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> Note that I did not use "pattern recognition" in a derogatory manner, neither did I say that anything is "just" pattern recognition. I think you may be mistak

> Note that I did not use "pattern recognition" in a derogatory manner, neither did I say that anything is "just" pattern recognition. I think you may be mistaking my comment for a different opinion that you disagree with. > [...] > In any case, before commenting on a complex subject of research with a long history, my recommendation remains to first become well acquainted with the subject and its history. Otherwise, one runs the risk of appearing confused. Sorry, I did misunderstand what you were saying. I do find it somewhat baffling how you're talking as if 1980s terminology is today's terminology, or that everyone should just recognize it as the default way of things. As much as you argue I should adopt the subject's history, I'm saying you should adopt its present. The point you're making would have held fine two decades ago, but as a description of modern ML, it's like calling a sedan a cart. Even if you don't mean it as a criticism (‘just a cart without a horse’), the world has moved on.

YeGoblynQueenne 6y ago
>> Sorry, I did misunderstand what you were saying. Thank you for your honesty and there's no need to apologise. Let me explain my point above, if I can. You said that search, planning, expert systems, etc, are "not AI, and they never will be". I understand that as saying that such systems are not artificial intelligences, in the sense of an intelligent machine created by humans out of whole cloth (without trying to define what "intelligent" means). That is certainly true, but it is also uncontroversial that the above are sub-fields of the field of research that is known as "AI". That is, there is a field that researches approaches that could lead to the creation of intelligent machines and that is called "AI" and then there's the ultimate target of that field which is to create "AI". My original comment bemoans the fact that in some sectors, "AI", as a field of research, has become synonymous with only one sub-sub-field of AI research, that is, deep learning. Contrary to what you state, these "GOFAI" fields (symbolic AI, if you will) are still active and far from having "failed" in any way, they are "SOTA" in their respective tasks. For example, the field of automated theorem proving is dominated by systems that employ the resolution rule, a logic-based approach and while recent efforts have been made to make inroads into theorem proving with deep neural nets (e.g. a recent attempt to use transformers) results are still very far from being comparable to the traditional approaches. I know more about automated theorem proving that I know e.g. about planning or search (because my PhD is in a subject close to the former) but my understanding is that in those fields too, traditional techniques dominate- which is why research in them is still active. 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). And so on. These are just some examples of AI research that is ongoing, that is not deep learning, and that is state of the art for its respective tasks. In view of that, I consider that using "AI" to mean "deep learning" (as the article above does) is not only misinformed but also misinforming and actively harmful. In the sense of harming people's understanding, that is. As to your comment about how GOFAI "failed", I'm afraid this opinion, which is common, is also misinformed. Here, too, a knowledge of the history of the field helps, but to summarise, 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. In fact, expert systems, now widely considered a failure, were one of the first big success stories of AI; a success story that was cut short only because, again, of political reasons. I could talk about the AI winter subject for hours (it's a favourit subject of mine) but a good starting point is this article by the editor of the IEEE journal Intelligent Systems: (Avoiding Another Winter) https://www.computer.org/csdl/magazine/ex/2008/02/mex2008020002/13rRUyeCkdP https://www.computer.org/csdl/magazine/ex/2008/02/mex2008020.... The wikipedia page on https://en.wikipedia.org/wiki/AI_winter https://en.wikipedia.org/wiki/AI_winter also has a decent summary and some sources. Finally, see the wikipedia article on the Lighthill Report https://en.wikipedia.org/wiki/Lighthill_report https://en.wikipedia.org/wiki/Lighthill_report which is more relevant to AI research in the UK (the Report killed AI research in the UK, dead) and this review of the report by John McCarthy: http://www-formal.stanford.edu/jmc/reviews/lighthill/lighthill.html http://www-formal.stanford.edu/jmc/reviews/lighthill/lighthi... (man who coined the term AI and also created LISP on the side). >> As much as you argue I should adopt the subject's history, I'm saying you should adopt its present. More to the point, I'm recommending that you should know a subject's history before forming a strong opinion about its present and future. As to me, I'm firmly rooted to the present. About half of the literature I read is neural networks- and that's not even the subject of my research. 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.
- Veedrac 6y ago
  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?

YeGoblynQueenne 6y ago

>> Sorry, I did misunderstand what you were saying. Thank you for your honesty and there's no need to apologise. Let me explain my point above, if I can. You said that search, planning, expert systems, etc, are "not AI, and they never will be". I understand that as saying that such systems are not artificial intelligences, in the sense of an intelligent machine created by humans out of whole cloth (without trying to define what "intelligent" means). That is certainly true, but it is also uncontroversial that the above are sub-fields of the field of research that is known as "AI". That is, there is a field that researches approaches that could lead to the creation of intelligent machines and that is called "AI" and then there's the ultimate target of that field which is to create "AI". My original comment bemoans the fact that in some sectors, "AI", as a field of research, has become synonymous with only one sub-sub-field of AI research, that is, deep learning. Contrary to what you state, these "GOFAI" fields (symbolic AI, if you will) are still active and far from having "failed" in any way, they are "SOTA" in their respective tasks. For example, the field of automated theorem proving is dominated by systems that employ the resolution rule, a logic-based approach and while recent efforts have been made to make inroads into theorem proving with deep neural nets (e.g. a recent attempt to use transformers) results are still very far from being comparable to the traditional approaches. I know more about automated theorem proving that I know e.g. about planning or search (because my PhD is in a subject close to the former) but my understanding is that in those fields too, traditional techniques dominate- which is why research in them is still active. 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). And so on. These are just some examples of AI research that is ongoing, that is not deep learning, and that is state of the art for its respective tasks. In view of that, I consider that using "AI" to mean "deep learning" (as the article above does) is not only misinformed but also misinforming and actively harmful. In the sense of harming people's understanding, that is. As to your comment about how GOFAI "failed", I'm afraid this opinion, which is common, is also misinformed. Here, too, a knowledge of the history of the field helps, but to summarise, 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. In fact, expert systems, now widely considered a failure, were one of the first big success stories of AI; a success story that was cut short only because, again, of political reasons. I could talk about the AI winter subject for hours (it's a favourit subject of mine) but a good starting point is this article by the editor of the IEEE journal Intelligent Systems: (Avoiding Another Winter) https://www.computer.org/csdl/magazine/ex/2008/02/mex2008020002/13rRUyeCkdP https://www.computer.org/csdl/magazine/ex/2008/02/mex2008020.... The wikipedia page on https://en.wikipedia.org/wiki/AI_winter https://en.wikipedia.org/wiki/AI_winter also has a decent summary and some sources. Finally, see the wikipedia article on the Lighthill Report https://en.wikipedia.org/wiki/Lighthill_report https://en.wikipedia.org/wiki/Lighthill_report which is more relevant to AI research in the UK (the Report killed AI research in the UK, dead) and this review of the report by John McCarthy: http://www-formal.stanford.edu/jmc/reviews/lighthill/lighthill.html http://www-formal.stanford.edu/jmc/reviews/lighthill/lighthi... (man who coined the term AI and also created LISP on the side). >> As much as you argue I should adopt the subject's history, I'm saying you should adopt its present. More to the point, I'm recommending that you should know a subject's history before forming a strong opinion about its present and future. As to me, I'm firmly rooted to the present. About half of the literature I read is neural networks- and that's not even the subject of my research. 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.

Veedrac 6y ago
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?