DeepMind’s AI helps untangle the mathematics of knots

This happens every time people post research here. Any article about new battery tech will have people saying "What about all the other new battery techs that never went anywhere? This is just a prototype, it wont revolutionize the industry", and they are right. The difference is that when people say those things in a thread about ML, then AI optimists who thinks that these things are evidence that the singularity is near starts to argue that no, this time is truly different! And they do that every single time, AI optimists have believed that computers will take over any day now for 50 years. If those AI optimists didn't post so much then those threads wouldn't grow.

The critique was the tendency to continually pass these strides off as "AI" or something asymptotically close to it. Either on the part of sleazy companies pushing self-driving cars like they're just around the corner. Or supposedly reputable science journals that really ought to know better by now.

I have no opinion on this paper, but that is generally how cutting-edge research works. First, choose an impactful unsolved problem you have the highest chance of solving. Then, use the new learnings to ascend higher mountains.

So the natural desire is downplay the discovery, because this does two things: one it makes you appear smarter than them, and two it might just prevent them from actually achieving something great, which would take away your envy.

I can get where these people are coming from, but it's so silly to say "look at these idiots, they didn't even consider that their research has limitations!" and think it makes you look like you're genuinely concerned about the limits of the research and not just an envious person picking away at small mistakes because you can't do the hard work.

AlphaFold has changed the course of structural biology research and some descendant of that technology will absolutely be used to design drugs that many people take.

The academic research community would eventually have built models with the same performance as AlphaFold2, probably within the decade. Nonetheless, DeepMind was first, and way ahead of everyone else.

I don't understand how the goalposts have moved so far that solving protein folding only counts as a cool trick or novelty.

Discovering new areas of application however obscure or deep in any field is what I find more amazing than any practical immediate application of ML-after all the G in AGI is general so we should be hitting all the bases.

The verdict is that people in every scientific/engineering field should learn a bit of ML/DL (the underlying theory in it is not that hard), think a bit about the benefits and costs of it, tuck it in their toolbelt as one of their various tools, and move on. There's no harm in learning about the new trendy stuff, but keep in mind that there's no point in banging even the most high-quality hammer at a screw.

But I don’t see that part of it in the link.

Edit: just to expand a bit, the "intelligence" in modern AI is in the training, not inference. I think people see inference in a neural network as pattern matching or some kind of filter, and that's basically true, and "dumb" if you like. But learning the weights is a whole different thing. Stochastic Gradient Descent et al are using often only a comparative few examples to learn a pattern and embed it in a set of weights in a way that can generalize despite being highly underdetermined. It's not general intelligence, but it's a much different thing than the casual dismissals people like to post, usually directed at the inference part as if the weights just magically appeared

Yes, that's all I'm saying. And it seems like such a known point that they're not the same and not interchangeable, or so I thought (that I'm kind of astonished at the downvotes at my original comment).

Because in this case, while AI is a more term for the technology than ML, it is not a strictly incorrect title.

I get what you're saying at the product level -- and the fact that the vast bulk of the public subjected to these technologies couldn't tell you the difference, nor could they begin to care.

But to practitioners, the basic facts remain: ML ≠ AI, it's a proper subset, and we're doing the public a genuine disservice (and arguably causing substantial harm) by pretending to tell them that we're making good progress developing AI as distinct from hypercharged ML, that any day now they'll have self driving cars ... and all that crap the industry has basically been telling people.

And again, I thought it was pretty simple and generally known.

Yes, I'm aware of the Rodney Brooks quote. I think it's kind of glib and misses the mark.

Take chess, for example. When algorithms were developed to beat humans at chess -- it wasn't, like the quote implies, out of some sense of envy or schadenfreude that people said, "Oh, but that's not real AI".

But rather -- from the intrinsic features of the system that first met this challenge. Which, at the end of the day, were basically about generalized tree search. Okay, there was more to it than that -- but in 3 word or less, that's gist of the AlphaZero algorithm, as described in the original paper.

So people said "Oh -- it turns out you don't need human-like cognition to beat humans at chess/go/shogi. You just need good enough tree search, and some decent hardware."

Pretty soon self-driving cars will also not count as AI, because we'll have solved self-driving and therefore it's not hard enough anymore.

That's one of the weird things about the current moment in AI: people keep talking about self-driving cars as if they're a straight-line extrapolation the breakthroughs we've seen so far (give or take a few details).

They're not. If you just think about if for a minute: nearly all of the challenges met thus far -- taking the two you just mentioned as particularly shining examples -- as significant as they have been, have been pretty darn one-dimensional. "Here's a function; optimize it" (basically).

The reason self-driving cars are floundering at the moment is that the intrinsic problem they're dealing with is an entirely different beast from "solve this symbolic game faster than a human" or "label these photos better than a roomful of monkees". It is vastly more complex. And the emerging consensus (whatever some companies may say to the contrary) is that solution is not lurking just around the corner, and not coming our way anytime soon -- certainly not "pretty soon".