishop's PRML's Chapter 8, luckily this chapter is available on the web.
Agreed on ML. We used Alpaydin for the intro class and Bishop for the advanced class.
That's not a bad idea at all - I used EM way back (like 2002) for natural language processing, still remember it a bit, but dang gonna have to brush up :) Thx for the pointer!
Edit: Haha just realized I have that book! Recognized it from the cover shot on amazon :)
Pattern Recognition by Chris Bishop. There are other books which come from the same philosophical approach. I will edit in a list of other books when I get a chance. A very good book that is also available for free is David Mackay's Information Theory, Inference and Learning Algorithms.
Correct, thats the bachelor's I took... And continued on the Mathematical Modelling and Computing masters.
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In regards to books, if you intend on going with Machine Learning Christopher Bishop - Pattern Recognition and Machine Learning (2006 pdf version) is pretty much the bible. Its a bit expensive, but well worth the investment and goes through everything you will ever need to know.
It wont be able to replace course books, but will be just about the best supplement you can find.
And if going with Neural Networks (Deep Learning), basically look up George Hinton and start reading all his stuff (And Yann LeCun, but they often wrote together)
A lot of techniques in machine learning can be described from a Bayesian perspective, as evinced in one of the most popular textbooks in the field.
> it is still a current research topic
Absolutely, e.g. Bayesian nonparametrics among many many others.
The argument regarding the date that some underlying principles of Bayesian analysis were discovered is moot, it's like saying computer graphics is not that relevant since geometry originated in Euclid's works in 300 BC.
Would this book https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=mt_hardcover?_encoding=UTF8&amp;me=
Be good for someone with no knowlege of machine learning that wants to learn.
This:
http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
Thank you!! If you don't mind my asking, if you're working a full-time job, how much time have you been allocating for the program, and in how many months are you projected to finish?
Also, do you have any tips on how I can best prepare before entering the program? I'm considering reading the Elements of Statistics during commute instead of the usual ones I read and brush up on my linear algebra to prepare.
Discrete mathematics with ducks!
Contemporary Abstract Algebra
An Illustrated Theory of Numbers
Visual Complex Analysis
Number-Crunching
The Magic of Math: Solving for x and Figuring Out Why
The Princeton Companion to Mathematics
The Number Devil: A Mathematical Adventure
Professor Stewart's Cabinet of Mathematical Curiosities
Gödel, Escher, Bach: An Eternal Golden Braid
One Two Three . . . Infinity: Facts and Speculations of Science
Recently made a move and these are a few books on my shelf that stand out more.
There are so many beautiful mathematical images that could be chosen though and I do wish more publishers would have more creative book covers also.
First of all, let's establish something about the audience for whom I'm writing: People who have read and are reasonably familiar with Eliezer's great work of fan fiction Harry Potter and the Methods of Rationality.
Paragraph 1, while necessary in order to set up what follows, is mostly just silly, but already playing with little self-referential jokes. The self-consciously weird use of "isomorphism" is meant to remind readers of Gödel, Escher, Bach, which will come up again later. If the reader thinks that nonsensical phrases such as "All characters are fictional" are mistakes by a dimwitted author in a hurry: They are not. Read more Nabokov.
Godel, Escher, Bach: An Eternal Golden Braid
> By exploring common themes in the lives and works of logician Kurt Gödel, artist M. C. Escher and composer Johann Sebastian Bach, GEB expounds concepts fundamental to mathematics, symmetry, and intelligence. Through illustration and analysis, the book discusses how self-reference and formal rules allow systems to acquire meaning despite being made of "meaningless" elements. It also discusses what it means to communicate, how knowledge can be represented and stored, the methods and limitations of symbolic representation, and even the fundamental notion of "meaning" itself.
Read it. Trust me. There's also a course on this by MIT available here
Beyond the obvious choices, Watts' The Book, Ram Dass' Be Here Now, Huxley's Doors of Perception, Leary’s The Psychedelic Experience, and of course Fear and Loathing (all of these should be on the list without question; they’re classics), here are a some others from a few different perspectives:
From a Secular Contemporary Perspective
Godel Escher Bach by Douglass Hofstadter -- This is a classic for anyone, but man is it food for psychedelic thought. It's a giant book, but even just reading the dialogues in between chapters is worth it.
