Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path? | Richard M. Karp Distinguished Lecture

The leading AI companies are increasingly focused on building generalist AI agents — systems that can autonomously plan, act, and pursue goals across almost all tasks that humans can perform. Despite how useful these systems might be, unchecked AI agency poses significant risks to public safety and security, ranging from misuse by malicious actors to a potentially irreversible loss of human control. In this talk, Yoshua Bengio will discuss how these risks arise from current AI training methods. 

Indeed, various scenarios and experiments have demonstrated the possibility of AI agents engaging in deception or pursuing goals that were not specified by human operators and that conflict with human interests, such as self-preservation. Following the precautionary principle, Bengio and his colleagues see a strong need for safer, yet still useful, alternatives to the current agency-driven trajectory. Accordingly, they propose as a core building block for further advances the development of a non-agentic AI system that is trustworthy and safe by design, which they call Scientist AI. This system is designed to explain the world from observations, as opposed to taking actions in it to imitate or please humans. It comprises a world model that generates theories to explain data and a question-answering inference machine. Both components operate with an explicit notion of uncertainty to mitigate the risks of overconfident predictions. 

In light of these considerations, a Scientist AI could be used to assist human researchers in accelerating scientific progress, including in AI safety. In particular, this system could be employed as a guardrail against AI agents that might be created despite the risks involved. Ultimately, focusing on non-agentic AI may enable the benefits of AI innovation while avoiding the risks associated with the current trajectory. Bengio and his colleagues hope these arguments will motivate researchers, developers, and policymakers to favor this safer path.

Yoshua Bengio is a full professor in the Department of Computer Science and Operations Research at Université de Montréal, as well as the founder and scientific director of Mila and the scientific director of IVADO. He also holds a Canada CIFAR AI chair. Considered one of the world’s leaders in artificial intelligence and deep learning, he is the recipient of the 2018 A.M. Turing Award, considered the “Nobel Prize of computing.”

He is a fellow of both the U.K.’s Royal Society and the Royal Society of Canada, an officer of the Order of Canada, a knight of the Legion of Honor of France, and a member of the U.N.’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology.

The Richard M. Karp Distinguished Lectures were created in Fall 2019 to celebrate the role of Simons Institute Founding Director Dick Karp in establishing the field of theoretical computer science, formulating its central problems, and contributing stunning results in the areas of computational complexity and algorithms. Formerly known as the Simons Institute Open Lectures, the series features visionary leaders in the field of theoretical computer science and is geared toward a broad scientific audience.

Light refreshments will be available at 3:30 p.m., prior to the start of the lecture. 

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