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It's weird that it still feels like this is almost something I'm 'supposed' to know and at some point work my way through. (Maybe because it was on the brink of
by naillo 4y ago
It's weird that it still feels like this is almost something I'm 'supposed' to know and at some point work my way through. (Maybe because it was on the brink of still being relevant when I got interested in programming in middle school.) Kind of a relief to realize that this for sure is no longer something you're expected to know whatsoever.
- medo-bear 4y agothe engineering concepts elaborated in this book are very much relevant today and you might very well be expected to know them. what this book does (and this is relevant merrit) is that it so effectively drills them into you (provided you follow along of course)
- jonjacky 4y agoYes, check out the mostly non-AI, non-Lisp advice in Norvig's very brief summary in What Lessons are in PAIP?, the next to last section in http://norvig.com/Lisp-retro.html http://norvig.com/Lisp-retro.html
- BlueTemplar 4y agoWhat changed ?
- PaulHoule 4y agoWhen people talk about AI today they are usually talking about machine learning systems based in neural networks. The kind of symbolic AI described in this book went through several cycles of hype and disappointment to the point where many think it is obsolete. People often do it connect recent breakthroughs in SAT and SMT solvers with this history and for that matter production rules engines are dramatically better than they were in the 1980s but they’ve never made a breakthrough into general purpose use.
- hmcamp 4y agoHi @Paul, I’m a newbie in this field. Are you saying that this branch of AI isn’t relevant when compared to machine learning that’s based on neural networks?
- dunefox 4y agoMostly not, no.
- potatoman22 4y agoThe most relevant field this would apply to is AI for games.
- ameasure 4y agoNorvig addresses this at the end of f http://norvig.com/Lisp-retro.html http://norvig.com/Lisp-retro.html . Basically these old AI techniques are now considered regular programming and modern AI is focused on ML. Both are useful but for different tasks.
- mark_l_watson 4y agoThat is a great retrospective! Still very relevant 20 years after it was updated.
- KineticLensman 4y agoSymbolic AI never managed to scale to significant and general real world problems. Neither did Machine Learning, not until the advent of fast processors and massive datasets for training which broke the scale-barrier. Rule-based expert systems (as opposed to SAT, etc) based on Symbolic AI also have the issue that for non-trivial problems, coding the rules themselves usually requires programming expertise in addition to the domain knowledge required to capture the business logic. Things have improved a lot since the 80s, but applications remain fairly niche.
- PaulHoule 4y agoI think ideas from symbolic AI are relevant here and there but they certainly aren't fashionable. Almost any financial institution has a copy of IBM iLOG in there somewhere implementing policy in terms of production rules. Some of the most interesting systems today combine ideas from machine learning with ideas from AI search. For instance there are many game playing programs like AlphaGo that use https://en.wikipedia.org/wiki/Monte_Carlo_tree_search https://en.wikipedia.org/wiki/Monte_Carlo_tree_search which runs a large number of games to the end rather than searching the next few moves exhaustively. Using a machine learning model to play the game for the playouts but sampling a large number of moves with A.I. search turns out to be a winning strategy.