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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.

This may vary by school, but it's been my experience that there aren't a lot of classes explicitly labeled as "artificial intelligence" (especially at the undergraduate level). However, AI is a very broad and interdisciplinary field so one thing I would recommend is that you take courses from fields that form the foundation of AI. (Math, Statistics, Computer Science, Psychology, Philosophy, Neurobiology, etc.)

In addition, take advantage of the resources you can find online! Self-study the topics you're interested in and try to get some hands on experience if possible: read blogs, read papers , browse subreddits, program a game-playing AI, etc.

Given that you're specifically interested in reasoning:

• (From the sidebar) AITopics has a page on reasoning with some recommendations on where to start.
  
  
• I'm not an expert in this area but from what I've been exposed to I believe many of the state-of-the-art approaches to reasoning rely on bayesian statistics so I would look into learning more about it. I've heard good things about this book, the author also has some lectures available on youtube
  
  
• From what I understand, whether or not we should look to the human mind for inspiration in AI reasoning is a pretty controversial topic. However you may find it interesting, and taking a brief survey of the psychology of reasoning may be a good way to understand the types of problems involved in AI reasoning, if you aren't very familiar with the topic.
  
  
  *As a disclaimer: I'm fairly new to this field of study myself. What I've shared with you is my best understanding, but given my lack of experience it may not be completely accurate. (Anyone, please feel free to correct me if I'm mistaken on any of these points)

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].

To gain a good overview of AI, I recommend the book The Master Algorithm by Pedro Domingos. It's totally readable for a layperson.

Then, learn Python and become familiar with libraries and packages such as numpy, scipy, and scikit-learn. Perhaps you could start with Code Academy to get the basics of Python, but I feel like the best way to force yourself to really know useful stuff is through implementing some project with a goal.

Some other frameworks and tools are listed here. Spend a lot more time doing than reading, but reading can help you learn how to approach different tasks and problems. Norvig and Russell's AI textbook is a good resource to have on hand for this.

Some more resources include:

Make Your Own Neural Network book

OpenAI Gym

CS231N Course Notes

Udacity's Free Deep Learning Course

I find that more threatening than promising. I recently re-read Blindsight and Echopraxia by Peter Watts. One of his main themes is that transhumans and AIs are making scientific advances that are so far out there that "baseline" humans can't even understand what they're talking about.

The interesting non-fiction book Our Final Invention also touches on this at some length. We might get ever-more amazing discoveries, but the price would be that we really don't know how anything works. We would be as children, taking everything on trust because we're not smart enough to understand the answers or contribute to the conversation. But this presupposes that the AIs or augmented intelligences would be vastly smarter than us, not just tools we ourselves use to ask better questions. Who knows. But an interesting set of questions, in any case.

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?)

I highly recommend this book if you want to read up on some thought experiments around AGI. Spoiler alert: not great for mankind.

It's easy to come up with a lot of different ways a AGI plays out if one of it's main goals is to save the environment (alone with some other reasonable assumptions about it's ability o navigate in the world).

There is lots of low hanging fruit that humanity could do tomorrow to dramatically help the planet, be we are all selfish assholes, so we don't.

An AGI would/could basically pull us into a post scarcity economy by automating everything. It could then stick and carrot humanity into not destroying planet.

Not eating meat and eliminating private car ownership would go a long way to save the environment. Throw in free birth control/paying people to not have kids takes care of population growth.

But like the other commenter says, we just don't know.

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

> People are downvoting in this case because it is irrelevant spamming of your own project while the topic is about Google's project.

Sorry, with due respect, well-known AI devotee Don_Patrick, but discussion of Google's project warrants the sharing of an opinion that Google is not properly going about True AI. By the way, Wotan is my German-language Forth AI, about which I have written Artificial Intelligence in German as a cheap Kindle e-book.

> So can it do word sense disambiguation? If so, wouldn't it be good to demonstrate that performance in a comparison with other algorithms? Or can it just not do it?

The Ghost Perl AI can not yet do "word sense disambiguation", because I have focussed on getting the most basic AI Mind up and running. But lately I have been realizing that I could maybe disambiguate words (such as "book a flight" or "purchase a book") by letting the already-present parameters of noun-or-verb play a role in the "word sense disambiguation."

> just demonstrate it through its performance on benchmarks that other algorithms have been tested on.

The only benchmark I'm interested in is thinking. I am also trying to create jobs for Perl programmers at Ghost AI installations, and book-sales for authors who write about the new, Perl third-generation AI -- after first-generation Mind.REXX in 1994 Amiga ARexx and Mind.Forth as described in 1998 by the Association for Computing Machinery (ACM).

