9 papers
Two AI Metrics Diverged: Will it Make All the Difference?
Alex Fogelson, Zachary A. Brown, Hans Gundlach +2
As exponential compute scaling continues, will the capabilities of frontier AI models outstrip what is accessible to developers on a small fixed budget? Or will capabilities conver…
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…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
The Price of Progress: Price Performance and the Future of AI
Hans Gundlach, Jayson Lynch, Matthias Mertens +1
Language models have seen enormous progress on advanced benchmarks in recent years, but much of this progress has only been possible by using more costly models. Benchmarks may the…
On the Origin of Algorithmic Progress in AI
Hans Gundlach, Alex Fogelson, Jayson Lynch +4
Algorithms have been estimated to increase AI training FLOP efficiency by a factor of 22,000 between 2012 and 2023 [Ho et al., 2024]. Running small-scale ablation experiments on ke…
Quantum Deep Learning Still Needs a Quantum Leap
Hans Gundlach, Hrvoje Kukina, Jayson Lynch +1
Quantum computing technology is advancing rapidly. Yet, even accounting for these trends, a quantum leap would be needed for quantum computers to meaningfully impact deep learning…