alt Hacker NewsSounds like he was doing an xray diffraction experiment? The last time (in my opinion) XRay diffraction based structure results meaningfully changed scientific discourse that affects human life was probably in the 80s or 90s. While it's important work it's no more important for Healthcare than some physics guy doing things with a random metal alloy. The point is there are interesting things but one shouldn't delude that this is the thing that's keeping us from unleashing human health prosperity.
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There were https://en.wikipedia.org/wiki/Galectin proteins embedded in a thin lipid layer on the surface of the water. The goal was to understand what conditions triggered various conformational changes. I'm under the impression that such details end up in databases and get selected for further inquiry by drug design processes, particularly those targeting autoimmune diseases. Or at least, that's what I got from his talk on it. I'm still working my way up to the biochemistry classes.
But yeah I get your point about avoiding that delusion. Honestly I'd be happy enough to be doing something that I suspect is not actively harmful (this should be easy but SaaS tends to converge on products that control their users and not the other way around). I don't need to be humanity's savior. More helpful than harmful will be enough.
There's two kinds of ignorance which come into contact when people work across disciplines.
In my own work helping ecologists, I see plenty of CS/ML folks who think they'll change the world by throwing a transformer at the problem. (which problem? you think we haven't tried that?) It takes some time and exposure to figure out what kinds of problems you can meaningfully contribute on.
On the other hand, I've met lots (most?) of ecologists who underestimate the impact of looking at their work through CS/ML lenses. Effective automation can greatly improve iteration speed, which ultimately leads to better outcomes than a slow but 'perfect' process. (and, indeed, the 'slow-but-perfect' process may not be sufficiently benchmarked, and not be perfect at all...)
You can do a lot of good by working closely with a practitioner, and identifying the places where they are spending a lot of time doing 'boring' stuff, and finding ways to automate or approximate the outcomes of that boring work. As you work with more people, you'll be able to identify boring stuff that everyone in the field is stuck doing.
So, in short, an excellent goal is to find ways to save people time through bottleneck analysis. Improve iteration speed and you improve the speed at which we can accumulate knowledge / make discoveries / etc. When you're done, it's "just a tool", but beforehand it's a problem holding back discovery.