I see a lot of negative comments about how "AI" is a fad here. While I don't think it's uses are as great as advertised in the business sector, which is where most of this forum seems to originate, AI applications in academic discplines, especially the natural sciences haven't even begun scratching the surface. You can make a fantastic living keeping on top of the latest ML papers and having a degree in a physical science. There are so many obvious problems to be solved by AI algorithms that people haven't even gotten around to applying CNN's to yet. Source: me with 500 paper ideas in my head

As someone in this space, I think that most people vastly underestimate the actual practical considerations in converting a neat CNN or ML algorithm into a usable product. It's trivial to use the newest flavor of ML to write an academic paper and hold up a new high score for a particular problem. It is very hard to make that a usable commercial product, because often 95% accuracy is not good enough, or it's better to have a deterministic failure mode than to have a semi-random one.

scientific research doesn't always relate to 'products' - but scientific research still happens and needs to happen.

I agree with this a lot. When discussing this with someone who has been around ML/Optimization since the 70s he pointed out that it took 20 years for simple things like a Kalman filters to make their way out in to all the places in the world where they turned out to be useful. That is likely true of more modern ML technologies.

I'm still unconvinced Machine Learning can do much here. ML can predict something, but it cannot tell us why. You're only solving half the problem of 'science'.