David Krueger
Papers
1
Total Citations
15
H-Index
1
About
David Krueger is a prominent researcher in artificial intelligence, whose work sits at the critical intersection of deep learning theory and AI safety. He is best known for his foundational contributions to understanding neural network scaling, most notably through his 2022 paper "Broken Neural Scaling Laws." This influential work introduced a smoothly broken power law functional form that accurately models and extrapolates how a neural network's performance changes with compute, revealing that scaling trends are not uniform but shift at critical thresholds. With 15 citations already, this paper has become essential reading for researchers seeking to predict and optimize model behavior. Beyond scaling, Krueger has made significant strides in AI alignment, exploring how to ensure increasingly capable systems remain robust and beneficial. His research is characterized by a rare combination of rigorous mathematical theory and pressing practical concerns, making him a key voice in conversations about the future of safe and predictable AI development.
Research Focus
Key Achievements
Top Papers
- 1Broken Neural Scaling Laws15 citations · 2022