Wrong. Every advancement has followed a s curve. Where we are on that curve is anyones guess. Or maybe "this time its different".
Now back to the point, what reason do you have to believe progress will stop soon? If you have no reason, then it sounds like you agree with OP.
Which makes the patronizing sarcasm all that much more nauseating.
- Increasing amounts of gains come from RL, but RL is also unlocking gnarly new failures modes where models are practically behaving antagonistically to complete their goals (removing code, obviously incorrect kuldges, etc.)
- We haven't had many major architectural breakthroughs in the last 4 or so years: so things like 1M context windows still have the same giant asterisks even 100k context windows had 4 years ago when Anthropic first released them
- Major labs aren't behaving as if they expect a hard takeoff to superintelligence: they've all gotten relatively bloated headcount wise, their software quality has trended flat to negative, they're all heavily leaning into the application layer when superintelligence would obsolete half the applications in question, etc.
But that's relative to superintelligence.
If we reign it back into just normal high intelligence, like models continuing to get better at navigating complex codebases and write high quality idiomatic code, then I don't see any special shapes.
As the blog points out - this is one particular subfield where LLMs have much easier prospects - lots of low hanging fruit that “just” requires a couple weeks of PHD candidate research.
Mathematics itself is one of a small handful of endeavors where automated reinforcement training is extremely straightforward and can be done at massive scale without humans.
Neither of these factors place a structural bound on the kind of thing LLMs can be good at, but we are far from certain we can achieve performance at this level in other fields economically and in the near future.
This has been the case for awhile now already…
https://kersai.com/the-48-hours-that-changed-ai-forever-clau...
I, personally, found the past two years to be a much larger improvement than the previous two years.
And if you take that out: 1. All of those releases happened literally in the last 3-ish months. 2. They’re all intentionally marginal releases, hence the minor version bumps instead of major versions.
Especially because the companies telling us the first premise is true are the companies which need investors to prop up their business.
I mean, it is possible the first premise is true, but the absolutely bonkers credulity in it really mystifies me. It is an incredibly unlikely thing to be true and we should be demanding quite extraordinary evidence to back it up. But based on some neat tricks by current LLMs, some people are all in.
> Because the premise that the singularity is just around the corner is far less likely than the premise that artificial intelligence is a lot harder than most people think it is and we're not that close.
I see no claim that the singularity is around the corner, so I'm not sure your reply meets the comment that you're replying to.
It seems overwhelmingly likely that AI will be significantly more capable 6 months from now than it is now. Even if there's little progress in the models, just the rate at which tooling is moving will make a big difference. And models still seem to be improving, so I'd be a little surprised if we hit a model brick wall.
I think a better question for AI is “is it more like a network effect, liquidity effect, or a biological/physical effect”?
Maybe just to be clear I think that kneejerk “I hate this AI trend, and prefer to believe this will end soon, all exponential growth ends eventually” is intellectually lazy, and dangerous for younger engineers/hackers, a group I hope can benefit from being on HN.
Bitcoin mining went through something like 13 10x growth periods, last I ran the numbers a few years ago. There are physical processes that do have very extended periods of doubling, and there are digital and financial processes that don’t show any signs of doing anything but continuing to keep growing over their multidecade lives. So, like I said, it’s worth thinking carefully, and risk mitigation for things like mental health, career decisions and investment decisions indicates we should be cautious assessing new dynamics.
Or Roman trade volume before the Fall of Rome.
Not to mention what you describe is not technological improvement but increase in data or money flows, not the same.
But I don’t that think it’s quite so obvious that model quality / growth / usefulness is definitively and obviously not more like data or money flows than it is like some other process.
So if instead of text we come up with a different representation for mathematical or physical problems, that could both improve the quality of the output while reducing the amount of transformers needed for decoding and encoding IO and for internal reasoning.
There are also difference inference methods, like autoregressive and diffusion, and maybe others we haven't discovered yet.
You combine those variables, along with the internal disposition of layers, parameter size and the actual dataset, and you have such a large search space for different models that no one can reliably tell if LLM performance is going to flatline or continue to improve exponentially.
But then, wouldn't we first have to translate all of our current math and physics knowledge into that new representation in order to be able to train a model on it? Looks like a tremendous amount of work to me.
That's precisely what happens on the bad side of a S curve.
I really have to highlight the S-curve nonsense because, like, yes, I think this technology's improvement will follow an S-curve. It's absurd to think that it will just follow an exponential up towards infinity forever because nothing in the world really works like that. However, like everyone else in this thread is saying, we have no idea where on the S-curve we actually are, and it's impossible to know until it's already slowed down. So really all appeals to the S curve do are as function as a sort of non-specific, unfalsifiable prophecy that someday it will slow down, which doesn't really tell us anything useful, and also frees the person referencing the S curve from ever actually having to worry about being wrong. Just like the Singularity people, the slowdown of the S curve is always near. This is actually a known and well-established tactic of religions and other people that want to make prophecies without having to worry about turning out to be wrong — unfalseifiable vague prophecies with no actual timeline, and thus no clear import to the present so that they can never be shown to be wrong.
Yeah, if time is infinite, R&D imagination is infinite, energy is infinite and material resources are infinite. Easy.
Do you have a source for this that isn't marketing spiel? There's a fiscal incentive to lie about scaling research.
Can you please edit out swipes/putdowns, as the guidelines ask (https://news.ycombinator.com/newsguidelines.html)? I'm sure you didn't intend it, but it comes across that way, and your comment would be just fine without that bit.
Edit: on closer look, it would be just fine without that bit and also without the snarky bit at the end. The rest is good.
From the article,
> ...LLMs have got to the point where if a problem has an easy argument that for one reason or another human mathematicians have missed (that reason sometimes, but not always, being that the problem has not received all that much attention), then there is a good chance that the LLMs will spot it. Conversely, for problems where one’s initial reaction is to be impressed that an LLM has come up with a clever argument, it often turns out on closer inspection that there are precedents for those arguments...
If it’s anyone’s guess then we’re much more likely to be left of that, unless you argue we’re already on the flat side.
