76 citations · 98 across the 8 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.LG2023★ 2 cited
Tools for Verifying Neural Models' Training Data
Dami Choi, Yonadav Shavit, David Duvenaud
It is important that consumers and regulators can verify the provenance of large neural models to evaluate their capabilities and risks. We introduce the concept of a "Proof-of-Tra…
cs.CY2023★ 76 cited
Frontier AI Regulation: Managing Emerging Risks to Public Safety
Markus Anderljung, Joslyn Barnhart, Anton Korinek +21
Advanced AI models hold the promise of tremendous benefits for humanity, but society needs to proactively manage the accompanying risks. In this paper, we focus on what we term "fr…
cs.LG2023★ 12 cited
What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring
Yonadav Shavit
As advanced machine learning systems' capabilities begin to play a significant role in geopolitics and societal order, it may become imperative that (1) governments be able to enfo…