19 ms·

It never ceases to amaze me what you can do with these transformer models. They created millions of potential solutions for each problem, used the provided exam

• bricemo 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? (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
    • 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
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• briga 5y ago
  Another way to frame it is that these models still perform very poorly at the task they're designed to do. Imagine if real programmer needed to write a solution a hundred times before they were able to achieve (average) performance. You'd probably wonder if it was just blind luck that got them to the solution. You'd also fire them. What these models are very good at doing is plagiarizing content, so part of me wonders if they aren't just copying previous solutions with slight adjustments.
  • plutonorm 5y ago
    How do you know the inner workings of the mind don't operate in a similar manner? How many different solutions to the problem are constructed within your mind before the correct one 'just arrives'?
    • briga 5y ago
      I suspect there is some similarity between language models and the structure of language in the mind, but there's a whole lot more going on behind the scenes in the brain than simple runtime statistical model output. Intentionality, planning, narrativity, memory formation, object permanence... Language models are exciting and interesting because apparently they can do abstract symbolic manipulation and produce coherent text, but I wouldn't call AGI solved quite yet.
  • Vetch 5y ago
    > Imagine if real programmer needed to write a solution a hundred times To be fair, a lot of creative work requires plenty of trial and error. And since no problems are solved from scratch, all things considered, the most immediate contributors to your result and you might have iterated through tens of dozens of possibilities. My advantage as a human is I can often tell you why I am eliminating this branch of the search space. The catch is my reasoning can be flawed. But we do ok. > just copying previous solutions with slight adjustments. It's not just doing that, Copilot can do a workable job providing suggestions for an invented DSL. A better analogy than autocomplete is inpainting missing or corrupted details based on a surrounding context. Except instead of a painting we are probabilistically filling in patterns common in solutions to leetcode style problems. Novelty beyond slight adjustments comes in when constraints are insufficient to pin down a problem to a known combination of concepts. The intelligence of the model is then how appropriate its best guesses are. The limitations to GPT3 codex and AlphaCode seems to be they're relatively weak at selection and that they require problem spaces with enough data to distill a sketch of and how to inpaint well in them. Leetcode style puzzles are constructed to be soluble in a reasonable number of lines, are not open ended and have a trick to them. One can complain that while we're closer to real world utility, we're still restricted to the closed worlds of verbose apis, games and puzzles. While lots of commenters seem concerned about jobs, I look forward to having the dataset oliphaunt and ship computer from Fire Upon Deep someday soon.
• mikesabbagh 5y ago
  github autopilot scares me every time I write code on my personal pc and get those auto-suggestions. I am happy we dont have it at work yet. It is clear writing code will soon be something of the past; maybe it is a bad idea to train our children to code. Let's make sure we milk every penny before the party is over!
  • evouga 5y ago
    Maybe… maybe… tools like Copilot will allow us to work at a higher level of abstraction (like optimizing compilers have allowed us to do). I say maybe because so far the code that Copilot has generated for me has been impressive for what it is, but riddled with obvious and subtle bugs. It’s like outsourcing my function implementations to a C-student undergraduate intern. I definitely wouldn’t use any of its code without close scrutiny. AI will make some software engineering tasks more efficient and more accessible but human programmers are not going anywhere any time this side of the Singularity.
• derangedHorse 5y ago
  We have a clue as to what it is (these are just functions at the end of the day) but don't know how the model's learned parameters relate to the problem domain. I saw a talk (maybe of Jeff Dean?) a while back that discussed creating models that could explain why certain features weighed more than others. Maybe with more approaches targeted towards understanding, these algorithms could start to seem less and less like a semantically opaque computational exercise, and more in line with how we humans think about things.
• noduerme 5y ago
  >> filter out 99% of incorrect solutions And next year they can filter out 99.99%. And the year after that, 99.9999%. So literally, an exponentially greater number of monkey/typewriting units. (An AI produced Shakespeare play coming soon). >> we have no clue at all what that actually is and how these model learn This is why I'm super cool-to-cold about the AI/deep learning classes being sold to young people who would otherwise be learning fundamental programming skills. It appears to me like trying to teach someone to ride a horse before they understand what skin, bones, muscles, animals, and horses are. >>get anywhere close to human (expert) capability in any sufficiently complex domain You can get close enough to scalp a lot of billionaires, but at the end of the day it's always going to be human coders banging our heads against management, where they ask for shit they can't visualize and it's our job to visualize how their employees/customers will use it. Yes it involves domain specific knowledge, but it also requires, er, having eyeballs and fingers, and understanding how a biological organism uses a silicon-based device. That's kind of the ultimate DS knowledge, after all. Now, lots of coders just copy-pasta a front end, but after all the hooplah here I'd be extremely surprised if in ten years an AI has caught up to your basic web mill in Indonesia when it comes to building a decent website.
  • parentheses 5y ago
    i like that you drew a connection with monkeys on typewriters.
  • TOMDM 5y ago
    Surely if your discrimintator gets orders of magnitude better like your describing, we could train the transformer GAN style, and reduce the dependence on generating so many examples to throw away.