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> 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

> 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?
  - goodside 5y ago
    The ranking criteria of this list make it very unrepresentative of compressors used in the real world. The benchmark they’re using for example is the sum of the compressed file plus the compressor binary: this penalizes memorization of the evaluation text in the compressor binary itself. But in the real world, you would have no concerns at all that your compressor is “cheating” by working too well only for your particular data — having useful priors that model real-world data for more compact representations is the whole point. Many of these algorithms are also impractical due to speed or memory use. Ask yourself: How many of the top-10 algorithms do you have installed right now, or even recognize? The winners aren’t dominating outside the arena of this list. I’m also not an expert in symbolic AI — my comment above is more about neural vs. pre-neural NLP methods, rather than symbolic AI, which I admit drifts a bit from the parent. A compressor replacing word tokens with dictionary indices is definitely symbolic but it’s not especially “AI”.
YeGoblynQueenne 5y ago
>> I would challenge you to name any (non-simple) problem where traditional AI methods are still state of the art. Theorem proving, classical planning, SAT solving, robotics, search, in particular adversarial search, program induction, knowledge representation. Plus all the stuff that used to be considered "AI" but aren't anymore, like rule-based systems (e.g. for fraud detection) etc. Sorry, I know you asked for only one.
- lostdog 5y ago
  Great answers. If we check back in a couple years, I'm sure we'll see good learned approaches to most of these problems. Robotics is already on its way there. Is this program induction https://paperswithcode.com/task/program-induction https://paperswithcode.com/task/program-induction ? Looks like it's headed towards ML too. I suspect you could stick ML into SAT solving and get yourself a system that worked pretty decently.
  - Cybiote 5y ago
    I've never met a coherent definition of symbolic AI so I'll just focus on what it contained: some type of search or inference algorithm (iterative deepening, depth first search being major ones) often combined with heuristics written in a programming language like prolog or lisp. Specification + Inference ≊ Specification + Control flow. That means Probabilistic models written in a language like Stan, which is Inference + Specification, fit neatly into so called symbolic AI (which is basically just programming with search/inference). These search and sampling algorithms still play key roles in game playing AI (chess, poker, Go) and natural language generation. It is the human knowledge, specification and heuristics, part that tends to be more readily replaceable. A lot of control flow and data-structures that powered old AI approaches can be found in databases, compilers, type inference, computer algebra and even the autodiff libraries neural nets are written in. Video game AI, constraint solving and business rules engines are probably closest to still using the full symbolic approach rather than merely extracting the control flow and structures portion. We can therefore make a compact prediction: learned approaches replace human written computer programs (specifications, rules systems or heuristics) whenever human contribution is not valuable or is somehow harmful to robustness/generalization.
avmich 5y ago
> I would challenge you to name any (non-simple) problem where traditional AI methods are still state of the art. Did ML methods best classic AI in dialog comprehension, say to the level of SHRDLU? I'm curious, can ML system do that - https://en.wikipedia.org/wiki/SHRDLU https://en.wikipedia.org/wiki/SHRDLU ?
- YeGoblynQueenne 5y ago
  Short answer: no. The capabilities of SHRDLU remain unsurpassed by modern systems. A simulated robot hand controlled by natural language to move blocks inside a virtual world. It's not terribly useful but nothing that was created since can do any better and the state-of-the-art NLP approach of large language models is completely incapable of anything like it. Which is a bit sad, really, if you think that SHRDLU was created by one graduate student, fifty years ago.
- lostdog 5y ago
  How about LOReL, where a robot manipulates objects and open drawers when given text instructions: https://sites.google.com/view/robotlorl https://sites.google.com/view/robotlorl

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?
- goodside 5y ago
  The ranking criteria of this list make it very unrepresentative of compressors used in the real world. The benchmark they’re using for example is the sum of the compressed file plus the compressor binary: this penalizes memorization of the evaluation text in the compressor binary itself. But in the real world, you would have no concerns at all that your compressor is “cheating” by working too well only for your particular data — having useful priors that model real-world data for more compact representations is the whole point. Many of these algorithms are also impractical due to speed or memory use. Ask yourself: How many of the top-10 algorithms do you have installed right now, or even recognize? The winners aren’t dominating outside the arena of this list. I’m also not an expert in symbolic AI — my comment above is more about neural vs. pre-neural NLP methods, rather than symbolic AI, which I admit drifts a bit from the parent. A compressor replacing word tokens with dictionary indices is definitely symbolic but it’s not especially “AI”.

YeGoblynQueenne 5y ago

>> I would challenge you to name any (non-simple) problem where traditional AI methods are still state of the art. Theorem proving, classical planning, SAT solving, robotics, search, in particular adversarial search, program induction, knowledge representation. Plus all the stuff that used to be considered "AI" but aren't anymore, like rule-based systems (e.g. for fraud detection) etc. Sorry, I know you asked for only one.

lostdog 5y ago
Great answers. If we check back in a couple years, I'm sure we'll see good learned approaches to most of these problems. Robotics is already on its way there. Is this program induction https://paperswithcode.com/task/program-induction https://paperswithcode.com/task/program-induction ? Looks like it's headed towards ML too. I suspect you could stick ML into SAT solving and get yourself a system that worked pretty decently.
- Cybiote 5y ago
  I've never met a coherent definition of symbolic AI so I'll just focus on what it contained: some type of search or inference algorithm (iterative deepening, depth first search being major ones) often combined with heuristics written in a programming language like prolog or lisp. Specification + Inference ≊ Specification + Control flow. That means Probabilistic models written in a language like Stan, which is Inference + Specification, fit neatly into so called symbolic AI (which is basically just programming with search/inference). These search and sampling algorithms still play key roles in game playing AI (chess, poker, Go) and natural language generation. It is the human knowledge, specification and heuristics, part that tends to be more readily replaceable. A lot of control flow and data-structures that powered old AI approaches can be found in databases, compilers, type inference, computer algebra and even the autodiff libraries neural nets are written in. Video game AI, constraint solving and business rules engines are probably closest to still using the full symbolic approach rather than merely extracting the control flow and structures portion. We can therefore make a compact prediction: learned approaches replace human written computer programs (specifications, rules systems or heuristics) whenever human contribution is not valuable or is somehow harmful to robustness/generalization.

avmich 5y ago

> I would challenge you to name any (non-simple) problem where traditional AI methods are still state of the art. Did ML methods best classic AI in dialog comprehension, say to the level of SHRDLU? I'm curious, can ML system do that - https://en.wikipedia.org/wiki/SHRDLU https://en.wikipedia.org/wiki/SHRDLU ?

YeGoblynQueenne 5y ago
Short answer: no. The capabilities of SHRDLU remain unsurpassed by modern systems. A simulated robot hand controlled by natural language to move blocks inside a virtual world. It's not terribly useful but nothing that was created since can do any better and the state-of-the-art NLP approach of large language models is completely incapable of anything like it. Which is a bit sad, really, if you think that SHRDLU was created by one graduate student, fifty years ago.
lostdog 5y ago
How about LOReL, where a robot manipulates objects and open drawers when given text instructions: https://sites.google.com/view/robotlorl https://sites.google.com/view/robotlorl