[Hermitian909]

Previously worked on the intersection of ML and Material Science (specifically batteries). I think the link to osium-ai[0] tells the story pretty well. Material science is plagued by long, expensive, exploration phases - even in places you wouldn't expect them. ML ends up being really good at cutting the expense of these phases by >50% by making you just a little smarter in your exploration.

[0] https://www.ycombinator.com/companies/osium-ai

[epolanski]

But this isn't new, isn't it?

I remember, e.g, Italy and Brazil had projects more than ten years ago where they used some sort of machine learning to find hidden historical buildings under terrain or jungle by looking at patterns in satellite/aerial image and it was successful in finding archeological sites in both countries.

[Hermitian909]

Very similar in nature yes - we had a few rather spectacular results using only techniques from the 80s and earlier.

A lot of the revolution is coming from material scientists doing the work of creating data sets that encode their knowledge, better computational techniques in the field (e.g. DFT[0]), and democratization of petabyte scale data pipelines.

[0] https://en.wikipedia.org/wiki/Density_functional_theory

[SkyMarshal]

By “exploration” he’s referring to Materials Science stuff, like figuring out molecular combinations that produce materials with desired properties. Not Archeological/GIS exploration.

[rjsw]

The examples of Materials Science and Archeology are both looking for patterns.