I’m not sure the problem in biology is decoding. At least not in the same sense it is with neural networks. I see the main difficulty in biology more one of mechanistic inference where a major roadblock may be getting better measurements of what is going on in cells over time rather some algorithm that’s just going to be able to overcome the fact that you’re getting both very high levels of molecular noise in biological data and single snapshots in time that are difficult to place in context. With a neural network you have the parameters and it seems reasonable to say you just need some math to make it more interpretable.
Whereas in biology I think we likely need both better measurements and better tools. I’m not sure the same tools would be particularly applicable to the ai interpretability problem either.
If, for example, I managed to create mathematical tools to reliably learn mechanistic dependencies between proteins and/or genes from high dimensional biological data sets, it’s not clear to me that would be easily applicable to extracting bayes nets from large neural networks.
I’m coming at this from a comp bio angle so it’s possible I’m just not seeing the connections well, having not worked in both fields.
I’m not sure the problem in biology is decoding. At least not in the same sense it is with neural networks. I see the main difficulty in biology more one of mechanistic inference where a major roadblock may be getting better measurements of what is going on in cells over time rather some algorithm that’s just going to be able to overcome the fact that you’re getting both very high levels of molecular noise in biological data and single snapshots in time that are difficult to place in context. With a neural network you have the parameters and it seems reasonable to say you just need some math to make it more interpretable.
Whereas in biology I think we likely need both better measurements and better tools. I’m not sure the same tools would be particularly applicable to the ai interpretability problem either.
If, for example, I managed to create mathematical tools to reliably learn mechanistic dependencies between proteins and/or genes from high dimensional biological data sets, it’s not clear to me that would be easily applicable to extracting bayes nets from large neural networks.
I’m coming at this from a comp bio angle so it’s possible I’m just not seeing the connections well, having not worked in both fields.