The Catz Cosmos Lecture in Human-Centered AI annually brings a distinguished speaker to the University of Oxford, in collaboration with the Cosmos Institute and St Catherine’s College.
The Cosmos Institute is a non-profit organization dedicated to promoting human flourishing in the age of artificial intelligence. Through research, fellowships, grants, and education, Cosmos aims to cultivate a new generation of technologists and entrepreneurs equipped with deep philosophical thinking to navigate the uncharted territory of our AI age.
HAI Lab, launched that evening under the leadership of Professor Philipp Koralus, embodies these crossovers.
To learn more about Philipp’s vision and the “philosophy to code” pipeline, please visit HAI Lab’s new website.
Catz Cosmos Lecture 2024
Professor Koralus introducing Professor Valiant
The inaugural Cosmos Lecture was delivered by Turing Laureate Leslie Valiant of Harvard University on “Educability” in October 2024 at the Oxford University Museum of Natural History.
Click here or the image above to replay the lecture.
Educability
Leslie Valiant
T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University
“We seek to define the capability that has enabled humans to develop the civilization we have, and that distinguishes us from other species. For this it is not enough to identify a distinguishing characteristic – we want a capability that is also explanatory of humanity’s achievements. “Intelligence” does not work here because we have no agreed definition of what intelligence is or how an intelligent entity behaves. We need a concept that is behaviorally better defined. The definition will need to be computational in the sense that the expected outcomes of exercising the capability need to be both specifiable and computationally feasible. This formulation is related to the goals of artificial intelligence research but is not synonymous with it, leaving out, for example, the many capabilities we share with other species.
We make a proposal for this essential human capability, which we call “educability.” It synthesizes abilities to learn from experience, to learn from others, and to chain together what we have learned in either mode and apply that to particular situations. It starts with the now standard notion of learning from examples as captured by the Probably Approximately Correct model and used in machine learning. The demonstrated ability of Large Language Models learning to generate smoothly flowing prose is a clue that pursuing computationally well-defined tasks constitutes a promising approach to simulating human capabilities. The basic question then is how to extend this approach to encompass broader human capabilities beyond learning from examples. This is what the educability notion aims to answer.
What we ask computers to do, in the main, reflects human capabilities. Hence a better understanding of human capabilities can be expected to provide goals for our future technology.”