I am curious to hear an expert weigh in on this approach's implications for protein folding research. This sounds cool but it's really unclear to me what the implications are
https://genomely.substack.com/p/simplefold-and-the-future-of...
But as with anything in research, it will take months and years to see what the actual implications are. Predictions of future directions can only go so far!
People often like to say that we just need one more algorithmic breakthrough or two for AGI. But in reality it's the dataset and the environment based learning. Almost any model would do if you collected the data. It's not in the model, it's outside where we need to work on.
Doing too many things at once makes methods hard to adopt and makes conclusions harder to draw. So we try to find simple methods that show measurable gain, so we can adapt it to future approaches.
Its a cycle between complexity and simplicity. When a new simple and scalable approach beats the previous state of art, that just means we discovered a new local maxima hill to climp up.