The AAAI Award for Artificial Intelligence for the Benefit of Humanity recognizes positive impacts of artificial intelligence to protect, enhance, and improve human life in meaningful ways with long-lived effects. The award is given annually at the conference for the Association for the Advancement of Artificial Intelligence (AAAI).
This year, the AAAI Awards Committee has announced that the 2025 recipient of the award and $25,000 prize is Stuart J. Russell, “for his work on the conceptual and theoretical foundations of provably beneficial AI and his leadership in creating the field of AI safety”.
Stuart will give an invited talk at AAAI 2025 entitled “Can AI Benefit Humanity?”
About Stuart
Stuart J. Russell is a Distinguished Professor of Computer Science at the University of California, Berkeley, and holds the Michael H. Smith and Lotfi A. Zadeh Chair in Engineering. He is also a Distinguished Professor of Computational Precision Health at UCSF. His research covers a wide range of topics in artificial intelligence including machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, multitarget tracking, computer vision, computational physiology, and philosophical foundations. He has also worked with the United Nations to create a new global seismic monitoring system for the Comprehensive Nuclear-Test-Ban Treaty. His current concerns include the threat of autonomous weapons and the long-term future of artificial intelligence and its relation to humanity.
Read our content featuring previous winners of the award
- 2024 award #AAAI2024 invited talk: Milind Tambe – using ML for social good
- 2023 award: #AAAI2023 invited talk: Tuomas Sandholm on organ exchanges
- 2022 award: Cynthia Rudin wins AAAI Squirrel AI Award
- 2022 award: #AAAI2022 invited talk – Cynthia Rudin on interpretable machine learning
- 2021 award: Regina Barzilay wins $1M Association for the Advancement of Artificial Intelligence Squirrel AI award
- 2021 award: #AAAI2021 invited talk – Regina Barzilay on deploying machine learning methods in cancer diagnosis and drug design
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