Reading Guide To AI Design & Neural Networks?

Raistlin84 writes "I'm a PhD student in theoretical physics who's recently gotten quite interested in AI design. During my high school days, I spent most of my spare time coding various stuff, so I have a good working knowledge of some application programming languages (C/C++, Pascal/Delphi, Assembler) and how a computer works internally. Recently, I was given the book On Intelligence, where Jeff Hawkins describes numerous interesting ideas on how one would actually design a brain. As I have no formal background in computer science, I would like to broaden my knowledge in the direction of neural networks, pattern recognition, etc., but don't really know where to start reading. Due to my background, I figure that the 'abstract' theory would be mostly suited for me, so I would like to ask for a few book suggestions or other directions."

Comments

PDP

[kahizonaki]

Parallel Distributed Processing (both books) by Rumelhart, McClelland, and the PDP research group, 1986. "THE" classic neural network resource--and still somewhat relevant.

Re:PDP

[agravier]

For a somewhat more up-to-date and maybe complementary book, I advise you Computational Explorations in Cognitive Neuroscience by Randall C. O'Reilly and Yuko Munakata (The MIT Press). The simulator intends to extend and replace PDP++ and is quite pleasant to use. It is on http://grey.colorado.edu/emergent/index.php/Main_Page

Re:PDP

[Schwarzchild]

Cosma Shalizi is also a Physicist. I don't think he is actually doing research in machine learning or AI but he likes to read a lot and he tends to have fairly extensive reading lists.

Machine Learning

AI

You may also want to get familiar with Geoffrey Hinton's current work in neural networks.

AIMA

[omuls are tasty]

Artificial Intelligence: A Modern Approach by Rusell and Norvig is more or less the standard AI textbook and the book I'd suggest to get an overview of AI and its different methodologies. Mind you, it's over 1000 pages, but a very interesting read.

Re:AIMA

[Anonymous Coward]

I'd like to add to this. AIMA gives you a very broad and moderately deep overview of the state of AI ten years ago. As such, it is a truly excellent introduction introduction to the subject.

If you want a more recent, much more thorough and narrow introduction to neural networks in particular and machine learning in general, I'd recommend Chris Bishop's book: Pattern Recognition and Machine Learning (http://research.microsoft.com/~cmbishop/prml/), which focuses on learning rather than searching and planning. An outstanding more broad, shallow and dated book on machine learning is Tom Mitchell's book, Machine Learning (http://www.cs.cmu.edu/~tom/mlbook.html)

(Posting AC for the obvious reason that I can't be bothered to create an account)

Re:AIMA

[Yvanhoe]

Agreed. All the basic knowledge about the field is in this book. Part of these are available freely online. You can be judge : http://aima.cs.berkeley.edu/

Re:AIMA

[six11]

Also seconded. Russel & Norvig. Artificial Intelligence: A Modern Approach is a good book, well illustrated, and generally lacks the undecipherable academia-speak that pervades lots of AI literature.

Here's an article that was particularly influential on me and some of my friends: Brooks, R. 1991. Intelligence Without Reason. MIT AI Memo num 1292. Even though it is 'just' a tech report, it is frequently cited. He had another one, Intelligence without Representation, which is also good.

Somebody else mentioned the McClelland and Rumelhart PDP (neural networks) book, and it is also still quite good in spite of its age.

The interesting thing about AI (to me) is the funny mix of domain expertise. You have philosophers, sociologists, cognitive scientists, psychologists, computer scientists, and mathematicians. That's not a complete list---I'm in human-computer interaction and design research.

But because of the motley crew of domains you have a hundred people speaking a hundred different dialects. Some people put everything in really mathy terms, and their journal articles look (to me) like they are written in Klingon. Then you have others who write in beautiful prose but don't give any specifics on how to implement things. Still others express everything in code or predicate logic.

The oldest school of AI holds that you can reduce intelligence to a series of rules that can operate on any input and make some deterministic and intelligent sense of it. That works to a degree, but it falls apart at some point partly because of the computational complexity (e.g. the algorithm works if you have a million years to wait for the answer). Another reason it falls apart is because there are some kinds of intelligence that can't be reduced to rational computation (e.g. I love my wife because of that thing she does...).

