I work in bioinformatics and this is the kind of thing I keep trying to communicate to people in the field. Yes, these AI tools (like AlphaFold) are amazing, but if there’s a significant gap in their training data, the AI is going to have that gap too (most of the structures in the protein database were solved via X ray crystallography, which isn’t great for studying highly flexible or disordered proteins)
Yes. My (minimally informed from a single class) understanding is that it sort-of depends on the problem too. Like perhaps in looking at all the data on proteins, the neural network might notice a pattern in protein folding is applicable to the tweaked problem. Of course, there is no guarantee that such a generally applicable rule exists. And even if it does, it might not be discovered by the net before overtraining occurs.
Ah, but where do you find the training set of all of the human-written good commit messages? 😃
Came to say this. Take my up vote.
I work in bioinformatics and this is the kind of thing I keep trying to communicate to people in the field. Yes, these AI tools (like AlphaFold) are amazing, but if there’s a significant gap in their training data, the AI is going to have that gap too (most of the structures in the protein database were solved via X ray crystallography, which isn’t great for studying highly flexible or disordered proteins)
Yes. My (minimally informed from a single class) understanding is that it sort-of depends on the problem too. Like perhaps in looking at all the data on proteins, the neural network might notice a pattern in protein folding is applicable to the tweaked problem. Of course, there is no guarantee that such a generally applicable rule exists. And even if it does, it might not be discovered by the net before overtraining occurs.