3.2k citations · 30.4k across the 180 of their papers we have counts for
183 papers
Provably safe systems: the only path to controllable AGI
Max Tegmark, Steve Omohundro
We describe a path to humanity safely thriving with powerful Artificial General Intelligences (AGIs) by building them to provably satisfy human-specified requirements. We argue tha…
Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability
Ziming Liu, Eric Gan, Max Tegmark
We introduce Brain-Inspired Modular Training (BIMT), a method for making neural networks more modular and interpretable. Inspired by brains, BIMT embeds neurons in a geometric spac…
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
Ziqian Zhong, Ziming Liu, Max Tegmark +1
Do neural networks, trained on well-understood algorithmic tasks, reliably rediscover known algorithms for solving those tasks? Several recent studies, on tasks ranging from group…
The Quantization Model of Neural Scaling
Eric J. Michaud, Ziming Liu, Uzay Girit +1
We propose the Quantization Model of neural scaling laws, explaining both the observed power law dropoff of loss with model and data size, and also the sudden emergence of new capa…
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
Yilun Xu, Ziming Liu, Yonglong Tian +3
We introduce a new family of physics-inspired generative models termed PFGM++ that unifies diffusion models and Poisson Flow Generative Models (PFGM). These models realize generati…
Precision Machine Learning
Eric J. Michaud, Ziming Liu, Max Tegmark
We explore unique considerations involved in fitting ML models to data with very high precision, as is often required for science applications. We empirically compare various funct…