The two-day Cal Poly AI Symposium 2026 will bring together partners to examine the evolving role of artificial intelligence in higher education, industry and society.
Please join us for a special Noyce Distinguished Speaker Series event featuring technology executives Mike Abbott and Christopher Clark.
WHEN: May 7, 2026, beginning at 3:30 p.m.
WHERE: Chumash Auditorium, Cal Poly
Mike Abbott is a technology executive, professor, founder and investor who has built and scaled products, platforms and engineering teams across Apple, General Motors, Twitter, Palm, Microsoft and Kleiner Perkins. He is a founder and board member of Open Athena and teaches Stanford CS 153, focusing on frontier systems, AI infrastructure and large-scale technology organizations.
Christopher Clark is currently the director of Experiential Learning and an industry professor in the Department of Mechanical and Aerospace Engineering at New York University. He is also an emeritus professor at Harvey Mudd College, where he became a Fulbright Scholar. His recent industry work includes serving as a senior manager and research scientist at Apple for eight years. Clark’s research areas include multi-robot systems, underwater robot systems, machine learning, control theory, intelligent vehicles, state estimation and motion planning.
Presentation Topic
Generative AI has made significant advances in both its performance, and its use outside of the domains of language and image prediction. Robotics, for example, has benefited tremendously from these advancements: changing the way roboticists approach robot control, providing new representations of world models, and improving the predictions of other agents’ motion (e.g. how an autonomous vehicle may predict where other cars on the road will drive). This talk will present examples of such approaches, and focus on one of the biggest changes to autonomous robot control – the race for more data. Other models, like LLMs, can be trained with vast quantities of text extracted freely from the internet. But where does the data come from when training robot control policies? While we attempt to answer this question, we also postulate: what aspects of the work of a roboticist, and similar engineering roles, are being impacted by generative AI based tools that are affecting so many other industries? And relatedly, how is the university level education of roboticists impacted? These last questions seem largely answered, but maybe we begin by leveraging a key fact that differentiates robotics from other industries: robots are physical systems that can be touched, shaped, broken, repaired, catch on fire, sink, crash – and can also change the physical world around them.
Learn more and register here.
