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I would compare this question to "creating a new page on the Internet just adds to a countless pile of URLs. How important can any one really be?" And this lea

I would compare this question to "creating a new page on the Internet just adds to a countless pile of URLs. How important can any one really be?" And this leads us to: most will be slop, but if you can figure out effective ways to perform (a) Search, and (b) Alerts, then this scenario is definitely a game-changer. Let's take protein synthesis: imagine if we were able to programmatically generate an accurate paper describing every property of a given protein structure. And we just ran this for every single protein in the Universe. You'd end up with a seemingly infinite number of papers, most of which would be useless. But if you (scientist or engineer) could effectively look up "binds to receptor X and causes effect Y" and see all valid candidates within milliseconds, it would be more valuable than any technology we've ever come up with. If you could, also, set an alert eg. "tell me about any combination that has superconducting properties" and get notified when this one-in-a-trillion protein is found, this would also be more valuable than any technology we've ever come up with.

_heimdall 2y ago
That actually raises a more fundamental question here. This project specifically focused their tests on research topics that can feasibly be tested by the ML tools, writing software. I assume that was an intentional decision, and a clever one that let them point to promising test results while ignoring that potential limitation when valuing their tool. These ML tools will need to not only come up with novel ideas, they'll need a way to test and validate them. For anything outside of software that almost certainly means modelling. If we already have validated models that may work well enough, but if you extend the scope to literally any novel protein that is possible the ML tool would first have to figure out how to model it. What would that even look like? How would an ML tool trapped in a computer and limited to the knowledge it was trained on be able to model any protein in the universe and be able to validate exactly how it would function in the real world?

_heimdall 2y ago

That actually raises a more fundamental question here. This project specifically focused their tests on research topics that can feasibly be tested by the ML tools, writing software. I assume that was an intentional decision, and a clever one that let them point to promising test results while ignoring that potential limitation when valuing their tool. These ML tools will need to not only come up with novel ideas, they'll need a way to test and validate them. For anything outside of software that almost certainly means modelling. If we already have validated models that may work well enough, but if you extend the scope to literally any novel protein that is possible the ML tool would first have to figure out how to model it. What would that even look like? How would an ML tool trapped in a computer and limited to the knowledge it was trained on be able to model any protein in the universe and be able to validate exactly how it would function in the real world?