Stanford CS25: Transformers United V6 I Advancing Science and Medicine with Collaborative AI Agents

From medical licensing to hypothesis generation

Vive's team evolved from Med-PaLM (the first AI to pass medical licensing exams) to building collaborative AI for scientific hypothesis generation after Dr. Gary Peltz suggested applying LLMs to identify causative genes for rare diseases.

Lab-validated AI hypotheses

An early PALM-based agent generated a hypothesis about gene factors that Dr. Peltz validated through CRISPR knockin experiments on mice, resulting in a peer-reviewed paper published in Advanced Science.

The 'build while falling' methodology

The project began without a clear technical roadmap, following the approach of committing to ambitious goals and engineering solutions during the attempt rather than waiting for perfect conditions.

System 1 versus System 2 thinking

Current LLMs like Gemini or GPT primarily exhibit System 1 thinking—fast, intuitive, surface-level pattern matching—while scientific discovery requires slow, deliberate, rigorous System 2 thinking that develops over weeks or months.

Quantifying scientific complexity

Scientific tasks range from literature reviews (hours) to paradigm-shifting theories like general relativity (decades or lifetimes), requiring AI capable of sustained long-horizon reasoning across this entire spectrum.

AlphaFold's specialized brilliance

While AlphaFold accomplished work equivalent to millions of scientist-years and earned a Nobel Prize, it lacks generality because it only processes protein sequences and cannot adapt to other scientific domains.

Language as the foundation of general intelligence

Natural language serves as the critical input/output interface for building general-purpose scientific AI, enabling systems to understand diverse problems and attempt solutions across domains much like the human brain.