6 ms·

What do you think then is the difference between going from 50th to 99.9th percentile in their other domains? Is there something materially different between ag

What do you think then is the difference between going from 50th to 99.9th percentile in their other domains? Is there something materially different between ago, protein folding, or coding? (I don’t know the answer, just curious if anyone else does)

FiberBundle 5y ago
Well with respect to Go the fundamental difference afaict is that you can apply self-supervised learning, which is an incredibly powerful approach (But note e.g. that even this approach wasn't successful in "solving" Starcraft). Unfortunately it's extremely difficult to frame real-world problems in that setting. I don't know anything about protein-folding and don't know what Deepmind uses to try to solve that problem, so I cannot comment on that.
- cjbprime 5y ago
  > this approach wasn't successful in "solving" Starcraft) Why do you say that? As I understand it, AlphaStar beat pros consistently, including a not widely reported showmatch against Serral when he was BlizzCon champ.
  - zwaps 5y ago
    Not once humans adapted to it afaik. AlphaStar got to top grandmaster level and then that was it, as people found ways to beat it. Now, it may be that the team considered the project complete and stopped training it. But technically - as it stands - Starcraft is still the one game where humans beat AI.
    - cjbprime 5y ago
      No, the version which played on ladder was much weaker than the later version which played against pros and was at BlizzCon -- the later version was at professional level of play.
  - gavagai691 5y ago
    Two possible reasons. 1. First, though I am not sure of this (i.e. this should be verified), I heard that the team working on AlphaStar initially tried to create a Starcraft AI entirely through "self-play," but this was not successful. (Intuitively, in a real-time game, there are too many bad options too early on that even with a LOT of time to learn, if your approach is too "random" you will quickly enter an unwinnable position and not learn anything useful.) As a result, they replaced this approach with an approach which incorporated learning from human games. 2. "including a not widely reported showmatch against Serral when he was BlizzCon champ." is a mischaracterization. It was not a "showmatch," rather there was a setup at Blizzcon where anyone could sit down and play against AlphaStar, and Serral at some point sat down to play AlphaStar there. He went 0-4 vs AlphaStar's protoss and zerg, and 1-0 vs its Terran. However, not only was he not using his own keyboard and mouse, but he could not use any custom hotkeys. If you do not play Starcraft it may not be obvious just how large of a difference this could make. BTW, when Serral played (perhaps an earlier iteration of) AlphaStar's terran on the SC2 ladder, he demolished it. I remember when seeing the final report, I was a bit disappointed. It seemed like they cut the project off at a strange point, before AlphaStar was clearly better than humans. I feel that if they had continued they could have gotten to that point, but now we will never know.
    - dwohnitmok 5y ago
      > but he could not use any custom hotkeys. IIRC you could and Serral did set his own custom keybindings on the machine. The main difference was different keyboard and mouse.
      - gavagai691 5y ago
        I looked into this again and the hotkey situation seems more unclear than I suggested. You could not log into your Battle.net account, so it would have been somewhat time consuming to change all of your settings manually. If I had to guess, I might wager that Serral changed some of the more important ones manually but not the others, but this is just conjecture and maybe he changed all of them. I don't know if anyone but Serral would know this, however. In any case, Serral said this, which you can take as you will: https://twitter.com/ENCE_Serral/status/1192023800961019904 https://twitter.com/ENCE_Serral/status/1192023800961019904 "It was okay, I doubt i would lose too many games with a proper setup. I think the 6.3-6.4 mmr is pretty accurate, so not bad at all but nothing special at the same time." On the one hand, surely it doesn't seem surprising that the player who lost, the human, would say the above, and so one may be skeptical of how unbiased Serral's assessment is. On the other hand, I would say that Serral is among the more frank and level-headed players I've seen in the various videogames I've followed, so I wouldn't be too hasty to write off his assessment for this reason.
  - callmekit 5y ago
    There were numerous issues. First one (somewhat mitigated lately) was extremely large number of actions per minute and (most importantly) extremely fast reaction speed. Another big issue is that the bot communicated with the game via a custom API, not a via images and clicks. Details of this API are unknown - like how invisible units were handled, but it was much higher level than a human would have (pixels). If you look at the games, the bot wasn't clever (which was a hope), just fast and precise. And some people far from the top were able to beat it convincingly. And now the project is gone, even before people had a chance to really play against the bot and find more weaknesses.
- bglazer 5y ago
  That’s not entirely correct, as I know of at least one approach to neural program synthesis that employs self supervised learning. https://arxiv.org/abs/2006.08381 https://arxiv.org/abs/2006.08381 It’s a slightly different, easier problem: generating programs based on example outputs, rather than natural language specifications.
  - YeGoblynQueenne 5y ago
