26 citations · 41 across the 6 of their papers we have counts for
6 papers
Detecting Hidden ML Training With Zero-Overhead Telemetry
Robi Rahman, Sabiha Tajdari
Hardware-enabled monitoring of GPU workloads underpins many proposals for AI compute governance, but if developers can defeat monitoring mechanisms, such schemes are unworkable. We…
Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Alexander K. Saeri, Jess Graham, Michael Noetel +185
Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritizat…
Does Distributed Training Undermine Compute Governance?
Robi Rahman
Compute governance proposals often rely on the assumption that frontier AI training requires large, detectable computing clusters. However, recent advances in distributed training…
Trends in AI Supercomputers
Konstantin F. Pilz, James Sanders, Robi Rahman +1
Frontier AI development relies on powerful AI supercomputers, yet analysis of these systems is limited. We create a dataset of 500 AI supercomputers from 2019 to 2025 and analyze k…
The rising costs of training frontier AI models
Ben Cottier, Robi Rahman, Loredana Fattorini +3
The costs of training frontier AI models have grown dramatically in recent years, but there is limited public data on the magnitude and growth of these expenses. This paper develop…
Algorithmic progress in language models
Anson Ho, Tamay Besiroglu, Ege Erdil +6
We investigate the rate at which algorithms for pre-training language models have improved since the advent of deep learning. Using a dataset of over 200 language model evaluations…