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Comment by metagame | Hacker News Reader

I would have mentioned that, but it's mentioned in the Stanford post and is also less interesting from a "You Should Know" standpoint on a website built on a Common Lisp-inspired Lisp. Not to mention that the book isn't as good as PAIP.

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> Not to mention that the book isn't as good as PAIP.

Norvig would disagree -

http://www.norvig.com/Lisp-retro.html -

----- As an AI text, PAIP does not fare as well. It never attempted to be a comprehensive AI text, stressing the "Paradigms" or "Classics" of the field rather than the most current programs and theories. Happily, the classics are beginning to look obsolete now (the field would be in sorry shape if that didn't happen eventually). For a more modern approach to AI, forget PAIP and look at Artificial Intelligence: A Modern Approach. -----

But I would highly recommend reading PAIP. I felt that some important examples of classic AI (like SHRDLU, not to mention Eurisko) could be included, but it's still really good.

I'm aware of what he said on it, and I don't disagree with what he said, but you're misrepresenting him by leaving the preceding two lines out.

«As an advanced Lisp text, PAIP stands up very well. There are still very few other places to get a thorough treatment of efficiency issues, Lisp design issues, and uses of macros and compilers. (For macros, Paul Graham's books have done an especially excellent job.)

As an AI programming text, PAIP does well. The only real competing text to emerge recently is Forbus and de Kleer, and they have a more limited (and thus more focused and integrated) approach, concentrating on inference systems. (The Charniak, Riesbeck, and McDermott book is also still worth looking at.) One change over the last six years is that AI programming has begun to look more like "regular" programming, because (a) AI programs, like "regular" programs, are increasingly concerned with large data bases, and (b) "regular" programmers have begun to address things such as searching the internet and recognizing handwriting and speech. An AI programming text today would have to cover data base interfaces, http and other network protocols, threading, graphical interfaces, and other issues.»

While yes, it aged poorly as an AI text, and excellently as a Lisp & AI programming text, it's a better book than AI:AMA, even if ignoring that it's based around a better language.

I don't disagree with his assessment, exactly, but it leaves out that PAIP is just great as a look at the craft of programming, by example, even if old-school AI is not your main interest. See how he breaks down problems, develops solutions incrementally, expresses them in code, suggests further work. It offers another look at themes from SICP.

> but you're misrepresenting him by leaving the preceding two lines out.

That was the only place where he compared PAIP and AIMA. I guess he considers these books serving purposes different enough so comparisons in other areas have less sense. Back to where we started, PAIP isn't universally considered a better book, even though it's good.

Well, he didn't say what kind of book. My understanding is that PAIP is treated more as an advanced programming techniques text than an AI text. I imagine that you have good competitors for AIMA as an AI text but no good competitors for PAIP as an advanced programming techniques text. (At least in the lispy languages world, I know of none other -- maybe except for the less ambitious On Lisp?)

> forget PAIP and look at Artificial Intelligence: A Modern Approach.

I've read and enjoyed that book 10+ years ago. Haven't been following AI/ML since then. Is it still a "modern approach"?

On the one hand, the fourth edition includes an introduction to deep learning that's as up-to-date as it's possible for a printed book to be. On the other, it's placed late in the book, and many people these days would skip straight to the neural nets. Up to you. I think it's valuable for condensing an incredible amount of stuff in an accessible unified introductory way.

Right, if your goal is to understand the broad field of artificial intelligence, AIMA is great. If your goal is to understand ML in particular, then there are a bunch of other more applicable books (Deep Learning, Learning From Data, probably a bunch of more recent ones that I don't know because I haven't kept up super recently)

"12. We must resist the temptation to belive that all thinking follows the computational model."

Hnnng. This is the whole thing. Turing machines are limited. Data (inputs from humans) have more power.

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