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Reddit mentions of Artificial Intelligence: A Modern Approach (3rd Edition)

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Reddit mentions: 73

We found 73 Reddit mentions of Artificial Intelligence: A Modern Approach (3rd Edition). Here are the top ones.

Artificial Intelligence: A Modern Approach (3rd Edition)
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Found 73 comments on Artificial Intelligence: A Modern Approach (3rd Edition):

u/zorfbee · 32 pointsr/artificial

Reading some books would be a good idea.

u/abstractifier · 22 pointsr/learnprogramming

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.

u/jschm · 19 pointsr/compsci

AIMA. A real treasure trove!

u/ZeljkoS · 18 pointsr/philosophy

Author here. Let me start:

First software company I founded develops software components for other programmers:

Our customers include NASA, MS, Intel, and US Navy:

Second company I co-founded screens programmers before interviews:

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:

I have also been part of Croatian team at IOI in Capetown:

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:

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:


Every article should be judged by its arguments, not the credibility of the author.

u/hwillis · 16 pointsr/Physics

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.

u/sandsmark · 16 pointsr/artificial

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

u/lukeprog · 10 pointsr/Futurology

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.

u/SomeIrishGuy · 9 pointsr/IWantToLearn

Artificial Intelligence: A Modern Approach is a commonly used introductory textbook.

u/galahadredgrave · 9 pointsr/ArtificialInteligence

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.

u/FranciscoSilva · 8 pointsr/computerscience

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!

u/shaggorama · 8 pointsr/learnpython

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.

u/Xiroth · 7 pointsr/compsci

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.

u/TranshumanWarrior · 6 pointsr/slatestarcodex

> 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.

u/[deleted] · 5 pointsr/compsci

I don't have a lot of AI experience, but depending on the size of the square, it seems like you could conceivably just brute force it and find the best solution that way.

Finding a solution through a clever method might be trickier. You could probably implement some fairly simple heuristics, such as finding which type of blocks there are the most of us and eliminating the rest. If you were feeling more adventurous some sort of supervised or unsupervised learning algorithm might be interesting, although you probably don't have the experience for something like that. As far as AI textbooks the only one that I've heard recommended is the standard: AI: A modern approach, which I don't know how useful it would be to you.

u/Artaxerxes3rd · 4 pointsr/Futurology

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

u/sciencifying · 4 pointsr/compsci

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.

u/mhatt · 4 pointsr/compsci

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).

u/Soupy333 · 4 pointsr/Fitness

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&ie=UTF8&qid=1341852604&sr=1-2&keywords=machine+learning

That second book is a little older, but all of its algorithms/techniques are still relevant today.

u/anon35202 · 4 pointsr/artificial

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.


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.


u/a_bearded_man · 4 pointsr/AskEngineers

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.

u/nimblerabit · 3 pointsr/compsci

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:

u/LastMan0nMars · 3 pointsr/AskProgramming

I can recommend this (free) course:

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&qid=1506722436&sr=8-2&keywords=artificial+intelligence+a+modern

and by the way check out this thread maybe:

u/weelod · 3 pointsr/artificial

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?)
u/blindConjecture · 3 pointsr/MachineLearning

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

u/KnightOfDark · 3 pointsr/artificial

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.

u/Rigermerl · 3 pointsr/rmit

I think they use this:


Decent book (the bible for AI apparently).

u/groundshop · 3 pointsr/artificial

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)

u/paultypes · 3 pointsr/programming

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.

u/eatsleepravedad · 2 pointsr/philosophy

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.

u/lasthope106 · 2 pointsr/ECE


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:


u/dmazzoni · 2 pointsr/learnprogramming

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.


u/Dansio · 2 pointsr/learnprogramming

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.).

u/ProfessorAlgorithm · 2 pointsr/AskComputerScience

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)

u/sketerpot · 2 pointsr/IAmA

The standard introductory AI textbook is Artificial Intelligence: A Modern Approach, by Russell and Norvig. It can be a bit heavy, though.

u/sarahbau · 2 pointsr/artificial

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.

u/yturijea · 2 pointsr/learnprogramming

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)

u/just-an0ther-guy · 2 pointsr/sysadmin

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:

u/GreyMX · 2 pointsr/artificial

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.

u/scohan · 2 pointsr/compsci

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.

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.

u/Psygohn · 2 pointsr/cscareerquestions

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:


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.

u/loniousmonk · 2 pointsr/askscience

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

u/stevelosh · 2 pointsr/programming

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/>, 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&qid=1317855027&sr=8-1

Edit: Dear Markdown, you can be a dick sometimes.

u/interblag1 · 1 pointr/OMSCS


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...

u/TonySu · 1 pointr/learnprogramming

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.

u/mucus · 1 pointr/learnprogramming

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.

u/Rhawk187 · 1 pointr/television



If you want to say "wikipedia doesn't count", try reading this:


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.

u/pipocaQuemada · 1 pointr/learnprogramming


u/j6keey · 1 pointr/computerscience

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

u/andreyboytsov · 1 pointr/MachineLearning

Classic Russel & Norwig textbook is definitely worth reading. It starts from basics and goes to quite advanced topics:
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:
(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!

u/Pallidium · 1 pointr/artificial

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).

u/solid7 · 1 pointr/learnprogramming

> 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.

u/samakame · 1 pointr/art_int

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.

u/IllIIIlIlIlIIllIlI · 1 pointr/artificial

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

u/white_nerdy · 1 pointr/learnprogramming

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.

u/unitmike · 1 pointr/aiclass

(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?

u/khafra · 1 pointr/philosophy

> 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.

u/caverts · 1 pointr/worldnews

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...

u/beyond-antares · 1 pointr/learnprogramming

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

  1. Automate the Boring stuff - Al Sweigert

  2. Hitch hikers guide to Python

  3. Dive into python

    Great resources can be found here:

    The next step is to get a brief grasp of data science. You can learn these from:

  4. www.datacamp.com for Python and R

  5. Coursera course on data science

  6. Udemy courses in Python and R (Note these would most likely be paid courses so wait for the monthly discounts to kick in to purchase them for $10-$15)

    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.


  7. Udacity - Introduction to Artificial Intelligence (standford course)

  8. Coursera - Andrew Ng's Deep Learning specialization course. Note the course uses octave which is similar to Matlab style programming. The courses when accessed individually are for free or you can pay for a certification.

  9. Various lectures on youtube for MIT and Stanford's Artificial Intelligence courses.

  10. A really good text book to check out is Artificial Intelligence - A modern Approach. AI was traditionally scripted in Lisp or prolog. This has been coverted into Python over here


  11. Krill Ermenko - AI, Machine Learning and DEEP Learning from A-Z

  12. Fast.ai Dives into keras a top level library
u/csreid · 0 pointsr/IAmA

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.

u/stanfordai · 0 pointsr/aiclass

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.

u/aafirearrow · -2 pointsr/gamemaker


Sub my channel, going to start an Advanced AI series very soon.
Other than that,


This is a decent book, but I am not sure exactly what you are trying to find!