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I don't want to dismiss automated provers, as they are often quality, useful tools (SAT solvers in particular), but if you're interested in learning AI, traditi

I don't want to dismiss automated provers, as they are often quality, useful tools (SAT solvers in particular), but if you're interested in learning AI, traditional approaches are no longer more than briefly and tangentially relevant. That's my point, that you don't need to learn woodworking to build a car, even if wooden carts still have occasional uses, and some cars have wood trim or wooden trailers. > If I am permitted to tout my own horn, my broader subject area can be described as "program learning", i.e. machine learning of arbitrary programs from examples of their inputs and outputs. In this area also, deep learning systems are hopelessly outclassed by symbolic approaches, not least because these approaches learn precise representations of target programs (rather than approximations) from a mere handful of examples (four or five, etc). I have looked at the program synthesis literature before and it really does not seem very advanced to me. The General Program Synthesis Benchmark Suite lists unsolved benchmarks like “Given three strings n1, n2, and n3, return true if length(n1) < length(n2) < length(n3), and false otherwise”, and that's with 100 examples. So, probably less practically useful than GPT-3, which wasn't even trained on the task. > the last winter happened because of strictly political reasons and for no reason that had anything to do with the scientific merits, or practical successes of the relevant approaches I disagree, but you've not given me much more than a list of vague references that don't all exactly support your argument, so I don't have much clue where you diverge. If GOFAI worked, we'd see some indication of it working (again, in an AI context), but we don't. > But if you think about it, in the era where the trend is to use deep learning, the most interesting results can only come from not using deep learning. In a gold rush, if everyone is digging up Widow's Creek, then Widow's Creek is the last place to dig for gold. This analogy doesn't work. Neural networks are giving unparalleled results by the bucket. That's why people are digging there. A gold mine might have plenty of competing miners, but it's sure going to be a lot more likely to give you chunks of gold than a random patch of grass in your backyard.

YeGoblynQueenne 6y ago
I'm happy to see we're still in healthy disagreement. However, I have to apologise for confusing you by describing my field as "program learning" which is admittedly vague, but I didn't want to go into the particulars. My field is not program synthesis, which is constructing programs from complete, formal specifications. Rather, it's Inductive Programming and more specifically Inductive Logic Programming (ILP), which is learning programs from examples, i.e. "incomplete specifications". I'm not familiar with the General Program Synthesis Benchmark Suite, but the problem you list (test three strings are ordered by length) is trivial for ILP approaches. Again, I don't want to point to my own research, directly (I'm going through a bit of a modesty phase) (oh, alright, it's just that the documentation of my project is crap). However, I have to say that even so, if something is a difficult problem for program synthesis approaches, then it's very unlikely that neural networks will do any better at it. For instance, do you know how well deep neural nets perform on this benchmark? I can't find any results with a quick search. You make the point that one does not need to learn these "obsolete" AI approaches because they are not relevant anymore. I don't understand why you say that. These approaches are still state of the art for their respective tasks and there is no other approach that has been shown to do any better, including deep neural networks. In what sense are they "no longer more than briefly and tangentially relevant" as you say? Regarding the gold rush, the point of the analogy is that in a gold rush only a very few people will ever strike gold. This is exactly the state of research into deep learning currently. After a few initial big breakthroughs, like CNNs and LSTMs, progress has stalled and the vast, vast majority of published papers (or papers put on arxiv permanently) present incremental results, if that. Literally thousands of deep learning papers are published each month and the chance to have an impact is miniscule. From my point of view, as a researcher, going into deep learning right now would be career suicide. Not to mention that, while the first few successes were achieved by small academic teams, who had typical academic motives (er, glory), the game has now passed to the hands of big corporate teams that have quite different incentives, so it's almost impossible for small teams or individual researchers to make a dent. As to the winter and whether GOFAI works, perhaps I haven't convinced you with my sources, but in that case, I have to go back to my earlier question and ask where your knowledge comes from. You clearly have a strong opinion on GOFAI and the AI winter of the '80s, but what knowledge does this opinion come from? Can you say? And if this sounds like a challenge, well, that's because it is. I'm challenging you to re-examine the basis of your convictions, if you like. Because to me, they sound like they are not well-founded and that you should put some water in your wine. The things you say "don't work", work and the things you say work, don't work as well as you say. For my part, I certainly agree that GPT-3 or the next iteration of a large transformer-built language model can be a useful tool, but such a tool will always be limited by the fact that it's, well, a language model, and it can only do what language models do, which does not include e.g. the ability for reasoning (despite big claims to the contrary) or arithmetic (ditto) or generation of novel programs. For instance, the append() example you show above is clearly memorised: you haven't given the model any examples of append(), so it can't possibly learn its definition from examples. It only returns a correct result because it's seen the results of append() before. Not the same result, but close enough. Like I say, this ability can definitely be useful- but its usefulness is limited compared to the ability to learn arbitrary programs, never before seen. btw, why do you need to give it the list "a"? What happens if this is ommitted from the prompt?
