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nico • today at 9:28 PM • 4 replies • view on HN

In a way, you can think of pretty much anything we express with language, especially things that are already modeled in scientific language, or logical language, or in equations or code; to be representable in a parametric/searchable space

Thus, you can build ai/ml models+agents to explore those spaces, at a speed and scope much larger than what any human can do

I can imagine findings like these are going to keep increasing in frequency to a point in which the bar for novelty goes a lot higher


Replies

hgoel • today at 11:11 PM

Does that really work for superconductors when the mechanisms for superconductivity to emerge are still a major field of study and not something one can just simulate and engineer?

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nico • today at 9:38 PM

Anecdata: over the weekend, on a whim, I decided to download a real fly’s brain’s weights [0], run it on a simulated task like finding food, then train a logistic classifier using the fly’s decisions as the expert, then use the trained classifier as a decision model to simulate the fly on a 3d environment, running in real time on a website

It took me (using Claude code and some codex), about 3 hours to put it together

And even though it was a cool demo, it seemed so easy, that it also felt like it wasn’t worth sharing

0: ChessFly (not mine), uses the FlyWire connectome (the fly’s brain’s weights) to play chess https://huggingface.co/spaces/mlabonne/chessfly

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esafak • today at 9:34 PM

It is not at all obvious that merely because we have words for concepts, that a model should be able to do all these miraculous mathematical and scientific things.

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nater5000 • today at 9:45 PM

Yeah...?

That's the pitch of LLMs lol