The Mind’s Eye edited by Douglass Hofstadter and Daniel Dennett – This is an anthology with a bunch of great essays and short fictional works on the self.
From an Eastern Religious Perspective
The Tao is Silent by Raymond Smullyan -- This is a very fun and amusing exploration of Taoist thought from one of the best living logicians (he's 94 and still writing logic books!).
Religion and Nothingness by Keiji Nishitani – This one is a bit dense, but it is full of some of the most exciting philosophical and theological thought I’ve ever come across. Nishitani, an Eastern Buddhist brings together thought from Buddhist thinkers, Christian mystics, and the existentialists like Neitzsche and Heidegger to try to bridge some of the philosophical gaps between the east and the west.
The Fundamental Wisdom of the Middle Way by Nagarjuna (and Garfield's translation/commentary is very good as well) -- This is the classic work from Nagarjuna, who lived around the turn of the millennium and is arguably the most important Buddhist thinker after the Buddha himself.
From a Western Religious Perspective
I and Thou by Martin Buber – Buber wouldn’t approve of this book being on this list, but it’s a profound book, and there’s not much quite like it. Buber is a mystical Jewish Philosopher who argues, in beautiful and poetic prose, that we get glimpses of the Divine from interpersonal moments with others which transcend what he calls “I-it” experience.
The Interior Castle by St. Teresa of Avila – this is an old book (from the 1500s) and it is very steeped in Christian language, so it might not be everyone’s favorite, but it is perhaps the seminal work of medieval Christian mysticism.
From an Existentialist Perspective
Nausea by Jean Paul Sartre – Not for the light of heart, this existential novel talks about existential nausea a strange perception of the absurdity of existence.
The Myth of Sisyphus by Albert Camus – a classic essay that discusses the struggle one faces in a world inherently devoid of meaning.
----
I’ll add more if I think of anything else that needs to be thrown in there!
Thanks for the conversation. I'd need to think this over some more before commenting further. Much appreciated.
And as for Hofstadter, he's a writer who deals with many topics but largely mathematics and philosophy. I especially recommend "Escher Godel and Bach". Some of your comments, in particular the Magritte painting, could be right out of his writing.
One book that i didnt see mentioned in a casual skim of the posts is Off to be the Wizard
https://www.amazon.co.uk/Off-Be-Wizard-Magic-2-0/dp/1612184715
A very silly series where a modern day guy ends up in an alternate dimension where he can do magic/control the world via programming. Super light reads, fun and funny, and pulls in your computer interest. If you enjoy the first one, you can pick up the others.
If you want something a bit meatier, check out some Douglas Hofstadter.
Le Ton Beau de Marot (it's in English) is about the process and problems of translating languages, and makes surprisingly good bathroom reading because the chapters are short. He starts the scope small, talking about whether to focus on literal meaning or the spirit of the words, and then brings in more concepts like artificial constraints (poetry, or even writing without certain letters, for one example). It is philosophical, informative, and amusing. https://www.amazon.co.uk/dp/B012HVQ1R0/ref=cm_sw_r_cp_awdb_L2sgAbDYFK1XK
He also wrote Godel Escher Bach: an Eternal Golden Braid. https://www.amazon.co.uk/dp/0465026567/ref=cm_sw_r_cp_awdb_b3sgAbQ79TTGS better writers than I have written reviews (this one is from Amazon)
>Twenty years after it topped the bestseller charts, Douglas R Hofstadter's Gödel, Escher, Bach: An Eternal Golden Braid is still something of a marvel. Besides being a profound and entertaining meditation on human thought and creativity, this book looks at the surprising points of contact between the music of Bach, the artwork of Escher, and the mathematics of Gödel. It also looks at the prospects for computers and artificial intelligence (AI) for mimicking human thought. For the general reader and the computer techie alike, this book still sets a standard for thinking about the future of computers and their relation to the way we think.
you should read this book
Gödel, Escher, Bach (Amazon, Goodreads, Wikipedia )
That thing is half typography, font, indentation, ... puns and metaphors. For example this holism/reductionism joke: http://i93.photobucket.com/albums/l76/orestesmantra/MU.jpg
I can't think of a way that could work on Kindle. You could try, and some things might not make sense, and some you might miss altogether, just because the font is different.