- Veedrac 6y ago
  > Rather, it's Inductive Programming and more specifically Inductive Logic Programming (ILP), which is learning programs from examples, i.e. "incomplete specifications". I'm not familiar with the General Program Synthesis Benchmark Suite, but the problem you list (test three strings are ordered by length) is trivial for ILP approaches. The General Program Synthesis Benchmark Suite works from input-output examples, not “complete, formal specifications”. How would you tackle this with ILP? > However, I have to say that even so, if something is a difficult problem for program synthesis approaches, then it's very unlikely that neural networks will do any better at it. For instance, do you know how well deep neural nets perform on this benchmark? I'm not aware of any serious at-scale attempts. Your option is basically to try few-shot with GPT-3. OTOH, learning these trivial programs from 100 examples is a largely artificial framing used to support a field which hadn't worked its way up to meaningful problems, and in the more general sense, large networks are promising; eg. the GitHub-trained GPT: https://www.youtube.com/watch?v=y5-wzgIySb4 https://www.youtube.com/watch?v=y5-wzgIySb4 or any of the GPT-3 programming demos: https://twitter.com/sharifshameem/status/1284103765218299904 https://twitter.com/sharifshameem/status/1284103765218299904 https://twitter.com/sharifshameem/status/1284815412949991425 https://twitter.com/sharifshameem/status/1284815412949991425 https://www.reddit.com/r/commandline/comments/jl8jyr/the_nlc2cmd_challenge_site_has_a_gpt3powered/gaop9ji/ https://www.reddit.com/r/commandline/comments/jl8jyr/the_nlc... > These approaches are still state of the art for their respective tasks and there is no other approach that has been shown to do any better, including deep neural networks. In what sense are they "no longer more than briefly and tangentially relevant" as you say? “if you're interested in learning AI” These techniques were invented from the field of AI, but that does not mean they remain in the field of AI. > You clearly have a strong opinion on GOFAI and the AI winter of the '80s, but what knowledge does this opinion come from? Can you say? I can argue why ML approaches are good and promising and point at that. I can argue why ML approaches make conceptual sense whereas GOFAI does not, though I don't see us resolving that short-term so I'd rather not. But what I can't so easily do is point to the non-existence of GOFAI AI successes. It's just not there. You do have tools Watson and WolframAlpha which use GOFAI techniques for fact search over a large set of human-built knowledge repositories (trivia q's / math tools), but Watson is mostly considered a stunt, and I'm not aware of anyone calling WolframAlpha AI. > the ability for reasoning (despite big claims to the contrary) The nebulousness of the term ‘reasoning’ is pulling a lot of weight here. It's clearly doing sophisticated computations of some sort, beyond brute memorization. > or arithmetic (ditto) http://gptprompts.wikidot.com/logic:math#toc6 http://gptprompts.wikidot.com/logic:math#toc6 There are more examples too, this is just addressing the one point people get wrong most often. BPEs are an interim performance hack, not an indictment on the approach in general. > or generation of novel programs Is clearly false. > For instance, the append() example you show above is clearly memorised: you haven't given the model any examples of append(), so it can't possibly learn its definition from examples. This is true, but it's mostly just an artifact of me having to prompt it through FitnessAI. Unlike smaller models, few-shot learning works, it just takes more space than I have to prompt with. See the GitHub-trained example for something that integrates with more arbitrary code. There are many other examples, like the database prompt below (all bold is human input), or see some of the examples I linked above. https://www.gwern.net/GPT-3#the-database-prompt https://www.gwern.net/GPT-3#the-database-prompt Or I can ask Q: “If z(str) = str + " " + str + " z" (for example, z("dumbell") = "dumbell dumbell z"), and g(str) = "k " + str + " j" then what is g("run")?” A: “g("run") = "k run j"” (The inverse problem doesn't work so well, giving “g(str) = "k run j"” for one example (valid but vapid) and “g(str) = "k str j"” for two (close but no banana), and confusion for more complex prompts, though I suspect the format is partially to blame. I can list other failure cases. But my point isn't that GPT-3 is reliable here; it's a language model.) > btw, why do you need to give it the list "a"? What happens if this is ommitted from the prompt? That example was from me trying to emulate an example I saw on Twitter I've since lost, which was a similar thing but multi-step, where each step GPT-3 returned all three lists, modified or queried per the given commands. Omitting `a`, I get Q: “b = ["lifting", "curls", "squats"], c = ["running", "jogging"], so what is b after b.append("pushups")?” A: “lifting,curls,squats,pushups” I had to change the prompt a bit because initially the result was truncated (FitnessAI is not made for this), or said “b.append("pushups") will add the string "pushups" to the end of b.”, which is correct but not what I wanted. Few-shot would fix formatting inconsistencies; right now the model is just guessing.

