Brandon Anderson

RJ Honicky hosts Latent Space AI for Science with Brandon Anderson.

RJ Honicky opens the episode as CTO of MiraOmics and introduces his co-host, Brandon Anderson, who develops RNA therapeutics at Atomic AI. The guest in the studio is Ian Quigley. The opening calls Ian Quigley the co-founder and CEO of Leash Bio, and describes the company as one of the favorite young tech-bio companies of the last few years. The reasons it gives are epistemic rigor, data collection at a large scale, a commitment to open science, and a run of blog posts about how to do machine learning in a hard domain.

Ian Quigley thanks them. He says he and his colleagues founded Leash Bio after trying to predict which sma

Sal Candido says there is a bitter lesson for data, but scaling laws are not everywhere and they do not always exist. Brandon Anderson asked the question after defining the bitter lesson as the claim that methods which scale eventually win. A lot of the work is finding the situation where more compute and more data actually produce a better result. Once that happens, the rest can become an engineering problem.

The result still depends on the data having the right information for the problem. Without that, more scale does not yield a model with the capabilities or the understanding you want. You can only pull the information out of the data you have, then use it to generalize beyond that dat

Ekin Dogus Cubuk says materials data is never deleted because any measurement the team can collect is precious. A person cannot watch every atom. If every position were visible, the experiment would hold more data than a computer can store, but that full record is not available. What the instruments return is already a thin slice, so throwing a reading away means losing something the lab cannot recapture.

Brandon Anderson compares the measurement to linear probing of a foundation model. The material is the large model. The experiment is a tiny probe, about eight bits of output, and a human or an agent has to rebuild what is happening inside from that coarse reading.

Brandon Anderson says Phil Anderson's line, more is different, means someone can know the basic laws and still be wrong about what happens when many parts are put together. The collective behavior is qualitatively different from what the simple laws suggest. The starting laws look simple, so the behavior of many atoms looks like it should be simple too. It is not. Collective behavior is hard to model.

Ekin Dogus Cubuk adds that the behavior is emergent and universal. The same pattern shows up in deep learning, where power laws appear across models and are not understood, yet they resemble the emergence physicists already study. He wants AI applied to materials because theory, computation, a