4 ms·
I feel like your take is overly cynical. The fact that humans can do the same thing by hand is not really the point. The contribution lies in the fact that thei
by saiojd 3y ago
I feel like your take is overly cynical. The fact that humans can do the same thing by hand is not really the point. The contribution lies in the fact that their method derived this improvement *automatically*, which is where the impact lies. No one cares all that much if a human can make a sorting routine 2% faster, but if a program can do it, it suddenly becomes interesting (since it suggests that a similar approach can be applied to many other routines).
- smeagull 3y agoA thing that is also not novel. People have done search for optimisations for at least the past decade.
- saiojd 3y agoSure, but the whole point is to reduce this kind of search to RL, which is a very general framework. Their paper shows that such a generic approach can solve a very specific problem, and solve it well. But, their paper is about improving RL, not about improving sorting.
- Zacharias030 3y agosometimes one has to wonder how RL can be both so generic and still be qualification to publich in nature again and again and again ;)
- orlp 3y agoI am not cynical about the research itself, I am critical of claims such as "new sorting algorithm uncovered", "up to 70% faster", or "first change in a decade". The research is good. The achieved results are massively inflated. What they achieved: automatically generated good code. What they claim: automatically generated code that is revolutionary and an improvement on the state of the art. And as another commenter noted, superoptimizers are also already a thing: https://en.wikipedia.org/wiki/Superoptimization https://en.wikipedia.org/wiki/Superoptimization There's also automatic searching being done on faster sorting networks that actually recently produced better than state of the art sorting networks: https://github.com/bertdobbelaere/SorterHunter https://github.com/bertdobbelaere/SorterHunter
- touisteur 3y agoI somehow agree that I'd be far more impressed by something that would find optimal or even just better sorting (or selection) networks for sizes higher than 17 (last time I looked at SOTA).
- orlp 3y agoPlease check my edit right as you commented :)
- touisteur 3y agoOh very, very cool thanks a bunch. Edit: compiling the hunter code Right Away and hopefully in some weeks I'll have better networks. Selection networks are even harder to find optimizers for, hopefully one can hack this new thing to get some.
- saiojd 3y agoWhile I agree that the claims are hyperbolic, I think you are approaching this paper from the point of view of someone who knows a lot about sorting. Because of this, its normal that the claims of these guys who probably don't know much about it are grating for you. But, at its core, this is really a RL paper. The objective is to see how far a generic approach can work while understanding as little as possible about the actual domain. After AlphaGo exceeded expectations, the question becomes: "What else can RL do, and can it do anything actually useful?", and this paper seems to suggest that it can optimize code pretty well! I'm really not sure they are self-aggrandizing in terms of impact. The impact of an approach like this could potentially be very large (although I'm not saying that it actually is, I don't know enough).
- bamboozled 3y agoWhen one has a big fuck off hammer, everything becomes a nail. Seems to apply to ML too.
- rep_movsd 3y ago
- x86x87 3y agoi don't read it as cynical. It's fair game to call bullshit on bullshit. If an approach exists and is known the "insert your favorite AI here" does not discover anything.
- mtlmtlmtlmtl 3y agoAutomatic tuning and optimisation of code is not new.