YeGoblynQueenne 6y ago

I'm happy to see we're still in healthy disagreement. However, I have to apologise for confusing you by describing my field as "program learning" which is admittedly vague, but I didn't want to go into the particulars. My field is not program synthesis, which is constructing programs from complete, formal specifications. Rather, it's Inductive Programming and more specifically Inductive Logic Programming (ILP), which is learning programs from examples, i.e. "incomplete specifications". I'm not familiar with the General Program Synthesis Benchmark Suite, but the problem you list (test three strings are ordered by length) is trivial for ILP approaches. Again, I don't want to point to my own research, directly (I'm going through a bit of a modesty phase) (oh, alright, it's just that the documentation of my project is crap). However, I have to say that even so, if something is a difficult problem for program synthesis approaches, then it's very unlikely that neural networks will do any better at it. For instance, do you know how well deep neural nets perform on this benchmark? I can't find any results with a quick search. You make the point that one does not need to learn these "obsolete" AI approaches because they are not relevant anymore. I don't understand why you say that. These approaches are still state of the art for their respective tasks and there is no other approach that has been shown to do any better, including deep neural networks. In what sense are they "no longer more than briefly and tangentially relevant" as you say? Regarding the gold rush, the point of the analogy is that in a gold rush only a very few people will ever strike gold. This is exactly the state of research into deep learning currently. After a few initial big breakthroughs, like CNNs and LSTMs, progress has stalled and the vast, vast majority of published papers (or papers put on arxiv permanently) present incremental results, if that. Literally thousands of deep learning papers are published each month and the chance to have an impact is miniscule. From my point of view, as a researcher, going into deep learning right now would be career suicide. Not to mention that, while the first few successes were achieved by small academic teams, who had typical academic motives (er, glory), the game has now passed to the hands of big corporate teams that have quite different incentives, so it's almost impossible for small teams or individual researchers to make a dent. As to the winter and whether GOFAI works, perhaps I haven't convinced you with my sources, but in that case, I have to go back to my earlier question and ask where your knowledge comes from. You clearly have a strong opinion on GOFAI and the AI winter of the '80s, but what knowledge does this opinion come from? Can you say? And if this sounds like a challenge, well, that's because it is. I'm challenging you to re-examine the basis of your convictions, if you like. Because to me, they sound like they are not well-founded and that you should put some water in your wine. The things you say "don't work", work and the things you say work, don't work as well as you say. For my part, I certainly agree that GPT-3 or the next iteration of a large transformer-built language model can be a useful tool, but such a tool will always be limited by the fact that it's, well, a language model, and it can only do what language models do, which does not include e.g. the ability for reasoning (despite big claims to the contrary) or arithmetic (ditto) or generation of novel programs. For instance, the append() example you show above is clearly memorised: you haven't given the model any examples of append(), so it can't possibly learn its definition from examples. It only returns a correct result because it's seen the results of append() before. Not the same result, but close enough. Like I say, this ability can definitely be useful- but its usefulness is limited compared to the ability to learn arbitrary programs, never before seen. btw, why do you need to give it the list "a"? What happens if this is ommitted from the prompt?

