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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 tha

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.

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.