    The difference is that DreamCoder has a hand-crafted PCFG [1] that is used to generate programs, rather than a large language model. So the difference is in how programs are generated. ________ [1] The structure of the PCFG is hand-crafted, but the weights are trained during learning in a cycle alternating with neural net training. It's pretty cool actually, thought a bit over-engineered if you ask me.
    - bglazer 5y ago
      Right, I think it’s a bit crazy not to use a grammar as part of the generation process when you have one. My guess is that constraining LLM generation with a grammar would make it way more efficient. But that’s more complicated than just throwing GPT3 at all of Github. Also, my understanding is that Dreamcoder does some fancy PL theory stuff to factorize blocks of code with identical behavior into functions. Honestly I think that’s the key advance in the paper, more than the wake-sleep algorithm they focus on. Anyways the point was more that self supervised learning is quite applicable to learning to program. I think the downside is that the model learns its own weird, non-idiomatic conventions, rather than copying github.
      - YeGoblynQueenne 5y ago
        I guess you're right. The sleep-wake cycle is like a kind of roundabout and overcomplicated EM process. I've read the paper carefully but theirs is a complicated approach and I'm not sure what its contributions are exactly. I guess I should read it again. Yes, it's possible to apply self-supervised learning to program synthesis, because it's possible to generate programs. It's possible to generate _infinite_ sets of programs. The problem is that if you make a generator with Universal Turing Machine expressivity, you're left with an intractable search over an infinite search space. And if you don't generate an infinite set of programs, then you 're left with an incomplete search over a space that may not include your target program. In the latter case you need to make sure that your generator can generate the programs you're looking for, which is possible, but it limits the approach to only generating certain kinds of programs. In the end, it's the easiest thing to create a generator for progams that you already know how to write- and no others. How useful is that is an open question. So far no artificial system has ever made an algorithmic contribution, to my knowledge, in the sense of coming up with a new algorithm for a problem for which we don't have good algorithms, or coming up with an algorithm for a problem we can't solve at all. My perception is influenced by my studies, of course, but for me, a more promising approach than the generate-and-test approach exemplified by DreamCoder and AlphaCode etc. is Inductive Programming, which is to say, program synthesis from input-output examples only, without examples of _programs_ (the AlphaCode paper says that is an easier setting but I very disagree). Instead of generating a set of candidate programs and trying to find a program that agrees with the I/O examples, you have an inference procedure that generates _only_ the programs that agree with the I/O examples. In that case you don't need to hand-craft or learn a generator. But you do need to impose an inductive bias on the inference procedure that restricts the hypothesis language, i.e. the form of the programs that can be learned. And then you're back to worrying about infinite vs. incomplete search spaces. But there may be ways around that, ways not available to purely search-based systems. Anyway program synthesis is a tough nut to crack and I don't think that language models can do the job, just like that. The work described in the article above, despite all the fanfare about "reasoning" and "critical thinking" is only preliminary and its results are not all that impressive. At least not yet. We shall see. After all, DeepMind has deep resources and they may yet surprise me.
jahewson 5y ago
That’s a big question but I’m tempted to answer it with a yes. A protein sequence contains a complete description of the structure of a protein but a coding question contains unknowns and the answers contain subjective variability.
YeGoblynQueenne 5y ago
>> What do you think then is the difference between going from 50th to 99.9th percentile in their other domains? Is there something materially different between ago, protein folding, or coding? Yes, it's the size of the search space for each problem. The search space for arbitrary programs in a language with Universal Turing Machine expressivity is infinite. Even worse, for any programming problem there are an infinite number of candidate programs that may or may not solve it and that differ in only minute ways from each other. For Go and protein structure prediction from sequences the search space is finite, although obviously not small. So there is a huge difference in the complexity of the problems right there. Btw, I note yet again that AlphaCode performs abysmally badly on the formal benchmark included in the arxiv preprint (see Section 5.4, and table 10). That makes sense because AlphaCode is a very dumb generate-and-test, brute-force search approach that doesn't even try to be smart and tries to make up for the lack of intelligence with an awesome amount of computational resources. Most work in program synthesis is also basically a search through the space of programs, but people in the field have come up with sophisticated techniques to avoid having to search an infinite number of programs- and to avoid having to generate millions of program candidates, like DeepMind actually brags about: At evaluation time, we create a massive amount of C++ and Python programs for each problem, orders of magnitude larger than previous work. They say that as if generating "orders of magnitude more" progams than previous work is a good thing, but it's not. It means their system is extremely bad at generating correct programs. It is orders of magnitude worse than earlier systems, in fact. (The arxiv paper linked from the article quantifies this "massive" amount as "millions"; see Section 4.4).
- deleted 5y ago
  [deleted]

