"A classic but I'm not quite sure why it was posted?"

and why is it getting so many upvotes? Extending this approach, if anyone is looking for free karma, just post separate links to all the classic CS books, without any comments or added explanation (3 separate submissions for the TAOCP Knuth books ;-)). Lots of karma for the taking ;-)

now that this is on the front page, here is something interesting. There's a 1000 $ prize for designing a cover for the third edition (see top right hand corner of teh page). The cover of the first two editions are very dense with references to AI history. If I could draw worth a damn I would have taken a shot at it.

Serious tease. I expected either the text available online, lecture videos or at the very least a courseware podcast from Berkeley. I guess I'll go back to staring at it at the bookstore...

Sign up for Gigapedia.org and you can get it. Email me if you have questions about how to DL it.

Agreed, although the code examples in python ( http://code.google.com/p/aima-python/ ) and lisp ( http://aima.cs.berkeley.edu/lisp/doc/overview.html ) are worth pointing out.

Does AI + Python have any practical use in production?

Most of the algorithms I've tested seem pretty slow. Although the linked examples are for learning.

From my research AI seems to be primarily LISP in academia and C in production.

I actually use a blend of C & Python, C for doing the heavy lifting and Python for the rapid prototyping.

Most AI algorithms have clearly defined segments that are very CPU intensive, by only using C in those areas I get the optimal balance of development speed and execution speed.

Ever since I learnt Cython, this has been my preferred combination as well. I can't overstate enough how great it is to be able to get the rapid prototyping of Python combined with being able to offload the heavy stuff to C.

Increasingly these days academic AI is done in Matlab or similar systems.