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> The difference between ML and symbolic AI is that ML works and symbolic AI doesn't. IBM managed to beat Garry Kasperov using symbolic AI did they not? So in

> The difference between ML and symbolic AI is that ML works and symbolic AI doesn't. IBM managed to beat Garry Kasperov using symbolic AI did they not? So in what way does it not work?

adgjlsfhk1 5y ago
they didn't. that was just alpha beta search with some custom hardware to speed it up. also at this point, both of the strongest chess ai (stockfish and lc0) are using neutral networks and are roughly 1000 elo above where deep blue was (and most of that is from software, not hardware)
- shmageggy 5y ago
  > just alpha beta search I will cling to these goal posts every time. Search was and still is AI, unless you think Russell and Norvig should have named the field's foundational textbook something other than "Artificial Intelligence: A Modern Approach"
  - adgjlsfhk1 5y ago
    I was more arguing with symbolic than AI.
PeterisP 5y ago
1. There's a world of problems (such as "perception-related" e.g. vision and NLP) which we tried to solve for decades with symbolic AI and got worse results than what nowadays first-year students can do as a homework with ML; 2. For your example of chess, for some time now ML engines are pretty much untouchable by engines based on pre-ML methods.
- CodeGlitch 5y ago
  Yes I agree with all your points - I was however responding to the point being made that symbolic AI "wasn't useful"...which in the past it was. Perhaps in the future some new method or breakthrough will mean it becomes useful once again?
  - panabee 5y ago
    this is a great point. much like deep learning was invented decades ago but didn't become feasible until technology caught up, could the same be true for symbolic AI? i.e., is the ceiling for symbolic AI technical and transient or fundamental and permanent?
    - PeterisP 5y ago
      My feeling is that even in our own thinking symbols are used mostly to communicate our (inherently non-symbolic) thoughts to others or record them; i.e. they are a solution to a bandwidth-limited transfer of information while the actual thinking process happens with concepts that have more similarity to collections of vague parameters and associations which can be compressed to symbols only imperfectly with losses. From that perspective, I don't see how symbolic AI would be competitive but there would be a role for symbolic AI in designing systems that can be comprehensible for humans, but perhaps just as a distillation/compression output from a non-symbolic system. I.e. have a strong "black box" ML system that learns to solve a task, and then have it construct a symbolic system that solves that task worse, but in an explainable way.
- YeGoblynQueenne 5y ago
  >> 1. There's a world of problems (such as "perception-related" e.g. vision and NLP) which we tried to solve for decades with symbolic AI and got worse results than what nowadays first-year students can do as a homework with ML; Perception tasks were traditionally attempted with statistical machine learning approaches rather than symbolic AI, for example the Perceptron was a very early neural network that was used in machine vision, created by Frank Rosenblatt in 1958. A lot of that research was carried out under the rubrik of "pattern recognition" rather than machine learning. In any case, no, "we" did not try "to solve [those problms] for decades with symbolic AI". Symbolic AI has traditionally focused on reasoning, which is generally considered to be on some kind of separate level to perception. As to chess engines, they're still symbolic-statistical hybrids. E.g. the Alpha-x family combines Monte Carlo Tree Search with neural nets that learn an evaluation function etc.