Today I have spent several hours writing and first-time
uploading http://ai.neocities.org/var.html which is a Table of Variables to further explain the Perl Mind Programming Journal by providing an on-line reference for each variable. Thanks, everybody, for the constructive criticism, and onwards to the Perl AI Singularity. -ATM

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":

• https://www.reddit.com/r/artificial/comments/1rg206/where_should_i_start_if_i_want_to_get_into_ai/
  
• https://www.quora.com/How-should-one-start-studying-Artificial-Intelligence-if-he-is-a-college-sophomore
  
• https://www.quora.com/What-are-some-good-books-on-Artificial-Intelligence
  
• https://www.quora.com/What-math-do-I-need-to-learn-for-AI-and-ML-Can-you-recommend-some-good-books-courses-for-beginners
  
  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

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!

The Artificial General Intelligence (AGI) mail-list has the same topic at http://www.listbox.com/member/archive/303/2013/10/. In response, please be advised that some Strong AI Minds in English, German and Russian are still in the "text-based" phase because they have not yet been embodied in robots with visual recognition inputs. These AI Minds do handle "simple context-sensitive language" when they engage in automated reasoning with InFerence as described by an Amazon e-book in Brazil, Canada, France, Germany, India, Italy, Japan, Mexico, Spain, United Kindgom and the United States.

The book is FREE in every country, not just on amazon.com (USA). You can try searching for the book title on your local country site or use one of the direct links below.

US - (link is in original post above)
UK - https://www.amazon.co.uk/dp/B075882XCP
India - https://www.amazon.in/dp/B075882XCP
Japan - https://www.amazon.co.jp/dp/B075882XCP

Australia - https://www.amazon.com.au/dp/B075882XCP
Brazil - https://www.amazon.com.br/dp/B075882XCP
Canada - https://www.amazon.ca/dp/B075882XCP
Germany - https://www.amazon.de/dp/B075882XCP
France - https://www.amazon.fr/dp/B075882XCP
Italy - https://www.amazon.it/dp/B075882XCP
Mexico - https://www.amazon.com.mx/dp/B075882XCP
Netherlands - https://www.amazon.nl/dp/B075882XCP
Spain - https://www.amazon.fr/dp/B075882XCP

Please upvote if this was helpful so others can find it.

Thank you.

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 :)

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".

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Hofstadter had this to say on the importance of Bongard problems in What are A and I?:

>... It is clear that in the solution of Bongard problems, perception is pervaded by intelligence, and intelligence by perception; they intermingle in such a profound way that one could not hope to tease them apart. In fact, this phenomenon had already been recognized by some psychologists, and even celebrated in a rather catchy little slogan: "Cognition equals perception"...
>
>...Sadly, Bongard's insights did not have much effect on either the AI world or the PR [pattern recognition] world, even though in some sense his puzzles provide a bridge between the two worlds, and suggest a deep interconnection. However, they certainly had a far-reaching effect on me, in that they pointed out that perception is far more than the recognition of members of already-established categories--it involves the spontaneous manufacture of new categories at arbitrary levels of abstraction. As I said earlier, this idea suggested in my mind a profound relationship between perception and analogy-making--indeed, it suggested that analogy-making is simply an abstract form of perception, and that the modeling of analogy-making on a computer ought to be based on models of perception...

It is unfortunate that Hofstader's insight on Bongard's insights still hasn't had much effect on the AGI world (AFAIK, no mention on the opencog group) or the ML [machine learning] world!

BTW, Hofstadter has expanded the latter portion of the 2nd paragraph above into a 500 page book published just last month: Surfaces and Essences: Analogy as the Fuel and Fire of Thinking. Has anyone here read it?

Hofstadter has expanded that idea into a 500+ pg book, Surfaces and Essences: Analogy as the Fuel & Fire of Thinking.

This view also seems to be gaining a foothold in the computer vision community. I recall a recent talk by a UC Berkeley professor specializing in CV, Alyosha Efros, IIRC, the main theme of which was: Ask not "what is this?", ask "what is this like?"

BTW, Bongard problems seem like a far better test for intelligence than the vague Turing test.

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.

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.

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.

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)

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).

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.

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.

http://www.amazon.com/Artificial-Intelligence-Modern-Approach-Edition/dp/0136042597 is what I (and probably most others) would recommend as an introductory book.

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.

If you work hard enough at it, and spend the time necessary (years usually), you can learn anything. If you're interested, this is the book that originally got me into neural networks: https://www.amazon.com/Mind-within-Net-Learning-Thinking/dp/0262194066/ref=sr_1_1?ie=UTF8&qid=1499609914&sr=8-1&keywords=the+mind+within+the+net

It's written for a general audience and is, for once, not focused on the mathematical descriptions of ANNs (not that there's anything wrong with that), yet goes into extremely useful detail about basic NN architectures.

I recommend you read Superintelligence. It answers this kind of question and more. Not an easy read, but not too hard either.

A book that should be linked here.

http://www.amazon.com/How-Body-Shapes-Way-Think/dp/0262162393/ref=cm_cr_pr_product_top?ie=UTF8

Try this https://www.amazon.in/dp/B075882XCP

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&ie=UTF8&qid=1487948083&sr=1-1&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.