Veedrac 6y ago
> Rather, it's Inductive Programming and more specifically Inductive Logic Programming (ILP), which is learning programs from examples, i.e. "incomplete specifications". I'm not familiar with the General Program Synthesis Benchmark Suite, but the problem you list (test three strings are ordered by length) is trivial for ILP approaches. The General Program Synthesis Benchmark Suite works from input-output examples, not “complete, formal specifications”. How would you tackle this with ILP? > However, I have to say that even so, if something is a difficult problem for program synthesis approaches, then it's very unlikely that neural networks will do any better at it. For instance, do you know how well deep neural nets perform on this benchmark? I'm not aware of any serious at-scale attempts. Your option is basically to try few-shot with GPT-3. OTOH, learning these trivial programs from 100 examples is a largely artificial framing used to support a field which hadn't worked its way up to meaningful problems, and in the more general sense, large networks are promising; eg. the GitHub-trained GPT: https://www.youtube.com/watch?v=y5-wzgIySb4 https://www.youtube.com/watch?v=y5-wzgIySb4 or any of the GPT-3 programming demos: https://twitter.com/sharifshameem/status/1284103765218299904 https://twitter.com/sharifshameem/status/1284103765218299904 https://twitter.com/sharifshameem/status/1284815412949991425 https://twitter.com/sharifshameem/status/1284815412949991425 https://www.reddit.com/r/commandline/comments/jl8jyr/the_nlc2cmd_challenge_site_has_a_gpt3powered/gaop9ji/ https://www.reddit.com/r/commandline/comments/jl8jyr/the_nlc... > These approaches are still state of the art for their respective tasks and there is no other approach that has been shown to do any better, including deep neural networks. In what sense are they "no longer more than briefly and tangentially relevant" as you say? “if you're interested in learning AI” These techniques were invented from the field of AI, but that does not mean they remain in the field of AI. > You clearly have a strong opinion on GOFAI and the AI winter of the '80s, but what knowledge does this opinion come from? Can you say? I can argue why ML approaches are good and promising and point at that. I can argue why ML approaches make conceptual sense whereas GOFAI does not, though I don't see us resolving that short-term so I'd rather not. But what I can't so easily do is point to the non-existence of GOFAI AI successes. It's just not there. You do have tools Watson and WolframAlpha which use GOFAI techniques for fact search over a large set of human-built knowledge repositories (trivia q's / math tools), but Watson is mostly considered a stunt, and I'm not aware of anyone calling WolframAlpha AI. > the ability for reasoning (despite big claims to the contrary) The nebulousness of the term ‘reasoning’ is pulling a lot of weight here. It's clearly doing sophisticated computations of some sort, beyond brute memorization. > or arithmetic (ditto) http://gptprompts.wikidot.com/logic:math#toc6 http://gptprompts.wikidot.com/logic:math#toc6 There are more examples too, this is just addressing the one point people get wrong most often. BPEs are an interim performance hack, not an indictment on the approach in general. > or generation of novel programs Is clearly false. > For instance, the append() example you show above is clearly memorised: you haven't given the model any examples of append(), so it can't possibly learn its definition from examples. This is true, but it's mostly just an artifact of me having to prompt it through FitnessAI. Unlike smaller models, few-shot learning works, it just takes more space than I have to prompt with. See the GitHub-trained example for something that integrates with more arbitrary code. There are many other examples, like the database prompt below (all bold is human input), or see some of the examples I linked above. https://www.gwern.net/GPT-3#the-database-prompt https://www.gwern.net/GPT-3#the-database-prompt Or I can ask Q: “If z(str) = str + " " + str + " z" (for example, z("dumbell") = "dumbell dumbell z"), and g(str) = "k " + str + " j" then what is g("run")?” A: “g("run") = "k run j"” (The inverse problem doesn't work so well, giving “g(str) = "k run j"” for one example (valid but vapid) and “g(str) = "k str j"” for two (close but no banana), and confusion for more complex prompts, though I suspect the format is partially to blame. I can list other failure cases. But my point isn't that GPT-3 is reliable here; it's a language model.) > btw, why do you need to give it the list "a"? What happens if this is ommitted from the prompt? That example was from me trying to emulate an example I saw on Twitter I've since lost, which was a similar thing but multi-step, where each step GPT-3 returned all three lists, modified or queried per the given commands. Omitting `a`, I get Q: “b = ["lifting", "curls", "squats"], c = ["running", "jogging"], so what is b after b.append("pushups")?” A: “lifting,curls,squats,pushups” I had to change the prompt a bit because initially the result was truncated (FitnessAI is not made for this), or said “b.append("pushups") will add the string "pushups" to the end of b.”, which is correct but not what I wanted. Few-shot would fix formatting inconsistencies; right now the model is just guessing.