❗️AI hits biology’s data bottleneck

AI biology has reached a problem GPUs alone can’t solve: there simply isn’t enough training data.

A $1.8 billion effort backed by Biohub, the U.S. Department of Energy, NIH, Meta, Google DeepMind and Isomorphic Labs aims to build a “universal virtual cell” that could predict how cells respond to interventions and let researchers test biological experiments digitally before doing them in the lab.

Today’s datasets contain hundreds of millions of cells. Future models may need billions and eventually trillions. The goal is to compress work that normally takes decades into about five years, with the first dataset expected in roughly one year.

There’s also an unusual access model: commercial funders get one year of exclusive access to the data they help generate before it becomes public. Government-funded data has no such embargo.

@aipost 🏴