Personally, my area of interest is scientific discovery. Could a model not dissimilar from what we have today, if asked a cogent question, not answer it with an experiment that could be carried out? For example, one of the most important experiments, Avery-MacCleod, which proved (to the extent that you can prove anything in biology) that DNA, not protein, was the primary element of heredity, is not all that complicated, and the mechanical details seem nearly in reach of modern ML techniques. Similarly, could the ML model provide a significant advance in the area of understanding the molecular function in intimate detail of proteins as determined by their structure (which AlphaFold does not do, yet), complete with experimental instructions on how to verify these hypotheses? As of this time, my review of modern ML methods for science suggest we have made some advances, but still have not passed the "phase transition" demonstrating superscientist-level understanding of any field. But perhaps it will just fall out naturally from improved methods for media generation/parsing and ad targeting.
I continue to remain hopeful that within my remaining 20-40 or so years (I'm a typical american male, age 51, with a genome that contains no known risk factors) I will see something like what Vinge describes in https://edoras.sdsu.edu/~vinge/misc/singularity.html in a way that is demonstrable and safe, but honestly, I think it could go in any number of directions from "grim meat-hook future" to "unexpected asteroid takes out human life on the planet, leaving tardigrades to inherit the earth" to "kardyshev-scale civilization".
