Demis Hassabis: evaluation

Transcript context

…The idea of splitting the hypothesis space in two is super interesting. So I’ve heard you say that there’s basically no failure or failure is extremely valuable if you construct the questions right, if you construct the experiments right, if you design them right, that failure or success are both useful, so perhaps because it splits the hypothesis basically in two, it’s like a binary search? Yes, that’s right. So when you do real Blue Sky research, there’s no such thing as failure really. As long as you are picking experiments and hypotheses that meaningfully split the hypothesis space and you learn something. You can learn something kind of equally valuable from an experiment that doesn’t work. That should tell you if you’ve designed the experiment well and your hypotheses are interesting, it should tell you a lot about where to go next. And then you’re effectively doing a search process and using that information in very helpful ways. So to go to your dream of modeling a cell, what are the big challenges that lay ahead for us to make that happen? We should maybe highlight that in AlphaFold, I mean there’s just so many leaps. So AlphaFold solved, if it’s fair to say, protein folding. And there’s so many incredible things we could talk about there, including the open sourcing, everything you’ve released AlphaFold 3 is doing protein, RNA, DNA interactions, which is super complicated and fascinating. It’s amenable to modeling. AlphaGenome predicts how small genetic changes if we think about single mutations, how they link to actual function. So it seems like it’s creeping along to sophisticated to much more complicated things like a cell. But a cell has a lot of really complicated components.…

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