(Part 2) Top products from r/artificial

I like both of the books that you mention, but Bostrom's Superintelligence is more about the impacts of a certain kind of AI that most researchers aren't even working on. Hawking's On Intelligence is probably nicer if you're interested in how AI (and the neocortex) might work, but you should realize that it's just one approach.

Ray Kurzweil's How to Create a Mind is also about reverse-engineering the brain. For an overview of the history of the field, I recommend checking out Nils Nilsson's The Quest for AI which has a free online web version (pdf). If you're more interested in the subfield of machine learning, you might also try Pedro Domingos' The Master Algorithm.

And how do you feel about undergraduate textbooks? Undergraduates are laymen before they start reading these and taking their courses, right? The AI textbook is Russell & Norvig's AI: A Modern Approach, but it's very extensive. Some lighter reading we used in one of my courses was The Essence of AI by Alison Cawsey, and from I remember it was fine, but when I was searching for it I also saw many more introductory books that I didn't read, but which might be better (and/or more recent). I just don't know. There's also a pretty good free online textbook by Poole and Mackworth.

Reading some books would be a good idea.

• Superintelligence: Paths, Dangers, Strategies by Nick Bostrom is a good introduction to some philosophy and general tech behind AI. It's really a lovely read.
  
  The following are textbooks:
  
  General AI
  
  
• Artificial Intelligence: A Modern Approach by Russell and Norvig is a conical book on AI. It has an AMAZING companion site ran by UC Berkeley
  
  
• Artifical Intelligence for Humans by Heaton has some overlap with Artificial Intelligence: A Modern Approach, but is also a good overview
  
  Machine Learning
  
  
• Deep Learning by Goodfellow, Bengio, and Courville is a great book on deep learning
  
  
• Introduction to the Math of Neural Networks by Heaton is a great book on the math behind neural networks
  
  
• Make Your Own Neural Network by Rashid is a good guide to making your own neural network
  
  
• Reinforcement Learning: An Introduction by Sutton and Barto
  
  Statistics for Machine Learning
  
  
• Machine Learning: A Probabilistic Perspective by Murphy
  
  
• The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman
  
  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.

My PhD thesis was on some of the core challenges with integrating a model of emotion (based on appraisal theory) with general AI like cognitive architectures.

Yes! The first two points reflect what others have stated that (and I think are spot on) and I'll introduce a 3rd point.

1. There's no reason to believe any process of the human brain cannot be captured as AI. This would only be challenged by ideas such as dualism, which most of modern neuroscience has abandoned.
   
   
2. Intelligence is useless without emotion - An important reason for this that has been mentioend is motivation. It doesn't stop there though. Based on Antonio Damasio's Somatic Marker Hypothesis, we believe emotions are fundamental to all rational thought, serving as a mechanic for dealing with limitless information to process. Think of it as generalized +/- information that serves as a heuristic to other rational processes.
   
   The exact nature of this is still under active investigation, but it's at least worth noting that evolution has developed emotion as a central aspect of our thinking for some reason. It also appears to be present in many other animals (though if that's true is up for debate), and its clear that those with impaired emotional processes cannot make complex decisions rationally.
   
   
3. What doesn't seem mentioned yet is a work done by the Affective Computing group at MIT's Media Lab: http://affect.media.mit.edu/ . In contrast to my work which seeks to synthesize emotions in AI first, they're more focused on giving computers the ability to perceive and display emotions. One of the major roles of emotion happens to be social communication (i.e., we don't just have emotion, but we also express it as a way of communicating information to others).
   
   In the simplest of cases, perhaps AI should understand when it does something you don't like by being able to detect when you're pissed off. More broadly, having an ability to understand and express emotion will do things like allow for an emotionally visceral experience while speaking with a robot, allow an automated customer service robot to understand when you are angry and thus change strategy (like route you to a live manager), or help older lonely patients feel like they're still needed in the world.
   
   ---
   
   In summary how it affects us is 2 ways:
   
   
4. Enable more general intelligent robots to be embedded in our world
   
   
5. Impove AI and human interactions

If you're interested in natural language processing, learning Prolog would be a great start. It's very different from most other languages, but its structure makes tokenizing, tagging, parsing, etc. super simple once you get comfortable with it. I used this book to learn Prolog in a class. It's written by a computational linguist (Covington). He also has another book specifically about NLP, but it is out of print and thus quite expensive.

I'm currently reading Apocalyptic AI: Visions of Heaven in Robotics, Artificial Intelligence, and Virtual Reality by Robert M. Geraci. This book explores how religious ideas have infested our expectations for AI. It's arguments are quite similar to The Secret Life of Puppets by Victoria Nelson which was an even deeper consideration of the metaphysical implications of uncanny representations of human beings whether in the form of dolls, puppets, robots, avatars, or cyborgs. I think it is really important to understand what is driving the push for this technology.

Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat is also a good book on the dangers of AI.

You want more book recommendations? Well, one of the creepiest aspects of AI is that Amazon is using it for its recommendation engine. So just go on Amazon and it will be an AI that recommends more books for you to read!