  - PeterisP 5y ago
    Various NLP tasks used to be very heavy on symbolic approaches (with some of them still being used), I myself worked on them for some years until the statistical approaches started to work better. For computer vision, I would probably consider the work on edge detectors, HOG and SIFT algorithms as the "symbolic" heritage for object detection which has now been replaced with pure ML.
    - YeGoblynQueenne 5y ago
      NLP has been dominated by statistical learning approaches for some time, that's true, although when I did my Master's in 2016, I seem to remember the Brill tagger was still considered state-of-the-art and there was still a lot of work on learning PCFGs or dependency grammars. Perhaps that just happened to be what the tutors at my course were working on, though. In any case, it seems to me that while real progress has been achieved in language modelling, the same cannot be said for language understanding. That's a bigger conversation but anyway, modelling is still what statistical learning techniques do best, whereas anything to do with semantics, you still need some kind of symbolic approach. I never thought of HOG and SIFT as "symbolic". If I remember correctly, they were just sets of hand-crafted features? But, features for classifiers, like SVMs and so on.
lostdog 5y ago
> IBM managed to beat Garry Kasperov using symbolic AI did they not? So in what way does it not work? Ok, I should be clearer. ML approaches are way way better than symbolic approaches. Given almost any problem, it is much much easier to make an ML approach work than any symbolic approach. Yes, chess was first solved symbolically, but it's since been solved by ML better and more easily, to the point that stockfish now incorporates neural nets [1]. ML has also given extremely high levels of performance on Go, Starcraft, DoTA, and on protein folding, image recognition, text processing, speech recognition, and pretty much everything else. I would challenge you to name any (non-simple) problem where traditional AI methods are still state of the art. [1] https://stockfishchess.org/blog/2020/stockfish-12/ https://stockfishchess.org/blog/2020/stockfish-12/
- CodeGlitch 5y ago
  Thanks for clearing that up, I do agree that ML-based AI has surpassed symbolic approaches in every field.
- goodside 5y ago
  “I would challenge you to name any (non-simple) problem where traditional AI methods are still state of the art.” Lossless file compression. As far as I know none of the algorithms in widespread use are neural-based, despite the fact that compression is clearly a rich statistical modeling problem, at least on par with GPT-3-style language understanding in difficulty. There are published attempts to solve the problem with neural networks, but they simply don’t work well enough to date. Modern solutions also still use old-fashioned AI ingredients like compiled dictionaries of common natural-language words — any other domain where nat-lang dictionaries are useful has been conquered by neural solutions, e.g. spelling and grammar checkers.
  - _game_of_life 5y ago
    I'm far from an expert in this subject but doesn't this ranking of large text compression algorithms with NNCP coming first suggest that neural-nets are pretty great at compression? http://mattmahoney.net/dc/text.html http://mattmahoney.net/dc/text.html https://bellard.org/nncp/ https://bellard.org/nncp/ I don't see examples of high performing symbolic AI based compression algorithms anywhere, but again I am very ignorant, do you have examples?

