Executive Summary

• Automation of the experimental loop is a key focus area
• Applicability across many fields of science and engineering
• Strong expertise in machine learning and large-scale systems is required

The Internet’s Verdict: 70% Hyped, 30% Skeptical

Introduction to Discovery Loop

Experts discuss the potential of automating the experimental loop.

Our general approach is to automate the experimental loop. We think this approach is broadly applicable across many different fields of science and engineering.

The approach has far-reaching implications for various fields, including the fourteen NAE Grand Challenges for Engineering.

Expert Insights

Some experts draw parallels with existing projects.

This seems to be an institutional, massively scaled version of https://github.com/karpathy/autoresearch.

Others question the feasibility of automation in certain domains.

How do you automate experimentation? Doubtlessly, AI can iterate at superhuman speeds in the domains of thought and design: Software, mathematical proofs, literature search.

The founding team’s mission statement is seen as pragmatic.

Their mission statement reads pretty pragmatic compared to other AI companies (the bar is very low…)

Focus Keyword: Discovery Loop