9 citations · 10 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
Introducing the Quantum Economic Advantage Online Calculator
Frederick Mejia, Hans Gundlach, Jayson Lynch +5
Developing a systematic view of where quantum computers will outperform classical ones is important for researchers, policy makers and business leaders. But developing such a view…
Quantum Advantage in Computational Chemistry?
Hans Gundlach, Keeper Sharkey, Jayson Lynch +8
For decades, computational chemistry has been posited as one of the areas in which quantum computing would revolutionize. However, the algorithmic advantages that fault-tolerant qu…
Meek Models Shall Inherit the Earth
Hans Gundlach, Jayson Lynch, Neil Thompson
The past decade has seen incredible scaling of AI systems by a few companies, leading to inequality in AI model performance. This paper argues that, contrary to prevailing intuitio…