There's a newer kind of AI that is based on having relatively simple computational structures that eat lots of data, "learn" rules based on that data, and are capable of giving fairly convincing illusions of smartness when given additional data from the wild. Neural nets fall into this category.

A third kind of AI brings these two schools together in the belief that there are fundamental computational structures like Bayesian Networks that can model intelligence* but those structures by themselves are insufficient and must be able to adapt based on exposure to real data. So instead of having a static BN whose topology is defined at the start and remains the same throughout the life of the robot, we can have a dynamic BN whose structure changes based on the environment.

I remember reading a recent article by John McCarthy arguing that all this statistical business is hogwash, and that the old school positivist, reductionist approach will eventually win. He's a smart guy, inventor of LISP and a Turing Award recipient. It seems his view is in the minority, but I'm not one to say he's wrong. However, my inclination is that the third hybrid group is probably going to be the one to make the most progress in the years to come.

The reason for my preference to the hybrid school could probably be best explained by Lucy Suchman's Plans and Situated Actions . I can't really do her thesis justice in a few sentences, but the short version of her argument is that there are plans (the sequence of steps that we think we are about to carry out before performing some task) and actions, which is the set of things we actually do. In my mind, a plan corresponds roughly with the underlying computational mechanism, but the actions correspond with how that mechanism executes and what happens when the underlying structure is insufficient, wrong, misleading, or fails.

Hope that helps.

Gabe

* None of this is to say that computational structures that we implement with software/hardware ar

Re:AIMA

[hoofinasia]

Nope. Its got neural networks. (section 20.5) Try walking into any cog sci / AI faculty office without seeing this book. Don't let anyone tell you it's dry (its got math! gasp!). It's accessible and thorough.

Also:
Statistics!

...learn it, love it. Thats mostly what AI is under all the gloss. That sound is a thousand Cog Sci students screaming in terror, ignore them.

Heard of AGI?

[QuantumG]

http://www.opencog.org/wiki/OpenCogPrime:WikiBook

Some interesting stuff.

machine learning resources

[Anonymous Coward]

Following Books are must have for machine learning enthusiasts:

Christopher Bishop
http://research.microsoft.com/~cmbishop/prml/

Richard Duda
http://rii.ricoh.com/~stork/DHS.html

There you will get an insight how machine learning methods (like neural networks, SVM, boosting, bayes classificator) work

for general AI (not so much in direction of statistical learning as the books above, but more into higher level learning like inference rules) I can recommend published work done by

Drew McDermott
http://cs-www.cs.yale.edu/homes/dvm/

Re:machine learning resources

[DocDJ]

+1 for the book by Bishop (don't know about the others). In addition, have a look at Information Theory by David Mackay which I found stunningly good. There is a free on-line version available, but you should buy it: http://www.inference.phy.cam.ac.uk/itprnn/book.html

Re:machine learning resources

[Beezlebub33]

I'll second Duda and Hart, though I guess it's Duda, Hart, and Stork now.

It's probably the most widely used pattern classification book that I've seen, and covers most of the techniques that you'll find. The coverage of neural networks is limited to Backprop though, so you'll need to look elsewhere for more in-depth on those.

Re:AI != design brain

[Dan East]

http://www.databasecolumn.com/2008/01/mapreduce-a-major-step-back.html

As both educators and researchers, we are amazed at the hype that the MapReduce proponents have spread about how it represents a paradigm shift in the development of scalable, data-intensive applications. MapReduce may be a good idea for writing certain types of general-purpose computations, but to the database community, it is:

      1. A giant step backward in the programming paradigm for large-scale data intensive applications

      2. A sub-optimal implementation, in that it uses brute force instead of indexing

      3. Not novel at all -- it represents a specific implementation of well known techniques developed nearly 25 years ago

      4. Missing most of the features that are routinely included in current DBMS

      5. Incompatible with all of the tools DBMS users have come to depend on

Reinforcement and Machine Learning

[jacksonpauls]

These might seem a little old, but are still a couple of my favorites:
Reinforcement Learning by Sutton & Barto
Machine Learning by Tom Mitchell

Re:AI != design brain

[TapeCutter]

"I'd also be looking as seriously parallel processing."

If you haven't seen this it might interest you. Note that it's a simulation for use in studying the physiology of the mammalian brain, not an AI experiment. Any ghost in the machine would have to emerge by itself in pretty much the same way mind emerges from brain function.