Achievements of Artificial Intelligence (AI)

[ScoutB]

They have LEAN to help them, a maths programming language of sorts that essentially allows them to check if the logic is correct.

Yeah exactly. Mathematicians have been happily developing Lean for the last decade intending to use it on the sort of math problem where the solution would be extremely long and tedious for humans to check manually. Turns out automatic correctness checking was exactly what AI needs.

It sounds like as @EndME says basically this person had Claude write the proof in Lean:

The Lean sources in this repository — definitions, statements and proofs, together with Challenge.lean, Solution.lean and the metadata — were written by an AI system (Anthropic's Claude models) working autonomously under the direction of Justin Leder; no human wrote or edited the Lean code. Correctness rests on mechanical checking [...] The work has not yet been refereed by human mathematicians or by anyone independent of the author; the only review so far was carried out by AI systems (adversarial reads of the formal statements and of the proof chain against the cited literature). Mechanical checking does not cover whether the formal statements express the intended mathematics: readers should satisfy themselves that the definitions in Challenge.lean state "θ(p_c) = 0 for nearest-neighbour Bernoulli bond percolation on ℤ^d, d ≥ 2".

 

[EndME]

I kind of agree but also it seems to me that if a PhD’s career can be nuked because a problem they were working on is solved. That sounds more like a problem with how the field works career wise that LLMs have exposed, than something inherently bad about LLMs. So I found it hard to completely agree with his analysis. It also seemed very melancholic.

I see your point, but I think it touches upon a larger issue: We build understanding by countless hours spent learning. It's becoming much harder to do that, because we often take shortcuts when we are offered them.

 

[ScoutB]

Part of the emphasis on new PhD's might be because established mathematicians will have tenure and be relatively safe from the immediate effects of this. There's a big pre-tenure post-tenure gap where math people are working their butt off to solve problems and get publications out beforehand, and then after they can coast more. It's rather hard for the university to get rid of them even if they're not doing anything 'useful' (and nobody knows what pure mathematicians are doing anyway). At least that's how it is in the US and Canada.

 

[EndME]

Part of the emphasis on new PhD's might be because established mathematicians will have tenure and be relatively safe from the immediate effects of this. There's a big pre-tenure post-tenure gap where math people are working their butt off to solve problems and get publications out beforehand, and then after they can coast more. It's rather hard for the university to get rid of them even if they're not doing anything 'useful' (and nobody knows what pure mathematicians are doing anyway). At least that's how it is in the US and Canada.

And who will they look up to when the new Riemann, Fermat and Gauß are ChatGPT 10.8 and Claude 14.4 with the fanciest results called theorem 12882 version 4.768?

 

[ChronicallyOverIt]

Verifiable domains will be first and will accelerate other domains. How much of biology is verifiable right now with our current knowledge is a bit unknown. I do see running tests for biology in wet labs about to make astronomic gains, making verification faster.

Think of this as chat-gpt3 and in three years Claude will be very very good at executing experiments autonomously:

 

[Yann04]

I see your point, but I think it touches upon a larger issue: We build understanding by countless hours spent learning. It's becoming much harder to do that, because we often take shortcuts when we are offered them.

I mean sure. But also like even though calculators/computer arithmetic sort of removed a whole part of “intuition” learning and grind that was deemed necessary in the past, it unlocked things we could never have imagined and propelled human knowledge much further. For the people who built a career doing and gaining intuition about what has been automated away it’s obviously very difficult at first. But it frees up very limited funding and work time to push knowledge further by focusing on what we haven’t automated away.

 

[EndME]

For the people who built a career doing and gaining intuition about what has been automated away it’s obviously very difficult at first. But it frees up very limited funding and work time to push knowledge further by focusing on what we haven’t automated away.

I think this is an illusion. A shoe cobbler does not all of a sudden become an AI engineer. Someone who spent his whole life doing one thing doesn't all of a sudden gain the ability to do a completely different thing. A large majority of the population, not just mathematicians, will not have jobs and if there's no solution to that then it might not matter so much that future generations can be taught to work on certain problems that haven't been automated away yet. And after all life is not just about pushing things further.

 

[Rick Sanchez]

With the rate at which AI is developing there is just no doubt in my mind that it won't speed up MECFS research progress massively. The achievements since the start of this year. I've never experienced anything like this in my life, doubt anyone else has either. GPT-Astra and even the cheap META models.

However the AI future does worry me. I have a have a hard time seeing it all ending well.

Would be the cruelest of ironies if it all goes to hell before a effective treatment is widely available

 

[nafione]

especially when most breakthroughs are the product of brute force with a chance factor.

I think that people massively overestimate how important reasoning really is here, especially when most breakthroughs are the product of brute force with a chance factor.

It takes some, but the level of reasoning that the average human, even the average scientist, is capable of is massively lower than what most people imagine is needed. A lot of it is just creative insight with a bit of an obsessive streak, and that's something AIs are already capable of. All it takes is being thorough and being able to accept when a hunch doesn't pan out, something humans massively struggle with, and has basically blocked all progress for us.

Frankly, it takes very little reasoning to be comparable to what the average researcher can do, and that's before you factor being able to work for millions of years subjective-equivalent-time. And AIs will be better at this by year's end anyway.

Hard disagree on your beliefs about the importance of reasoning versus progress.

It’s true that blind luck can yield discoveries, but it’s not efficient. No better than biological evolution and if we can’t do any better than that, we aren’t as smart as we think we are.

I never tracked the exact number of patents I’m named on, but it’s around 5. On two I was the sole inventor. I didn’t build 1000 prototypes like Edison, I reasoned out a solution and within a couple prototypes advanced the state of the art of a highly technical field, repeatedly. Over $100m of those products sold to date.

I’d like to think my reasoning skills are proven, I know what good reasoning is when I see it, and I understand the benefits of good reasoning. Frankly, I wish I saw more reasoning in me/cfs research because too much of it is blindly throwing darts at a board with little chance of progress, but what do I know.

As far as consumer facing LLM’s, I recognize no evidence of reasoning or intelligence in their output. Their noise isn’t useful either, it just increases the amount of slop for people to filter through. And they are worthless for filtering their own slop because they lack reasoning in the first place. Perhaps they are good for rote tasks, but they are truly bullshit machines as far as advancing the state of the art.

I think we should be very careful putting much hope in that particular tech. It could be a harmful distraction.

Machine learning in general may have its uses but I don’t think that’s what anybody is talking about when they say AI.

 

[ChronicallyOverIt]

How Claude is accelerating protein design and analytical chemistry

In this post, we share two results that show how Claude can help life scientists increase the pace of their research. In the first, we tested Claude’s ability to design protein binders from scratch, a key step in creating protein-based drugs that has historically taken a specialist weeks or...

www.anthropic.com

From a computer science prospective I find it interesting the rest of the world finds these so useless. It’s absolutely transformative, even the most staunch anti AI people are using these to program now, the tide has truly flipped in CS. The entire robotics industry is accelerating at rates that would have been unbelievable even 6 months ago. The speed at which you can develop is insane. The human is absolutely still the architect but that role has shrunken more and more.

While nothing novel was discovered here the speed at which prototype to lab will explode.