Skip to content
aiqbench takeAI Research

aiqbench analysis

John Platt's work on Google's Empirical Research Assistance (ERA) represents a significant advancement in AI for Science, specifically by automating the search for optimal solutions to "scoreable" scientific problems. This system leverages large language models and Monte Carlo Tree Search techniques to automate experimental design, effectively acting as an "auto-Kaggle" agent that can tackle complex research tasks previously requiring human intuition. The development underscores a critical shift where AI systems are evolving from tools for analysis into autonomous agents capable of driving empirical discovery across diverse scientific domains.
Source coveringLatent Space
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

🔬 An Oscar, Two Asteroids, and the Algorithm in Your sklearn: John Platt on AI for Science

Latent Space10 min read

We talked to Google’s Oscar winning “Giganerd” about automating science, solving climate change, and how future generations can contribute to science in the age of superintelligent AI

How often do you get to talk to a guest who has both an Academy Award and who invented textbook machine learning algorithms? John Platt has an Oscar, two textbook algorithms, two named asteroids, and an Erdos-Bacon number of 6. This was easily the most fun bio of all the guests we’ve read to date. And the result was an epic and fun chat covering Google’s Empirical Research Assistance (ERA), how AI can help battle climate change, and tons of great stories about the co-evolution of science and AI. John’s colleague Dave Bacon likes to tease John that his career has been defined by being twenty years early to the next big thing. This may be convolutional neural networks (some credit him with coining the term), fusion research, quantum computing. John and Google have been working on solving some of humanity’s hardest problems with AI and computation for well over a decade now. Recently John and his team set their sights on using AI to solve any scientific problem that can be written down as a score. John’s team has taken on many hard scientific problems over the years. In solving these, they noticed a pattern, many scientific problems can…

Excerpt shown under the publisher's RSS terms · 200 words · full text and figures are on the publisher's site

Related coverage