Not what I was thinking at all. Read these:
http://www.amazon.com/New-Kind-Science-Stephen-Wolfram/dp/1579550088
http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567/ref=sr_1_1?s=books&amp;ie=UTF8&amp;qid=1371004122&amp;sr=1-1&amp;keywords=godel+escher+bach
And try out this free online course:
http://www.santafe.edu/education/schools/sfi-mooc/introduction-complexity/
Read this... Godel, Echer, Bach, it's interesting.
>I'd argue that the idea that consciousness is non-material is our basic intuition.
And that is all you have to go on. Intuition is not a terribly reliable source of information about the nature of real reality. By intuition the sun rises in the east, travels across the heavens, and sets in the west while the earth remains stationary.
I pointed to books in links above that show the material basis for consciousness. I'm not going to be able to reproduce it here. But if you want to credit intuition there seems to be enough information about the universe being material and none about it being non-material to intuit that consciousness is also material.
Some Books:
I Am a Strange Loop; Godel, Escher, Bach; Philosophy in the Flesh; The Feeling of What Happens; Descartes' Error; Self Comes to Mind
>Holy shit, how many times do I have to say that I think that the physical brain plays a vital role in consciousness before you stop trying to argue as if I was asserting something to the contrary?
How many times do I have to say that physical brain is the only thing in evidence? If it is the physical brain and something, produce the "and something". I can produce the physical brain. So it seems my task is done and yours has yet to begin.
Do you have to demonstrate the non-material scientifically? Well of course you do. You say you can't, yet at the same time want it to be the controlling stuff. How can it do that with no connection? And if it has a connection to the material, then you should be able to study it scientifically.
Well, meta-think about this: a "strong AI" like ourselves might seem mysterious, but consider the fact that our brains are really just nueral nets, which can be mathematically approximated. The process of learning and thinking, in the neurological sense, is really nothing a blind search of your cells for optimal solutions. Using a fairly simple set of rules (See Conway's Game), complicated, intelligent behavior emerges. So I'm not really sure what you mean, but I'm guessing you're implying that intelligence cannot be designed, it must emerge. And don't forget that a method of discovering algorithms, is itself, still "just an algorithm!" Cornell's Eureqa project can do this, to a degree. I think my most important point is this: Intelligence is different between individuals and even animals. Don't judge machine intelligence by it's resemblance to the human mind. Both are machines but they process data in profoundly different ways.
PS http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567
some amazing books I would suggest to you are:
Charles Petzold.
All of these I would love to read again, if I had the time, but none more so than Godel, Escher, Bach, which is one of the most beautiful books I have ever come across.
Road to Reality is the most technical of these books, but gives a really clear outline of how mathematics is used to describe reality (in the sense of physics).
Code, basically, teaches you how you could build a computer (minus, you know, all the engineering. But that's trivial surely? :) ). The last chapter on operating systems is pretty dated now but the rest of it is great.
Pi in the Sky is more of a casual read about the philosophy of mathematics. But its very well written, good night time reading!
You have a really good opportunity to get an intuitive understanding of the heart of mathematics, which even at a college level is somewhat glossed over, in my experience. Use it!
Well, here's a really cool book.
GEB by Douglas R. Hofstadter
GEB
It is a strange loop.
(GEB being Hofstadter's Godel, Escher, Bach). I think that it might get a bit more buzz than it really deserves, but it is really a good book to read, as it brings up a number of computer science and philosophy problems like this one, makes you consider them. I'm not really a huge fan of the novel format, and some of the content went over my head, but if you like this sort of problem, I think that it presents a lot of similar-such issues. In GEB, the teleportation question is asked in the form of "replacement" -- that is, if I swap out one carbon molecule in you for another, have I made a new you? What about two molecules? Three? The whole thing?
You will notice the absence of A short history of nearly everything, which is a good book, but frankly it didn't speak very much to me. Bryson is almost only interested in geography.
For inspiration, at least read GEB -- http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567
I can't offer you a lot in the way of non-fiction. If you haven't read it, Gödel, Escher, Bach by Douglas Hofstadter is a good read. It is very dense and slow reading, but can be rewarding. If you like computer science, biology, math, or music in any combination, this could be a good book for you.
