Why Foundation Models for Biology Have to Be Small

When a model generates a molecule, you don't find out if it worked until you've synthesized it in the wet lab. That makes the learning signal sparse and hallucinations expensive. In this interview, CTO Mathias Lechner talks with co-founder and Chief Science Officer Alexander Amini about building Liquid Foundation Models (LFMs) for modalities beyond text: biology, audio, vision, and time series. Alexander explains why efficiency is a direct advantage in biology, where a single input, such as a human genome, can run to billions of base pairs, and points to Liquid's partnership with Insilico Medicine as an early example in drug discovery. Subscribe to follow every interview: https://www.youtube.com/@liquid-ai-inc Careers at Liquid AI: https://www.liquid.ai/careers