Dan Balsam: prediction

Transcript context

…Over time, more and more of the the tree of life has, like, been significantly shuffled and reordered over the past couple of decades as we've gotten better at understanding genomes. And, fundamentally, like, what defines a split branches on the tree of life is its genetic proximity. A species differentiate from each other when they cross some threshold of of genetic difference. But there is a lot of conservation across species, like sort of individual mutations, maybe random or, like, approximately random. But which mutations are adaptive and which mutations kill the organism are not random. Right? There's a ton of structure intrinsic in that. And there are consistent patterns or tendencies in which nucleotides turn to which other nucleotides, and all of this imposes, like, a a great amount of structure. And the hypothesis of, like, why why would you even train a autoregressive model on a bunch of genomes? I think the hypothesis for it was always that large models trained on diverse distributions of data eventually learn representations about the about the process that produced that distribution of data. In this case, the process that produced the distribution of data of all of the genomes and that are that have been sequenced, which was evo two was trained on, is evolution itself. And so our hypothesis coming into this was that because the tree of life itself is a natural ontology, there's a sort of, like, hierarchical structure where you have things that are similar and at some point they become more different. There's some, like, speciation of that and they become more different. That a model that…

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