Machine Learning

This article discusses the transformative potential of using large language models as uncertainty-calibrated optimizers for scientific experimentation. It highlights how these models can enhance discovery processes in chemistry and materials science by effectively managing exploration and exploitation.

UC Berkeley and QuantrolOx are collaborating to move superconducting quantum computing from manual laboratory settings to automated industrial processes. The partnership leverages AI-driven software and physical hardware testbeds to optimize the entire quantum device lifecycle.

Security researchers have proven that open-weight AI models can be maliciously backdoored using minimal resources and training data. This discovery highlights a critical lack of observability and verification capabilities in the current AI supply chain.