FiberBundle 5y ago

Well with respect to Go the fundamental difference afaict is that you can apply self-supervised learning, which is an incredibly powerful approach (But note e.g. that even this approach wasn't successful in "solving" Starcraft). Unfortunately it's extremely difficult to frame real-world problems in that setting. I don't know anything about protein-folding and don't know what Deepmind uses to try to solve that problem, so I cannot comment on that.

cjbprime 5y ago
> this approach wasn't successful in "solving" Starcraft) Why do you say that? As I understand it, AlphaStar beat pros consistently, including a not widely reported showmatch against Serral when he was BlizzCon champ.
- zwaps 5y ago
  Not once humans adapted to it afaik. AlphaStar got to top grandmaster level and then that was it, as people found ways to beat it. Now, it may be that the team considered the project complete and stopped training it. But technically - as it stands - Starcraft is still the one game where humans beat AI.
  - cjbprime 5y ago
    No, the version which played on ladder was much weaker than the later version which played against pros and was at BlizzCon -- the later version was at professional level of play.
- gavagai691 5y ago
  Two possible reasons. 1. First, though I am not sure of this (i.e. this should be verified), I heard that the team working on AlphaStar initially tried to create a Starcraft AI entirely through "self-play," but this was not successful. (Intuitively, in a real-time game, there are too many bad options too early on that even with a LOT of time to learn, if your approach is too "random" you will quickly enter an unwinnable position and not learn anything useful.) As a result, they replaced this approach with an approach which incorporated learning from human games. 2. "including a not widely reported showmatch against Serral when he was BlizzCon champ." is a mischaracterization. It was not a "showmatch," rather there was a setup at Blizzcon where anyone could sit down and play against AlphaStar, and Serral at some point sat down to play AlphaStar there. He went 0-4 vs AlphaStar's protoss and zerg, and 1-0 vs its Terran. However, not only was he not using his own keyboard and mouse, but he could not use any custom hotkeys. If you do not play Starcraft it may not be obvious just how large of a difference this could make. BTW, when Serral played (perhaps an earlier iteration of) AlphaStar's terran on the SC2 ladder, he demolished it. I remember when seeing the final report, I was a bit disappointed. It seemed like they cut the project off at a strange point, before AlphaStar was clearly better than humans. I feel that if they had continued they could have gotten to that point, but now we will never know.
  - dwohnitmok 5y ago
    > but he could not use any custom hotkeys. IIRC you could and Serral did set his own custom keybindings on the machine. The main difference was different keyboard and mouse.
    - gavagai691 5y ago
      I looked into this again and the hotkey situation seems more unclear than I suggested. You could not log into your Battle.net account, so it would have been somewhat time consuming to change all of your settings manually. If I had to guess, I might wager that Serral changed some of the more important ones manually but not the others, but this is just conjecture and maybe he changed all of them. I don't know if anyone but Serral would know this, however. In any case, Serral said this, which you can take as you will: https://twitter.com/ENCE_Serral/status/1192023800961019904 https://twitter.com/ENCE_Serral/status/1192023800961019904 "It was okay, I doubt i would lose too many games with a proper setup. I think the 6.3-6.4 mmr is pretty accurate, so not bad at all but nothing special at the same time." On the one hand, surely it doesn't seem surprising that the player who lost, the human, would say the above, and so one may be skeptical of how unbiased Serral's assessment is. On the other hand, I would say that Serral is among the more frank and level-headed players I've seen in the various videogames I've followed, so I wouldn't be too hasty to write off his assessment for this reason.
- callmekit 5y ago
  There were numerous issues. First one (somewhat mitigated lately) was extremely large number of actions per minute and (most importantly) extremely fast reaction speed. Another big issue is that the bot communicated with the game via a custom API, not a via images and clicks. Details of this API are unknown - like how invisible units were handled, but it was much higher level than a human would have (pixels). If you look at the games, the bot wasn't clever (which was a hope), just fast and precise. And some people far from the top were able to beat it convincingly. And now the project is gone, even before people had a chance to really play against the bot and find more weaknesses.
bglazer 5y ago
That’s not entirely correct, as I know of at least one approach to neural program synthesis that employs self supervised learning. https://arxiv.org/abs/2006.08381 https://arxiv.org/abs/2006.08381 It’s a slightly different, easier problem: generating programs based on example outputs, rather than natural language specifications.
- YeGoblynQueenne 5y ago
  The difference is that DreamCoder has a hand-crafted PCFG [1] that is used to generate programs, rather than a large language model. So the difference is in how programs are generated. ________ [1] The structure of the PCFG is hand-crafted, but the weights are trained during learning in a cycle alternating with neural net training. It's pretty cool actually, thought a bit over-engineered if you ask me.
  - bglazer 5y ago
    Right, I think it’s a bit crazy not to use a grammar as part of the generation process when you have one. My guess is that constraining LLM generation with a grammar would make it way more efficient. But that’s more complicated than just throwing GPT3 at all of Github. Also, my understanding is that Dreamcoder does some fancy PL theory stuff to factorize blocks of code with identical behavior into functions. Honestly I think that’s the key advance in the paper, more than the wake-sleep algorithm they focus on. Anyways the point was more that self supervised learning is quite applicable to learning to program. I think the downside is that the model learns its own weird, non-idiomatic conventions, rather than copying github.
    - YeGoblynQueenne 5y ago
      I guess you're right. The sleep-wake cycle is like a kind of roundabout and overcomplicated EM process. I've read the paper carefully but theirs is a complicated approach and I'm not sure what its contributions are exactly. I guess I should read it again. Yes, it's possible to apply self-supervised learning to program synthesis, because it's possible to generate programs. It's possible to generate _infinite_ sets of programs. The problem is that if you make a generator with Universal Turing Machine expressivity, you're left with an intractable search over an infinite search space. And if you don't generate an infinite set of programs, then you 're left with an incomplete search over a space that may not include your target program. In the latter case you need to make sure that your generator can generate the programs you're looking for, which is possible, but it limits the approach to only generating certain kinds of programs. In the end, it's the easiest thing to create a generator for progams that you already know how to write- and no others. How useful is that is an open question. So far no artificial system has ever made an algorithmic contribution, to my knowledge, in the sense of coming up with a new algorithm for a problem for which we don't have good algorithms, or coming up with an algorithm for a problem we can't solve at all. My perception is influenced by my studies, of course, but for me, a more promising approach than the generate-and-test approach exemplified by DreamCoder and AlphaCode etc. is Inductive Programming, which is to say, program synthesis from input-output examples only, without examples of _programs_ (the AlphaCode paper says that is an easier setting but I very disagree). Instead of generating a set of candidate programs and trying to find a program that agrees with the I/O examples, you have an inference procedure that generates _only_ the programs that agree with the I/O examples. In that case you don't need to hand-craft or learn a generator. But you do need to impose an inductive bias on the inference procedure that restricts the hypothesis language, i.e. the form of the programs that can be learned. And then you're back to worrying about infinite vs. incomplete search spaces. But there may be ways around that, ways not available to purely search-based systems. Anyway program synthesis is a tough nut to crack and I don't think that language models can do the job, just like that. The work described in the article above, despite all the fanfare about "reasoning" and "critical thinking" is only preliminary and its results are not all that impressive. At least not yet. We shall see. After all, DeepMind has deep resources and they may yet surprise me.