The secret to picking good non-fiction is to find something you're interested in or curious about and read a book about it. Things like neuro-linguistic programming, cryptography, riding horses, biking, running, cacti of the saguaro desert, Trees of the Eastern Forests, Scuba diving, Lockpicking, Prestidigitation (aka "magic tricks"), etc.
Of other books I've loved but could not mention in my top 3, I include:
That's all I can think of right now at work, but if you want more, PM me and I'll see what I can dig up.
You (meaning OP) may find useful either Smullyan's book http://www.amazon.com/Godels-Incompleteness-Theorems-Oxford-Guides/dp/0195046722 or Hofstadter's http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567 .
That depends on what you're interested in. There's a bunch of philosophy, psych books I could recommend but I'll go for some more general but still amazing books.
http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567
And
http://www.amazon.com/Flatland-Romance-Dimensions-Thrift-Editions/dp/048627263X
It seems that your comment contains 1 or more links that are hard to tap for mobile users.
I will extend those so they're easier for our sausage fingers to click!
Here is link number 1 - Previous text "GEB"
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^Please ^PM ^/u/eganwall ^with ^issues ^or ^feedback! ^| ^Delete
Sometimes depression isn't about circumstances or perspective, it's entirely chemical.
I've thought of the answers to these questions hundreds of times. But the thing about depression is that sometimes it makes it impossible to find a happy answer.
Here are my thoughts:
I'll try to keep the philosophical bit short, because I could really lose myself in a rant otherwise :p
I think that an existentialist philosophy can have a lot to offer on a human level. In a way, everything is functionally meaningless, in the sense that so much meaning is beyond our understanding. However, I think that rather than a complete lack of meaning in the universe, meaning is an inherent part of the universe.
To quote Hofstadter (taken from P-6 of Godel, Escher, Bach:
>Shouldn't meanings that one choooses to read into strings of meaningless symbols be totally without consequence?
>Something very strange thus emerges from the Godelian loop: the revelation of the causal power of meaning in a rule-bound but meaning-free universe.
Basically, by the way that matter is related to other matter, meaning emerges from even the tiniest connection. And that meaning can push matter around in the same way that matter can cause meaning. Ideas are not only meaningful, they have causative power. I think that's pretty cool!
So basically, I agree that humans may have insignificance on some scale. But in the grand scheme of things, there is something so much more magnificent that we are a part of.
Anyway...while I appreciate your thoughts and respect your desire to help others, I think that you are a bit misinformed. That's okay! It's nearly impossible for someone who hasn't experienced depression to know what it's like. But there are ways to better understand and help. Here is a great resource from /r/SuicideWatch that shares some ways that you can connect with depressed or suicidal people. I think it may help a lot!
Oh, and sorry for what turned out to be a philosophical rant anyways; I just can't resist invoking Hofstadter and isomorphism in the face of existentialism :p
One thing that's cool about math is that sometimes there are easily-understood examples for even the most mind-blowing stuff. In my last sentence I mentioned something that's (probably) true, but cannot be proven. Weird, right? Example:
"This sentence is true, but cannot be proven."
It's true, but go ahead and try to prove it. If this stuff interests you, you should take a look at this book.
> If it is about how we interpret our experiences, we can't but fall prey to confirmation bias.
When you're aware of confirmation bias, it is possible to overcome it. That is one of the most important parts of the scientific method: that it accounts for and eliminates the problem of confirmation bias. We can take lessons from the scientific method and eliminate our own confirmation bias in the same way. How we do this is by not trying to prove what we want to prove, but by trying our hardest to disprove it with objective tests, then have others check our work (like peer review).
> Assuming they existed, if you were to witness the sighting of a real ghost, you would likely interpret it as a wisp of fog.
Actually you're just projecting. I would not jump to any conclusions if I didn't know what it was. If it disappeared before I could investigate, the most I could conclude would be that it was something unexplained that disappeared before I could investigate it. I might say that it was probably a wisp of fog and I could back that up with evidence and reasoned arguments, but I wouldn't just jump to whatever conclusion happens to fit my beliefs best as you clearly would.
> It doesn't matter which view you favor, for confirmation bias to occur. It only matters that you favor one over the others and that this can influence your perception of the event.
Which is why, for important conclusions, we should make sure to eliminate confirmation bias using the methods I described above, or some other method that would also take account of it and correct for it. When we do this for any supernatural claims, they quickly fall apart or retreat to the untestable.
