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Comment by _5tsv | Hacker News Reader

I probably should've defined "real value" more carefully. My framework here is basically the idea of technology as humanity's "skill tree," the total set of stuff we're able to do. Back when RCT (and Doom before that) were written, we genuinely weren't sure if such things could be created, and their creation represented a novel step forward. The vibe in software feels like we're long past the stage of doing things like that, like we're not even attempting to do things like that. I can only think of two things in recent history: whatever strand of AI culminated in ChatGPT, which is genuinely impressive, and Bitcoin, which ostensibly was created by one guy.

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I think there have been advancements in RL that deserve to be on this list. AlphaFold at the very least; I hope you had the pleasure of reading the reactions of researchers who work on protein structure determination and how stunned they were that their field of research had been in large part solved.

Here's a few more that come to mind:

- Work decoding brain signals that allowed paralyzed people to communicate via a computer (this all happened pre Neuralink)

- I think self-driving cars fit your criteria, even if the jury is still out as to whether this has/will soon be 'solved.'

- In gaming, Unreal Engine's Nanite is very impressive and a real technological innovation

- There are efforts to get LLMs to learn via RL and not just from training on huge text corpuses. Actually mathematics is a prime example where you might dream of getting superhuman performance this way. People are at least trying to do this.

Just because there's a lot of BS which tends to drown everything else out, doesn't mean there isn't real progress too.

I suppose with a blanket statement like "nothing is happening" I'm practically begging to be contradicted :)

Some of this stuff looks very cool. Have we started getting RL to work generally? I remember my last impression being that we were in the "promising idea but struggling to get it working in practice" phase but that was some time ago now.

Edit: after writing what follows, I realized you might have been asking about RL apply to LLMs. I don't know if anyone has made any progress there yet.

Depends on what you mean by 'generally?' It won't be able to solve all kinds of problems, since e.g. you need problems with well-defined cost functions like win probability for a board game. But AlphaGo and AlphaZero have outstripped the best Go players, whereas before this happened people didn't expect computers to surpass human play any time soon.

For AlphaFold, it has literally revolutionized the field of structural biology. Deepmind has produced a catalog of the structure of nearly every known protein (see https://www.theverge.com/2022/7/28/23280743/deepmind-alphafo..., and also https://www.nature.com/articles/d41586-020-03348-4 if you can get past the paywall). It's probably the biggest thing to happen to the field since cryoelectron microscopy and maybe since X-ray crystallography. It may be a long time before we see commercially available products from this breakthrough, but the novel pharmaceuticals are coming.

Those are the biggest success stories in RL that I can think of, where they are not just theoretical but have lead to tangible outcomes. I'm sure there are many more, as well as examples where reinforcement learning still struggles. Mathematics is still in the latter category, which is probably why Terence Tao doesn't mention it in this post. But I think these count as expanding the set of things humans can do, or could do if they ever work :)

It seems you've taken a roundabout way to arrive at innovation.

It's interesting because this is something that makes programming feel like a "bullshit job" to some: I'm not creating anything, I'm just assembling libraries/tools/framework. It certainly doesn't feel as rewarding, though the economic consensus (based on the salaries we get) is that value most definitely is being generated. But you could say the same of lumberjacks, potters, even the person doing QA on an assembly line, all in all it's not very innovative.

That's the thing with innovation though, once you've done it, it's done. We don't need to write assembly to make games, thanks to the sequential innovation of game engines (and everything they are built on).

Samme with LLMs and Bitcoin: now that they exist, we can (and did) build up on them. But that'll never make a whole new paradigm, at least not rapidly.

I think our economic system simply doesn't give the vast majority of people the chance to innovate. All the examples you've given (and others I can think of) represented big risks (usually in time invested) for big rewards. Give people UBI (or decent social safety nets) and you'll find that many more people can afford to innovate, some of which will do so in CS.

I have to go back to work now, some libraries need stitched together.

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