jahewson 5y ago

That’s a big question but I’m tempted to answer it with a yes. A protein sequence contains a complete description of the structure of a protein but a coding question contains unknowns and the answers contain subjective variability.

YeGoblynQueenne 5y ago

>> What do you think then is the difference between going from 50th to 99.9th percentile in their other domains? Is there something materially different between ago, protein folding, or coding? Yes, it's the size of the search space for each problem. The search space for arbitrary programs in a language with Universal Turing Machine expressivity is infinite. Even worse, for any programming problem there are an infinite number of candidate programs that may or may not solve it and that differ in only minute ways from each other. For Go and protein structure prediction from sequences the search space is finite, although obviously not small. So there is a huge difference in the complexity of the problems right there. Btw, I note yet again that AlphaCode performs abysmally badly on the formal benchmark included in the arxiv preprint (see Section 5.4, and table 10). That makes sense because AlphaCode is a very dumb generate-and-test, brute-force search approach that doesn't even try to be smart and tries to make up for the lack of intelligence with an awesome amount of computational resources. Most work in program synthesis is also basically a search through the space of programs, but people in the field have come up with sophisticated techniques to avoid having to search an infinite number of programs- and to avoid having to generate millions of program candidates, like DeepMind actually brags about: At evaluation time, we create a massive amount of C++ and Python programs for each problem, orders of magnitude larger than previous work. They say that as if generating "orders of magnitude more" progams than previous work is a good thing, but it's not. It means their system is extremely bad at generating correct programs. It is orders of magnitude worse than earlier systems, in fact. (The arxiv paper linked from the article quantifies this "massive" amount as "millions"; see Section 4.4).

deleted 5y ago
[deleted]