> You don't tell people who don't share your basic assumptions to look at things objectively, because it will mean different things to both of you.
I don't think you quite know what the word "objective" means. If it means different things to both of you, it's subjective, not objective.
> If you tell someone who believes in God to look at this objectively, it isn't an unfounded leap at all.
Yes it is. Asserting otherwise doesn't make it true. It does not logically follow from the fact that a dying man sees some light and feels calm that he is seeing an afterlife. There is no logical connection between the two. The only thing that connects them is pure speculation and wishful thinking.
> It is actually very reasonable: A belief predicts that after death my soul will go to heaven, a place filled with light, warmth, and joy. When I am close to death I experience a feeling of floating up, out of my body, and sensations just as predicted by my faith.
Actually I would be willing to bet that near death experiences far out-date religion. But even if not, there is a rather large disconnect between "Seeing some tunnel of light and feeling high" and "Seeing the afterlife". You are making a completely unfounded assumption (actually quite a lot of them), but you are too steeped in your own web of beliefs and superstitions to see it.
The absolute most you can reasonably conclude is that there is an unknown reason why people close to death experience what you described above. To conclude anything above that without demonstrable evidence is unfounded.
> Where a leap of faith is necessary, is at the level of basic assumptions: Belief in God and truth of the Bible. And nobody even disputes that...
They do, actually. Plenty of people, especially on Reddit, claim that their beliefs in God and the truth of the Bible are supported by evidence and reasoning, just as you are doing right now. Like you, they are too tangled in their own web of beliefs and confirmation bias to see that they are just making unfounded leaps.
> Welcome to the mind-body problem, and the deep waters of philosophy.
You need not welcome me. My girlfriend is pursuing a PhD in this very problem, and I myself have been interested in it for a long time. I've already heard and read far more about it than I could possibly digest. The more I've researched it, by the way, the more it seems to be true that there is no such thing as a separate soul or some entity that can survive the death of our physical brains. All of the evidence points away from such a thing. As a starting point, may I recommend Consciousness Explained by philosopher, Daniel Dennet. It attempts to provide a possible explanation of how consciousness is a distributed process in the brain with each part working together, rather than centered in some Cartesian theater. For a heavier read, I would recommend Gödel Escher Bach by physicist, Douglas Hofstadter. It explores the deep meaning that comes from recursion and self reference in art, music, mathematics, logic and in the world itself, and explains how consciousness could be the result of many layers of self referential recursive systems and structures.
Wow, awesome list, I've read way to many of those books.
You need to add G.E.B.; that book is amazing and fits in quite well on that list.
Currently only 1/4th through it, but so far it's the most amazing book I've ever read: Godel, Escher, Bach
Deep Learning (Adaptive Computation and Machine Learning series)
Hi, i see you already have read some good books. I would also recommend Bishop's pattern recognition book for ML https://www.amazon.ca/Pattern-Recognition-Machine-Learning-Christopher/dp/0387310738
However, not sure whether they teach this in grad stat . Good Luck !
&#x200B;
+1 as well: http://www.amazon.com/The-Elements-Statistical-Learning-Prediction/dp/0387848576/ref=pd_sim_b_1
You can legally download an electronic copy from Amazon. Amazon offers free Kindle reading apps on a number of platforms.
As far as I know, there isn't a legal PDF version available. If you look way over to the right, past the comments, you'll notice:
> Please follow the Stanford Honor Code, and please don't post any links to pirated versions of the textbook.
If you want to get into AI, learn a lot of probability. Read this if you can get your hands on it.
Depending on what you mean by "no formal training", I think it's important to realize that AI is a pretty advanced field within computer science. My undergrad specialization was in AI and I still know practically nothing; my senior year, I still had to deal with a lot of "beyond the scope of this course". I would avoid making that your first foray into programming, at least.
Here is the text that describes Kurzweil's vision. It is the only book I've ever read twice (besides the Bible, but I was confused then). I really suggest you pick it up - if money is an obstacle feel free to pm me your address and I'll send you a copy.
This book, is completely fascinating:
http://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111
Real answer: https://www.amazon.co.uk/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020
Docker or Kubernetes:
&#x200B;
Data Science, Machine Learning:
Hands-On Machine Learning with Scikit-Learn and TensorFlow
Python for Data Analysis
Python Data Science Handbook
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
I put together a list of resources on my blog.
