(Part 2) Best products from r/artificial

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.

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:

• Deep Learning (training an ANN with 6 layers of hidden nodes): http://deeplearning.net/tutorials/
  
  
• Neuroevolution (evolving ANNs): http://www.cs.ucf.edu/~kstanley/neat.html
  
  
• Hierarchical Temporal Memory (pattern recognition leading to prediction): https://grok.numenta.com/htm-overview/education/HTM_CorticalLearningAlgorithms.pdf
  
  
• There's more approaches out there, but these are the popular ones nowadays
  
  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:
  
  
• On Intelligence: http://www.amazon.com/Intelligence-Jeff-Hawkins/dp/B000GQLCVE/
  
  
• How the Body Shapes the Way We Think: A New View of Intelligence: http://www.amazon.com/How-Body-Shapes-Way-Think/dp/0262162393
  
  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].

> Basically, just find a way to simulate a couple million cortical columns.

You should not confuse a "column" in HTM with a "cortical column" from a real mammalian cortex. We have reason to believe that a single cortical column is a feature detector. A cluster of columns act over milliseconds to suppress other, similar columns in a competitive manner. Inside real brains, a single cortical column is a highly interconnected group of neurons. (mostly fully connected).

You could take several hundred feature detectors as cortical columns. Then you can create singular cells that index a connection between two of them at a time. That is, the connecting neuron is active only when both of its constituent columns are active. These neurons that "index" two columns are called cortico-cortical neurons or CCNs. After the columns have "Settled down" from re-entry, the remaining active columns indicate the presence of a feature in the perceptive stimulus. The CCNs will be active if-and-only-if both of their indexed columns are still active. Let the number of columns be N. Then the number of CCNs is (N^2 - N)

Now for the tricky part. You index each CCN with a hippocampal neuron. The hippocampal neurons are in a fully-connected hopfield network. Given that a percept is a collection of co-occuring features, then the hopfield network will learn a robust pattern after exposure to it, as a collection of co-firings of the active CCNs. Here is a toy diagram of what you are trying to accomplish:



http://i.imgur.com/MSRwT4C.png


> Reward system : Nearly universally, learning is based on rewarding correct behavior and answers. How well would an infant learn that it needs to eat if it felt no hunger? How well does a neural network learn without some type of fitness system to keep it on track? For AI, you need some type of reward system.

This was already done by Rolf Pfeifer, Gerald Edelman, and again by Jeffrey Krichmar. In all cases, they did not use HTM networks or even "deep belief nets". Instead they modulated connections between various networks. Each network was associated with a modality and it was structured as a SOM, or Kohonen Self-Organizing Map.

• A diagram of Pfeifer's agent http://i.imgur.com/bTTUlAc.png
  
  
• Krichmar's book http://www.amazon.com/Neuromorphic-Brain-Based-Robots-Jeffrey-Krichmar/dp/0521768780
  
  
• Tutorial on SOMs. Make sure to click the barely-visible blue numbers at the button to move to next page. http://www.ai-junkie.com/ann/som/som1.html
  
  
  
• Krichmar's lab website http://www.socsci.uci.edu/~jkrichma/
  
  

Before my last year in college I sat down and implemented a A path finding program in C#. Not sure if that seems too simple or not for what you're thinking, but in it's own way, it was a great project. I found an "outline" of how A works and built from there.

Also, i've just recently started reading this book it's a great course on developing an RPG in C#, however, it also provides an awesome platform to develop your own AI for NPC's. i'm looking forward to creating a little NPC economy where they buy and sell to and from each other. (as well as developing AI for the monsters in the game) anyway, fun stuff!

So you are saying you want an antidote to Bostrom's poison? If you got ten pages into that book and you didn't see the immediate problem, ten pages of hand waving and misdirection where it is never clear what he means by "intelligence" because any smart reader already understands the concept (right?), well you probably need to read a little more history and a little more philosophy and then you can just laugh at his 200 pages of science fiction.

For a difficult but rewarding critique of the cognitivist movement, which began in the late 50's and which has been reinvigorated by the current AI fad, I would highly recommend The Mind's Provisions: A Critique of Cognitivism by Vincent Descombes. Read the introduction, then go ahead and skip to the chapters where he talks about Searle's Chinese Room and the Turing test.

Otherwise, if you just can't shake the image of a computer god taking over the world, you might as well get out your checkbook and join the cult. Oh, did you not realize it was a cult? Yeah, it is. It is the new Scientology.

You didn't provide much information about what you hope to achieve in your learning. You call yourself a non-scientist who knows little about computers. If you have a goal of fully understanding the application of AI to law eventually, then you need to have more than a little knowledge of computers in general as a foundation for studying AI. In that case, you would begin your learning by studying to learn more about computers in general. Then, you would learn about how to program computers. Then, you would learn about AI. And only then would you focus on how to apply AI to law.

