In a Stanford interview hosted by Dawn Song, Jeff Dean reflects on the origins of MoE, the transformative impact of deep learning, lessons from TensorFlow, how to spot breakthrough directions, AI agent risks, and his new venture Discovery Loop that aims to automate scientific research.
AutoScientists is a self‑organizing multi‑agent framework that automates the full scientific loop—from hypothesis generation to paper writing—demonstrating superior performance on BioML‑Bench (74.4% average rank, +8.33% over baselines) and achieving notable gains in protein‑engineering tasks such as ACE2‑Spike binding.
A Nature‑published study introduces “The AI Scientist,” a system that autonomously generates research ideas, designs and runs experiments, writes a full paper, and even self‑reviews, achieving the first AI‑only submission to pass ICLR peer review with a score above the acceptance threshold.
Within 228 hours, the Fully Automated Research System (FARS) built on Claude Code and other AI agents used 160 NVIDIA GPUs to produce 100 peer‑review‑level papers, achieving an average ICLR score of 5.05—higher than human submissions—while highlighting the expanding role, limits, and safety concerns of AI‑driven scientific automation.