That was an unsatisfying answer, I suppose. You want me to make a genuine play before we continue, fine, I'll give it a shot. But the supposed delineation between literal and symbolic is the first thing you'll have to discard. It is entirely wrongheaded. It's not even wrong. On one end a literal interpretation has Christians eat the body of Christ and drink his blood, and you end up painting them as vampire cannibals. Which was probably your agenda anyway. On the other, everything is symbolic and the new testament might aswell be referring to Jesus' covert war against clown reptiles. These answers are silly because the question is silly.
In the early 20th century, mathmetician Kurt Gödel set out to create his incompleteness theorem. The theorem was originally intended to show Russel and Whitehead's system for working with natural numbers as complete. This is important, because what use is an incomplete system? Not only did he end up proving the system as incomplete (humiliating two people he actually admired), he ended up proving no consistent system of logic can ever be complete, and vice versa. Neither can the system prove it's own axioms, any more than you can lift yourself by pulling your hair. Why is this crucial to a debate about religion? Because divinity is complete.
Source: Godel, Escher, Bach by Douglas Hofstadter.
So math and science arrive at what religion already knew. All is one. Duality is false. Symbolic and literal interpretations do not exclude one another, they show aspects of the same complete divine thing. They are shifts in perspective. An unsolved rubix cube does not disprove the solution. One implies the other.
And so the singular divine splits itself into male and female. One becomes two. And the coupling of two will beget a third. But duality is false, division is false. Man is fallible and incomplete, so he stifles the world through his ego. The benevolent king becomes the tyrant. The mighty creator becomes Holdfast, the enemy, the dragon.
It's been awhile since I've actually seen a bible, but as I recall the new testament does not start with Mary or God. It was Herod who called the census and set the whole thing in motion. The bad, unjust king strangles the land in an attempt to secure his reign. Thereby his actions create the very thing he fears most: the hero.
The tyrant-father is just a different face of the holy creator. When the arch-enemy holds the entire world in his stranglehold, new life springs from the void itself. The story of the savior is the story of every single human being. It shows the hero ascending the dominance hierarchy, dethroning the evil tyrant, slaying the dragon and reuniting with the divine. The hero's special weapon is the ability to tell good from evil in all their different guises. And again good and evil are just a perspective shift away from being truth and untruth. What difference does it make if the dragon is an actual dragon, mankind's sin, or the duality of all existance itself? All of it is symbolic. And in the sense that all of us are heroes, all of it is real.
Source: Hero with a thousand faces by Joseph Campbell.
>Clearly we can infer the Apostles and many early Christians believed that these things literally happened, and whether or not you think this is zealotry, the number of Christians who believe this literally is greater than those who see this symbolically.
This part brings a particular quote to mind, and besides there's a third book that I can show off as having read:
>The sun signifies first of all gold. But just as philosophical gold is not common gold, so the sun is neither just the metallic gold nor the heavenly orb...Redness, heat and dryness are the classical qualities of the Egyptian Set (Greek Typhon), the evil principle which, like the alchemical sulphur, is closely connected with the devil. And just as TYphon has his kingdom in the forbidden sea, so the sun, as sol centralis has its sea, its "crude perceptible water" and as sol coelestis its "subtle imperceptible water." This sea water (Aqua pontica) is extracted from sun and moon...
>
>We can barely understand such a description, contaminated as it is by imaginative and mythological associations peculiar to the medieval mind. It is precisely this fantastical contamination however that renders the alchemical description worth examining- Not from the perspective of the history of science, concerned with the examination of outdated objective ideas, but from the perspective of psychology, focused on the interpretation of subjective frames of reference...The alchemist could not sperate his subjective ideas from the nature of things, from his hypotheses (emphasis by prof. Peterson)...The medieval man lived in a universe that was moral- where everything, even ores and metals, strived above all for perfection.
Source: Maps of meaning by Jordan Peterson.
You definitely, definitely want to give him Gödel, Escher, Bach: An Eternal Golden Braid . If he's into mathematical thinking, this book will expand his mind in ways that no other book can. More than books that are directly about mathematics, Godel, Escher, Bach is an exploration of ideas and their relationships that will expand the way he thinks.
