Stanford AI Experts Predict What Will Happen in 2026
The era of AI evangelism is giving way to evaluation. Stanford faculty see a coming year defined by rigor, transparency, and a long-overdue focus on actual utility over speculative promise.
The era of AI evangelism is giving way to evaluation. Stanford faculty see a coming year defined by rigor, transparency, and a long-overdue focus on actual utility over speculative promise.
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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.
Why 'Zero-Shot' Clinical Predictions Are Risky
These models generate plausible timelines from historical patterns; without calibration and auditing, their “probabilities” may not reflect reality.
Readers wanted to know if their therapy chatbot could be trusted, whether their boss was automating the wrong job, and if their private conversations were training tomorrow's models.
