Nice!

I think I should look into this. I sense that I lack a general understanding of what is really possible with ML. (My current understanding is weighted toward underestimating what is possible with ML).

That's a very good list. I think we're just starting to scratch at the surface of what is possible, which is why I've devoted myself to working in this field!

I got most of these right away, but could you expand on what you mean by "high throughput imaging (chemo/bioinformatics)"?

The high throughput imaging that I'm familiar with is in regards to cell imaging. Cells are cultured in high-density plates (96-well or 384-well dishes), each well is given a different experimental condition (drugs, RNAi, etc) and then imaged on an automated microscope.

As you can imagine, this generates tons of data. Our lab did a highthrouput screen of genetic mutants in neurons, and then used software to quantify basic morphology such as neurite length , arborization, and cell death.

Crystallographers will use a similar system to bathe their protein in billions of compounds to find the right combination for crystallizing. Automated cameras will capture images and try to identify which ones have crystallized so the researcher doesn't have to do it by hand.

robots chugging away at petri dishes that contain hundreds of mini-dishes with chemicals/biostuff. Making sense of the millions/billions of data points is the job of machine learning.