3 ms·
Your question fundamentally falls into the area of unanswerable philosophy akin to "do insects feel pain?" But there's a reasonable intuition suggesting that t
by lrem 4y ago
Your question fundamentally falls into the area of unanswerable philosophy akin to "do insects feel pain?"
But there's a reasonable intuition suggesting that the answer to your question is "no". What we're looking at is a non-linear regression model reproducing the function (which according to the article isn't really a function, but that's above both my and the model's knowledge) from a gene sequence to a 3d structure. It is heavily meta-optimised, so the "why's" would only be in the model, if reproducing the process of folding the protein was the cheapest way to guess the structure (). Intuitively it introduces at least one extra dimension, so should be way more expensive than finding analogues among known sub-aspects of the function. Hence, I would expect none of the "why's" to be in there.
Sadly, if any insight for the "why's" was there after all, we don't have a method to extract it anyway.
Disclaimer: I work in Google, far away from DeepMind, have no internal knowledge on this.
- salty_biscuits 4y ago"Sadly, if any insight for the "why's" was there after all, we don't have a method to extract it anyway." This has been my central frustration with working in ML. People always expect a "why" to exist, and by why I mean a cogent narrative explanation to complex phenomena. Maybe there is no "why" like this for a bunch of physical phenomena, maybe it is just a bunch of low level intricate stuff interacting in complex ways. There might be an emergent model that you can get a useful predictive model for with an ML model, then people get mad because the prediction doesn't solve the real meta problem that they were expecting to solve via the sub problem (e.g. solve folding then get mad because folding itself turns out not to be super useful because we don't know which protein to target, solve image classification then get mad because that doesn't make it easy to make a self driving car, etc, etc). "More is different" is definitely an idea in physics that needs to propagate into other fields to temper our expectations.