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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.

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.
YeGoblynQueenne 5y ago
Program induction (or inductive programming, program synthesis from incomplete specifications etc, the task of learning programs from examples) is not "headed towards ML", it is a branch of machine learning research - only, one that is dominated by symbolic learning approaches such as Inductive Logic Programming and Inductive Functional Programming. When you say "ML" you probably mean the deep neural networks approaches that are currently state of the art for machine vision etc. Deep neural network approaches have been proposed for the task of program induction but they generally lag well behind symbolic machine learning approaches. The most coherent efforts to tackle program induction by neural networks that I am aware of is the work of Dawn Song's group at Berkeley [1] and of Joshua Tenenbaum's group at MIT. I can't find a handy link to a compilation of the latter group's work but the Dreamcoder paper in the paperswithcode search you linked to was an interesting milestone [2]. There is a lot of work on neuro-symbolic approaches to program induction, for example see the recent (two weeks ago) NeSy workshop [3], part of the first International Joint Conference on Learning and Reasoning for some new work in that burgeoning field. Statistical Relational AI combines symbolic with probabilistic learning; see the STAR-AI workshop [4] also at IJCLR. If you're interested in recent developments on the front of program induction (again, learning programs from examples) then IJCRL is the conference to keep an eye on. _______ [1] https://sunblaze-ucb.github.io/program-synthesis/index.html https://sunblaze-ucb.github.io/program-synthesis/index.html [2] https://arxiv.org/abs/2006.08381 https://arxiv.org/abs/2006.08381 [3] https://sites.google.com/view/nesy20/home https://sites.google.com/view/nesy20/home [4] https://starai.cs.kuleuven.be/2021/ https://starai.cs.kuleuven.be/2021/
YeGoblynQueenne 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. Around November 2023? I'll try to remember.

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.

YeGoblynQueenne 5y ago

Program induction (or inductive programming, program synthesis from incomplete specifications etc, the task of learning programs from examples) is not "headed towards ML", it is a branch of machine learning research - only, one that is dominated by symbolic learning approaches such as Inductive Logic Programming and Inductive Functional Programming. When you say "ML" you probably mean the deep neural networks approaches that are currently state of the art for machine vision etc. Deep neural network approaches have been proposed for the task of program induction but they generally lag well behind symbolic machine learning approaches. The most coherent efforts to tackle program induction by neural networks that I am aware of is the work of Dawn Song's group at Berkeley [1] and of Joshua Tenenbaum's group at MIT. I can't find a handy link to a compilation of the latter group's work but the Dreamcoder paper in the paperswithcode search you linked to was an interesting milestone [2]. There is a lot of work on neuro-symbolic approaches to program induction, for example see the recent (two weeks ago) NeSy workshop [3], part of the first International Joint Conference on Learning and Reasoning for some new work in that burgeoning field. Statistical Relational AI combines symbolic with probabilistic learning; see the STAR-AI workshop [4] also at IJCLR. If you're interested in recent developments on the front of program induction (again, learning programs from examples) then IJCRL is the conference to keep an eye on. _______ [1] https://sunblaze-ucb.github.io/program-synthesis/index.html https://sunblaze-ucb.github.io/program-synthesis/index.html [2] https://arxiv.org/abs/2006.08381 https://arxiv.org/abs/2006.08381 [3] https://sites.google.com/view/nesy20/home https://sites.google.com/view/nesy20/home [4] https://starai.cs.kuleuven.be/2021/ https://starai.cs.kuleuven.be/2021/

YeGoblynQueenne 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. Around November 2023? I'll try to remember.