QuantiPhy is a new benchmark and training framework that evaluates whether AI can numerically reason about physical properties in video images. QuantiPhy reveals that today’s models struggle with basic estimates of size, speed, and distance but offers a way forward.

Stanford, ETH Zurich, and EPFL will develop open-source foundation models that prioritize societal values over commercial interests, strengthening academia's role in shaping AI's future.

Stanford researchers have developed a deep learning model that transforms overwhelming brain data into clear trajectories, opening new possibilities for understanding thought, emotion, and neurological disease.

Renowned leaders in AI, medicine, and ethics join interdisciplinary committee guiding the world’s leading resource on AI trends.

Scholars develop a framework in collaboration with luxury goods multinational LVMH that lays out how large companies can flexibly deploy principles on the responsible use of AI across business units worldwide.

These models generate plausible timelines from historical patterns; without calibration and auditing, their “probabilities” may not reflect reality.