14 citations · 14 across the 5 of their papers we have counts for
5 papers
The Singapore Consensus on Global AI Safety Research Priorities
Yoshua Bengio, Tegan Maharaj, Luke Ong +84
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to ensure that AI is safe, i.e., trustworthy,…
HCAST: Human-Calibrated Autonomy Software Tasks
David Rein, Joel Becker, Amy Deng +19
To understand and predict the societal impacts of highly autonomous AI systems, we need benchmarks with grounding, i.e., metrics that directly connect AI performance to real-world…
Measuring AI Ability to Complete Long Software Tasks
Thomas Kwa, Ben West, Joel Becker +23
Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear. To quantify the capabilities of AI systems in terms of human capabilities,…
Compact Proofs of Model Performance via Mechanistic Interpretability
Jason Gross, Rajashree Agrawal, Thomas Kwa +5
We propose using mechanistic interpretability -- techniques for reverse engineering model weights into human-interpretable algorithms -- to derive and compactly prove formal guaran…
Securing Smart Home Edge Devices against Compromised Cloud Servers
Rahmadi Trimananda, Ali Younis, Thomas Kwa +2
Smart home IoT systems often rely on cloud-based servers for communication between components. Although there exists a body of work on IoT security, most of it focuses on securing…