However, if your goal involves merely getting a handle on how AI might apply to law, without necessarily understanding AI in detail, then you could start by reading something like A Legal Theory for Autonomous Artificial Agents. I haven't read that book and I know little about the application of AI to law. You might find a better book for your pursposes. I did a quick search on Amazon, suspecting to find something like it, because I felt certain that a substantial amount of work on applying AI to law has already happened. Here's a quote about the book.

“An extraordinarily good synthesis from an amazing range of philosophical, legal, and technological sources . . . the book will appeal to legal academics and students, lawyers involved in e-commerce and cyberspace legal issues, technologists, moral philosophers, and intelligent lay readers interested in high tech issues, privacy, [and] robotics.”
—Kevin Ashley, University of Pittsburgh School of Law

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

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

• Machine Learning (Murphy)
  
• Deep Learning Book (Goodfellow , Bengio)
  
  and to go w/ that
  
  
• All of Statistics (Wasserman)
  
• Information Theory (Mackay)
  
  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?)

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.

moscheles is refering to the embodied cognitive science perspective on intelligence. It is far beyond the scope of this post to summarize the whole debate about embodiment and intelligence but to give you a very VERY short version:
Parts of the artificial intelligence community (me included) argue, that intelligence is an observer-ascribed characteristic of an agent-environment interaction. This interaction requires the use of any form of sensoric input into the system and an ability to physically navigate in or manipulate the environment. I would also like to hint you to the chinese room argument and the symbol system argument. If you are interested in the basic assumptions and arguments of embodied cognitive science, you should take a look at understandin intelligence, a very good introduction to the field by pfeifer and scheier.

Downvoted this aswell, for the reasons provided by moscheles. imho the article as well as the project is a decent approximation to "recycling" human-produced symbol systems - but far from producing a system that comes any close to understanding the output it produces.


//edit: english grammar

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!

That book has now been added to my library, thank you. Link for anyone interested.

As far as the "It's only a really convincing simulation" goes:

If it looks like a duck, walks like a duck and quacks like a duck...
If a simulation is so convincing of faking his consciousness that there's nothing we could do (except maybe open up the soft- and hardware) to differentiate it from something we would consider conscious, then by all means it is conscious. I know I'm conscious, because I have my own thoughts to prove it to myself. But everyone else in the world might just be a clever robot. But it's senseless to assume this because it's not functional.

I think this argument ultimately comes down to "there's something special about us" rather than accepting consciousness too is 'just' a product of complex mechanics. As I mentioned somewhere else, the problem is we don't have a clear definition of what is conscious and can therefore not test for it.

What else does it do? What low-level function of the brain is incapable of being emulated ('consciousness' is not an answer; that is a high-level result of the low-level machinery)? What observed data breaks with the model I have described?

EDIT:

To expand upon this: mathematical models of single neurons and groups of neurons behave basically just as we would expect them to behave and just as we observe neurons behaving in the lab. Obviously no one has ever made a conscious mathematical model in silicon, paper, or anything else.

However, we observe that our brains are made of neurons (which we understand) and demonstrate consciousness (which we don't). We are left with two possibilities: either there's some 'magic' that makes consciousness happen to large groups of neurons but is more or less unrelated to the individual actions of the neurons themselves, or something in the structure of groups of neurons and groups of groups of neurons (and groups of groups of groups of neurons) creates the phenomenon that we call 'consciousness'.

The 'magic' hypothesis is unfalsifiable and I assign it very low probability. If you have religious or dualist inclinations then you might think it more likely or even certain. The 'composition' hypothesis is testable and almost certainly true. I don't just say this to discredit the magic hypothesis but because there are some serious hints that higher level structures of neurons can do some very interesting things.

If you are interested in this topic I cannot recommend Douglas Hofstadter's Gödel, Escher, Bach enough. It's almost 40 years old so a lot of the science is out of date, but it is genius in basically every meaning of the word and goes into much more detail about this debate between taking things holistically versus reductionistically.

A good intro book on calculus I found helpful was Calculus: A Physical and Intuitive Approach by Morris Kline. Jumping right into Spivak, while doable, is not for the faint of heart. (But one should definitely approach it eventually!)

Edit: spelling

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 episode was adapted from the short story of the same name by Alastair Reynolds. It actually was a young man working in tech who invented the cleaner robot in the original novel. But it was indeed a talented girl in the show though. Not so sure exactly why Netflix made this change, but the novel is a well-written collection of engaging separate short stories. Highly recommended.

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.

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

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 "AI"



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