This week’s Paper Club is all about AI for biology. Sequencing costs have collapsed, biology is gen...
This week’s Paper Club is all about AI for biology.
Sequencing costs have collapsed, biology is generating more data than researchers can make sense of, and the models are beginning to catch up. The opportunity now is to move from reading biological data to modeling and designing living systems.
We brought together researchers and builders working across cancer genomics, virtual cells, genome language models, and biological design to show what that shift looks like in practice.
We start with a single patient, asking how AI can build a research program around their cancer, then zoom out to whole cells and genomes. Along the way, we look inside a language model trained on DNA and explore how in-context learning emerged even though the model was never explicitly trained to do it.
00:00 - Is the Turing test solved? A live experiment
03:22 - Why now is the best time for AI Bio
08:26 - Alfredo Gonzalez: cancer as a data problem
29:50 - Yusuf Roohani: virtual cells and autonomous research
51:47 - Garyk Brixi: what a model learns from DNA
1:09:57 - Divya Nori: few-shot prompting for biological design
Sequencing costs have collapsed, biology is generating more data than researchers can make sense of, and the models are beginning to catch up. The opportunity now is to move from reading biological data to modeling and designing living systems.
We brought together researchers and builders working across cancer genomics, virtual cells, genome language models, and biological design to show what that shift looks like in practice.
We start with a single patient, asking how AI can build a research program around their cancer, then zoom out to whole cells and genomes. Along the way, we look inside a language model trained on DNA and explore how in-context learning emerged even though the model was never explicitly trained to do it.
00:00 - Is the Turing test solved? A live experiment
03:22 - Why now is the best time for AI Bio
08:26 - Alfredo Gonzalez: cancer as a data problem
29:50 - Yusuf Roohani: virtual cells and autonomous research
51:47 - Garyk Brixi: what a model learns from DNA
1:09:57 - Divya Nori: few-shot prompting for biological design
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