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Math is an interesting case. Arguably a lot of the groundwork was already there. There’s a huge body of academic literature that’s historically been difficult for humans to draw on for various reasons. It’s relatively easier for LLMs to mine all of that for relevant prior work that it can recycle into proof segments to try. And we have theorem proving software that it can use to try out these proof segments and validate them. That creates a tight, self-contained, and relatively inexpensive feedback loop that lends itself well to a host of agents grinding away, as long as you have budget to pay for all that LLM inference.

I’m not sure the same conditions exist in other fields that deal with real world phenomena. Experiments to test hypotheses about biology or physics can take years and cost millions of dollars to run. They rarely scale down to the millipennies and milliseconds scale that makes letting a swarm of agents throw a bunch of shit at the wall and see what sticks a feasible option for finding mathematical proofs.

by LarsDu8815 hours ago|

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I posted the original comment, but there are multiple reasons why math success won't translate into drug development or materials engineering success. It is easy to create verifiable rewards environments for math and coding, and use the resulting data for RLVR post-training. For other domains you need machines or robots creating and running experiments. This is simply something that's never been attempted at scale.

Getting IPO level monies should enable the amount of capital needed to sustain such efforts, and if I were a betting man, that is going to be Anthropic's actual endgame given their nascent efforts in automating bio research.

by kaffekaka13 hours ago|

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Do you think their endgame is achievable? Honest question, your reasoning seems balanced.

by LarsDu8813 hours ago|

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I work in biotech. Honestly, I think they will run into massive hurdles.

Amodei's academic background is relevant for protein engineering. Hence the recent press releases Anthropic has released which all concern prototype proof-of-concepts related to binding efficacy.

For a large number of therapeutic relevant indications, the bottleneck is actually clinical trials where you must contend with the sideeffects of any given therapeutic. Almost every drug I've worked on involves very well known mechanisms where knockdowns with targeted compounds will work in a petri dish, but can have catastrophic consequences when dosed on a live human.

As far as I can see there is no way to accelerate clinical trials in humans.

Even the most fantastically large and detailed perturbation datasets will struggle to capture this dynamic in a full human. This is why tech oligarchs are investing in virtual cell models. With a large automated research lab running perturbations 24/7 and capturing massive amounts of data, perhaps we will get as confident with predicting outcomes in cells as we now are confident with many coding projects. However, this is a scientific question, not a technological one. Public companies rarely give the people running them leeway to try and fail on big scientific question marks as opposed to well established or soon-to-be well established technology.

by chasd0013 hours ago|

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> This is simply something that's never been attempted at scale.

isn't that the point though? You invest in a company so they can make these attempts. If it works you make money if it doesn't you lose money, that's the risk.

by mdavidn16 hours ago|

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There's not much money in math. The frontier labs massively outspent mathematics departments to achieve recent results.

by TripolitianFish15 hours ago|

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The sort of mathematics that has been published recently is very particular. Which isn't really downplaying it but it is to say that there's a pretty massive gap between mathematics and semiconductors or wetlab work. I don't want to say anything totally deflationary like "AI will never effect research in biology" or something like that, but I think the contribution of AI in those kind of fields will be much more modest.

by geraneum13 hours ago|

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> Don't see why it shouldn't

Don't see why it should! Unless there's a harness like in math for other fields.

by ActionHank15 hours ago|

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lol, no it's not, the machine that is built to find connections between concepts found connections, that's it.

What has evolved is that mathematicians are pearl clutching because they never thought it would happen to them and wondering if they can still pour all their ideas / thoughts / research into these tools after all is said and done.

That last point is the one that kills OAI / Ant going forward. Can they be trusted? Anecdotally no.