Reminds me of Godel Escher Bach: Eternal Golden Braid
Calculus is a bullshit excuse for mathematics. It's pure computation, and it's freaking useless. You have no idea what a formal proof is, or what an axiom is, or anything related to talking about math in an abstract, metamathematical fashion. Pick up a copy of Gödel, Escher, Bach and introduce yourself.
Abstract mathematics, or `pure mathematics', is a specific field of mathematics that spends time examining and proving theorems. It is, in some regard, the study of metamathematics - you use math to prove other math. Check out a basic explanation here.
Physicist here so don't pretend I don't know what science is. (Though like the ancient Pythagoreans I'm sure as soon as I discuss something that has been proven that goes against a purely scientific worldview out comes the pitchforks.) And though I love science, unlike some people here I am willing to admit to the limits of science. Science can lead to all truth in the same way that rational numbers define all numbers: it can't! and Godel proved it.
The real problem with science is that it has been mathematically proven by Godel that there are more things that are true then are provable and thus you can't ever have a scientific theory that can determine the truth or falsity of all things. As soon as you write down that theory, assuming it allows for arithmetic, Godel's incompleteness theorem immediately shows if the theory is true there will be true statements about reality that are beyond provability. Read Godel Esher Bach or Incompleteness or work through it yourself in this textbook as I have.
So like I said above, science is great in it's sphere (and in that sphere let me emphasize it is awesome!) but leads to all truth in the same way that rational numbers leads to all numbers. (And the analogy is precise since Godel used the famous diagonizational argument in his proof.) Russell and Whitehead set out to show in the early 1900s that if we could determine the axioms of reality then through logic work out everything that was true and Godel spoiled the party.
It it would be one thing if these truths were trivial things, but they are not. Some examples of true or false statements that may fall into this category of being unprovable are:
Now, at this point critics almost always tell me: but Joe, Godel's incompleteness theorem is only relative to your set of logic. (Ie... we can prove Goldbach by just adding axioms needed to do so.) Fine. But two things: (first) adding axioms to prove what you want willy nilly is not good science. (Two) You now have a new set of axioms and by Godel's theorem there is now a new uncountable set of things that are true (and non-trivial things like I listed) that are beyond proof.
Now usually comes the second critique: But Joe, this doesn't prove God exists. And this is true. But at least it has been proven God gives you a chance. It has been proven that an oracle machine is free from the problems that hold science and logic back from proving the truth of all things. At least something like God gives you a chance (whereas science falls short).
Or, like Elder Maxwell says so well: it may only be by the "lens of faith" that we can ever know the truth of all things. He maybe be right, and hence the importance to learn by study, and also by faith...
This really isn't a textbook, but it might as well be: Gödel, Escher, Bach
All of these suggestions are spot on, I can second Sipser's textbook, as well as Structure and Interpretation of Computer Programs.
>Ah, this is another troll tactic, the misleading statistic. Many DO disagree. Many MORE, at least twice as many, agree.
I personally tend to weight negative reviews a bit higher.
See the other book I named:
http://www.amazon.com/G%C3%B6del-Escher-Bach-Eternal-Golden/dp/0465026567
>Maybe you did not try asking non-inflammatory questions in the places where people are willing to answer, such as here and #lisp.
My memory of the chanop in #lisp (though this was years ago) was that he was exceedingly surly for no real reason. In fact, pretty much everyone there had a bug up their ass. There again, IRC in general has that same problem.
https://www.youtube.com/channel/UCmSLCkK6I5cONb-5N0v18Kw
Sub my channel, going to start an Advanced AI series very soon.
Other than that,
https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597/ref=sr_1_1?ie=UTF8&amp;qid=1498320096&amp;sr=8-1&amp;keywords=artificial+intelligence
This is a decent book, but I am not sure exactly what you are trying to find!
What about the 5 years part? That the next 5 years of technological advancements will be like the last 25? I didn't say in the next 5 years unemployment will be out of control... checkout the book The Singularity Is Near to learn more about that. Every technological advancement builds off of the last and helps the rest, i.e. self driving cars in the future making everything cheaper -> cheaper to do all kinds of scientific/medical research.
I can see how that was confusing, probably more like 20 years from now we'll absolutely have to have basic income or negative income tax. We'll have more people than ever with fewer jobs than ever!