> The difference between ML and symbolic AI is that ML works and symbolic AI doesn't. IBM managed to beat Garry Kasperov using symbolic AI did they not? So in what way does it not work?

adgjlsfhk1 5y ago

they didn't. that was just alpha beta search with some custom hardware to speed it up. also at this point, both of the strongest chess ai (stockfish and lc0) are using neutral networks and are roughly 1000 elo above where deep blue was (and most of that is from software, not hardware)

shmageggy 5y ago
> just alpha beta search I will cling to these goal posts every time. Search was and still is AI, unless you think Russell and Norvig should have named the field's foundational textbook something other than "Artificial Intelligence: A Modern Approach"
- adgjlsfhk1 5y ago
  I was more arguing with symbolic than AI.

PeterisP 5y ago

1. There's a world of problems (such as "perception-related" e.g. vision and NLP) which we tried to solve for decades with symbolic AI and got worse results than what nowadays first-year students can do as a homework with ML; 2. For your example of chess, for some time now ML engines are pretty much untouchable by engines based on pre-ML methods.

CodeGlitch 5y ago
Yes I agree with all your points - I was however responding to the point being made that symbolic AI "wasn't useful"...which in the past it was. Perhaps in the future some new method or breakthrough will mean it becomes useful once again?
- panabee 5y ago
  this is a great point. much like deep learning was invented decades ago but didn't become feasible until technology caught up, could the same be true for symbolic AI? i.e., is the ceiling for symbolic AI technical and transient or fundamental and permanent?
  - PeterisP 5y ago
    My feeling is that even in our own thinking symbols are used mostly to communicate our (inherently non-symbolic) thoughts to others or record them; i.e. they are a solution to a bandwidth-limited transfer of information while the actual thinking process happens with concepts that have more similarity to collections of vague parameters and associations which can be compressed to symbols only imperfectly with losses. From that perspective, I don't see how symbolic AI would be competitive but there would be a role for symbolic AI in designing systems that can be comprehensible for humans, but perhaps just as a distillation/compression output from a non-symbolic system. I.e. have a strong "black box" ML system that learns to solve a task, and then have it construct a symbolic system that solves that task worse, but in an explainable way.
YeGoblynQueenne 5y ago
>> 1. There's a world of problems (such as "perception-related" e.g. vision and NLP) which we tried to solve for decades with symbolic AI and got worse results than what nowadays first-year students can do as a homework with ML; Perception tasks were traditionally attempted with statistical machine learning approaches rather than symbolic AI, for example the Perceptron was a very early neural network that was used in machine vision, created by Frank Rosenblatt in 1958. A lot of that research was carried out under the rubrik of "pattern recognition" rather than machine learning. In any case, no, "we" did not try "to solve [those problms] for decades with symbolic AI". Symbolic AI has traditionally focused on reasoning, which is generally considered to be on some kind of separate level to perception. As to chess engines, they're still symbolic-statistical hybrids. E.g. the Alpha-x family combines Monte Carlo Tree Search with neural nets that learn an evaluation function etc.
- PeterisP 5y ago
  Various NLP tasks used to be very heavy on symbolic approaches (with some of them still being used), I myself worked on them for some years until the statistical approaches started to work better. For computer vision, I would probably consider the work on edge detectors, HOG and SIFT algorithms as the "symbolic" heritage for object detection which has now been replaced with pure ML.
  - YeGoblynQueenne 5y ago
    NLP has been dominated by statistical learning approaches for some time, that's true, although when I did my Master's in 2016, I seem to remember the Brill tagger was still considered state-of-the-art and there was still a lot of work on learning PCFGs or dependency grammars. Perhaps that just happened to be what the tutors at my course were working on, though. In any case, it seems to me that while real progress has been achieved in language modelling, the same cannot be said for language understanding. That's a bigger conversation but anyway, modelling is still what statistical learning techniques do best, whereas anything to do with semantics, you still need some kind of symbolic approach. I never thought of HOG and SIFT as "symbolic". If I remember correctly, they were just sets of hand-crafted features? But, features for classifiers, like SVMs and so on.

lostdog 5y ago

> IBM managed to beat Garry Kasperov using symbolic AI did they not? So in what way does it not work? Ok, I should be clearer. ML approaches are way way better than symbolic approaches. Given almost any problem, it is much much easier to make an ML approach work than any symbolic approach. Yes, chess was first solved symbolically, but it's since been solved by ML better and more easily, to the point that stockfish now incorporates neural nets [1]. ML has also given extremely high levels of performance on Go, Starcraft, DoTA, and on protein folding, image recognition, text processing, speech recognition, and pretty much everything else. I would challenge you to name any (non-simple) problem where traditional AI methods are still state of the art. [1] https://stockfishchess.org/blog/2020/stockfish-12/ https://stockfishchess.org/blog/2020/stockfish-12/

CodeGlitch 5y ago
Thanks for clearing that up, I do agree that ML-based AI has surpassed symbolic approaches in every field.
goodside 5y ago
“I would challenge you to name any (non-simple) problem where traditional AI methods are still state of the art.” Lossless file compression. As far as I know none of the algorithms in widespread use are neural-based, despite the fact that compression is clearly a rich statistical modeling problem, at least on par with GPT-3-style language understanding in difficulty. There are published attempts to solve the problem with neural networks, but they simply don’t work well enough to date. Modern solutions also still use old-fashioned AI ingredients like compiled dictionaries of common natural-language words — any other domain where nat-lang dictionaries are useful has been conquered by neural solutions, e.g. spelling and grammar checkers.
- _game_of_life 5y ago
  I'm far from an expert in this subject but doesn't this ranking of large text compression algorithms with NNCP coming first suggest that neural-nets are pretty great at compression? http://mattmahoney.net/dc/text.html http://mattmahoney.net/dc/text.html https://bellard.org/nncp/ https://bellard.org/nncp/ I don't see examples of high performing symbolic AI based compression algorithms anywhere, but again I am very ignorant, do you have examples?