Computational Intelligence: An Introduction

By Andries P. Engelbrecht

Should cover your question thoroughly.

A very rough short answer to your question would be that modern AI is roughly divided into Optimization(evolutionary algorithms, particle swarm optimisation, backpropagation, etc.) and I/O processing Models(neural networks, deep neural networks, genetic programming languages, etc.)

These are interrelated; the models do the task(such as recognizing cats or playing flappy bird) and the optimisation methods modify the free parameters of the model to match some goal/objective/fitness function.

Outside of that, the other subfields of AI are (generally) more isolated and narrow in problem solving ability.

Sorry, but that's not really accurate. I spend a year working in a neuroscience lab researching arithmetic cognition, and it's pretty well established that animals have an inborn sense of numerical magnitude. I would recommend taking a look at Stanislas Dehaene's book The Number Sense.

> There are genetic algorithms used for optimizations and there are neural networks used for learning. Both ideas are taken from biological evolution. But as far as I could determine no one really tried combining them.

They have been combined.

http://www.amazon.com/Bio-Inspired-Artificial-Intelligence-Technologies-Intelligent/dp/0262062712/

> Is it computationally possible to use genetic algorithms to evolve neural network seeds much like biological brains evolve in nature?

Yes.

• http://eplex.cs.ucf.edu/hyperNEATpage/HyperNEAT.html
  
  
• http://gral.ip.rm.cnr.it/nolfi/papers/nolfi.perac.pdf
  
  
• (.. more furnished upon request)
  
  > Is it currently computationally possible to virtually emulate the entire process of biological evolution for AI?
  
  As prof_eggburger pointed out there is dissension in the ranks regarding what the process of evolution is really doing. Normally we like to think that biological evolution is a straight-shot royal road to intelligence. This belief might be more a reflection of our "Victorian Attitudes" to life on earth, rather than couched in empirical science.
  

• reddit is not marshallp's personal blog for blabbing away about his own independent research.
  
  
• Episodic Memory is understood and clearly defined in all modern neuroscience texts.
  
  
• Chapter 6.1 from the Cognitive Computing Research Group elaborates at length about the role played by Episodic Memory in Action Selection and Attentional Awareness.
  
  
  
• Episodic memory is a major topic in the neuroscience of memory of laboratory mice. And numerous experiments have been performed showing that the hippocampus plays a major role in the processing of Episodic Memory of mice.
  
  
• Ben Goetzel has spoken about Episodic Memory in lectures and has written about it in recent blog posts. By "recent" I mean in the last 2 months.
  

"Big Brother is watching you." - George Orwell

I would say I disagree with this and that it's even unnerving but the reality is that is the world we live in now. If you haven't read George's 1984 you should. Very interesting parallels to our society today (globally, not just in the U.S.).

https://www.amazon.com/1984-Signet-Classics-George-Orwell/dp/0451524934

Regarding the machine learning in atmospheric sciences, a good overview is the Cloud Computing in Ocean and Atmospheric Sciences. In terms of ML in general see our sidebar.

Comments lie, code does not. If you can't name your classes, methods and variables in a way that I know what you are doing, then I'm not going to approve your pull request.

Also, if the pr comes from a junior dev or new hire, I will buy them a copy of this book:

https://www.amazon.com/Clean-Code-Handbook-Software-Craftsmanship/dp/0132350882/ref=pd_lpo_sbs_14_img_0?_encoding=UTF8&psc=1&refRID=2GRKWCXC0MEEKXJN49HK

which explains, rather eloquently, why comments are not a good thing.

(caveat: rarely, there needs to be a "why I did this" comment, and that's OK. That's not what I am talking about.)

I don't know what how comfortable you are with academic philosophy, but this is what I'd read https://www.amazon.com/Philosophy-Artificial-Intelligence-Oxford-Readings/dp/0198248547

It's a collection of papers on A.I.

Bishop's book is a classic

http://www.amazon.com/Neural-Networks-Pattern-Recognition-Christopher/dp/0198538642

It's more practical/numerical than neuroscience based. I don't think he gets in to the recurrent stuff. Frankly recurrent NNs are a bit of a boondoggle and have never justified their extra complexity for solving real problems.

If you want to look in to recurrent nets, I'd recommend reading some early connectionist stuff. Perhaps this:
http://www.barnesandnoble.com/w/unsupervised-learning-geoffrey-hinton/1100658986?cm_mmc=googlepla-_-textbook_notinstock_26to75_pt99-_-q000000633-_-9780262581684&cm_mmca2=pla&ean=9780262581684&isbn=9780262581684&r=1

Most of the modern AI books are more theory than algorithms and source code. Even worse they require a master's level of math just to understand them.

I had to buy this book from Amazon:
http://www.amazon.com/Artificial-Intelligence-Using-Herbert-Schildt/dp/0078812550

It was used for 1 cent and then $3.99 for shipping for a total of $4

Modern AI stuff is too complex and requires high level math, so I had to dig deep into 1987-1990's AI books to be able to start my learning.

I get attacked and bashed for not having a master's level knowledge of math required for these online courses like Linear Algebra or Advanced Statistics and Probability. So it turns me off these online resources.