The annual report reveals a field hitting breakthrough capabilities while raising urgent questions about environmental costs, transparency, and who benefits from the technology.

As artificial intelligence becomes central to national security, experts grapple with a technology that remains unpredictable, unregulated, and increasingly powerful.

Stanford computer scientist James Zou is exploring how AI can accelerate scientific research and peer review. His finding: AI excels at spotting gaps, but judgment calls still need humans.

The team is building a shared “conceptual grounding” so that artists can steer models with precision.

Stanford scientists have released an open-source platform that lets health researchers study the “screenome” – the digital traces of our daily lives – while protecting participants’ privacy.

Stanford scientists in Senegal hunting for schistosomiasis—a parasitic disease infecting 200+ million people worldwide—used AI to transform local field work into satellite-powered disease mapping.

An Amazon-backed fellowship will support 10 Stanford PhD students whose work explores everything from how we communicate to understanding disease and protecting our data.

We need to rethink student assessment, AI literacy, and technology’s usefulness, according to experts at the recent AI+Education Summit.

Governments worldwide are racing to control their AI futures, but unclear definitions hinder real policy progress.

As trust in the old order erodes, mid-sized countries are building new agreements involving shared digital infrastructure and localized AI.

A Stanford HAI workshop brought together experts to develop new evaluation methods that assess AI's hidden capabilities, not just its test-taking performance.

World leaders focused on ROI over hype this year, discussing sovereign AI, open ecosystems, and workplace change.

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.