Stuart Russell is a British computer scientist and professor at the University of California, Berkeley, best known as the co-author, with Peter Norvig, of Artificial Intelligence: A Modern Approach (AIMA), the most widely used introductory textbook in the field since its first edition in 1995.
Career
Born in 1962 in Portsmouth, England, Russell studied physics at Oxford before earning a PhD in computer science at Stanford University in 1986. He joined Berkeley the same year and has remained there since, working on topics spanning probabilistic reasoning, knowledge representation, and Reinforcement learning. AIMA, now in multiple editions, presents Artificial intelligence as a unified field organized around the concept of a rational agent, and has shaped how generations of students are introduced to the subject, including the search, logic, planning and Machine learning material that underpins much of modern AI.
Human-compatible AI
Since the mid-2010s, Russell has become one of the most prominent academic voices arguing that the "standard model" of AI, in which a system is built to optimize a fixed, human-specified objective, is fundamentally risky as systems become more capable, because any misspecified objective can be pursued in unintended and harmful ways. In his 2019 book Human Compatible, he proposes an alternative he calls "provably beneficial" AI, in which machines are designed to remain deliberately uncertain about human preferences and to defer to human oversight, rather than confidently pursuing a hard-coded goal, a framing closely tied to the broader field of AI alignment research.
Russell founded and directs the Center for Human-Compatible AI (CHAI) at Berkeley, which studies technical approaches to keeping advanced AI systems under meaningful human control. He was a signatory of the 2023 Pause Giant AI Experiments letter calling for a moratorium on training the most powerful models, and has testified before national and international bodies on AI risk and regulation, positioning his work within the wider conversation about AI safety and Existential risk from AI alongside researchers such as Yoshua Bengio. Russell has argued that the field's own stated long-term goal, building machines that meet or exceed human-level general intelligence across essentially all tasks, obligates researchers to take seriously what happens if that goal is achieved without solving the control problem first, a concern also central to work on Artificial general intelligence safety.