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Hi @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?

by hmcamp 4y ago

dunefox 4y ago
Mostly not, no.
potatoman22 4y ago
The most relevant field this would apply to is AI for games.
ameasure 4y ago
Norvig 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 ago
  That is a great retrospective! Still very relevant 20 years after it was updated.
KineticLensman 4y ago
Symbolic 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 ago
I 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.

Hi @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?

by hmcamp 4y ago

Hi @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 ago

Mostly not, no.

potatoman22 4y ago

The most relevant field this would apply to is AI for games.

ameasure 4y ago

Norvig 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 ago
That is a great retrospective! Still very relevant 20 years after it was updated.

KineticLensman 4y ago

Symbolic 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 ago

I 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.