Speaker unverified: commitment

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…But it's so incredibly complex, isn't it? Because the human body is alive and there is a symphony of complex adaptive compensatory mechanisms. I guess the the idea here is that we're proposing a mechanistic understanding of how this works, which means we can design interventions that are very effective. But in machine learning, we've kind of learned the opposite lesson, which is that all of our intuitions about how things work don't don't really work, we need lots of data, and we need to test lots of things. Could it be a similar thing here that, you know, it's like whack a mole. You you kind of you you you do 1 thing, and then something else compensates. I think really important in a certain sense is almost the humility of AlphaFold. In that, you know, people say, you know, we are trying to predict what this experiment will give you. We are not trying to tell you everything. We are not a model of the entire cell. We are a predictor of this experiment that you did all the time and took you a year. And so in a certain sense, I think and so we have validity in that I can characterize very well how well we're we we will reproduce that experiment. And then people figure out how to take this machine and use it in other ways that we didn't expect to find out new you know, discover new mechanisms, to try thousands of alpha fold predictions, to find 2 proteins that stick together, and find this unknown component of this complex system. So people are finding ways to push this further. But in a certain sense, we are narrow, or we predict the result of a scientific paper. We predict the result of a scientific paper that often appears in Nature and Science and Cell and these big journals. Right? We predict nature level science with the press of a button in a very narrow category of nature level science of the structure of a specific protein. But there's this enormous wide universe of biology that ultimately we're gonna have to figure out and understand what data will we pin ourselves to, what experiments will we predict, and predict really, really well such that you know, I mean, maybe the other story of machine learning is that predicting things okay is alright. Predicting things extraordinarily well starts to produce amazing machines. We see this, of course, in language models, in image generation, but also in protein. So I think this this kind of thing, we aren't building just 1 universal biology machine, or at least if we do, it will have to look a lot more like a language model than it will kind of a narrow predictor. But we are doing something truly useful. Can we talk through the predictive architectures of of the different versions of AlphaFold? So, you know, the 1st version was was a CNN. The last version is a diffusion model. The 2nd version, we spoke about this last night, it had a structure component. And, you know, obviously, geometric deep learning is spoken about a lot. And I think people misattributed the the benefit of having these, you know, and kind of symmetries. It did these SC 3 symmetries. And just just talk me through that process because you were kind of saying at the beginning, you were really trying to imbue…

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