Prof. Ernie Davis – Automating Commonsense
Reasoning – Where Do We Stand?

Abstract: It is generally agreed that current AI systems are limited by their lack of commonsense knowledge and commonsense reasoning. However, there is no agreement on how to achieve those or even what they consist of. My talk will survey the state of the art. Why is common sense important for AI applications? What has been achieved? What remains challenging? What approaches have been taken, and what are their strengths and weaknesses?

Bio: Ernie Davis is Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University. He is an expert on commonsense reasoning for artificial intelligence. He is the author of five books, including “Representations of Commonsense Knowledge” (1990), and, with Gary Marcus, “Rebooting AI: Building Artificial Intelligence We Can Trust” (2019).

Prof. Cesar A. Hidalgo – The Design and Implementation of Data Distribution Systems

Abstract: During the last couple of decades, the world saw numerous open data efforts that fell short of expectations. In this presentation, I will present the story of a long-term research effort, starting in academia and then moving into industry, focused on the design and delivery of several data distribution systems, including among others, the Observatory of Economic Complexity (, Data USA (, and Data Mexico ( I use these examples to illustrate the common technical, institutional, and organizational challenges involved in the creation of data distribution systems, and show how these systems are used today to derive insight from data.

Bio: Cesar A. Hidalgo directs the Center for Collective Learning at the Artificial and Natural Intelligence Institute (ANITI) at the University of Toulouse. Prior to joining ANITI, he directed the Collective Learning group at MIT. Hidalgo holds a PhD in Physics from the University of Notre Dame, and is the author of dozens of peer reviewed papers and three books. His latest book is How Humans Judge Machines (MIT Press, 2021).