gld773

>Imagine the same applied to physics - if instead of inventing QM and Relativity or Mechanics, physicists got it in their head to try to feed raw data into a black box and see how well it predicts some observed movements.

In fact, this would be a pretty interesting experiment: how large would a deep learning model that could accurately predict what mechanics predicts get, given only raw data (object positions, velocities, masses, colors, surface roughness, shape, taste etc.)? Unfortunately, I don't think anyone has been interested in this type of experiment, because it is not useful from an engineering (or profit) perspective.

Isn't that what googles alphafold is doing pretty much?

https://deepmind.com/blog/article/AlphaFold-Using-AI-for-sci...

and it seems GPT-3 formed concepts related words together without being asked, its not picking the next best word strictly as a matter of statistic probability. So why wouldn't that apply to physics simulations / chemistry etc?

feed it chemical formulas and balancing equations from old chem 101 textbooks and it will fill in the blanks and start teaching itself how those things relate just by being corrected enough, then you can see if it has any predictive value.

and no mention of the manhatten project at all? , we built secret cities for that

>so we could debate it and regulate it.

but you'd have no way of enforcing any treaty so that's a moot point and they would know this.

I think making people aware of the importance of the control or value loading problems is a much better use of efforts.