Dear colleagues, you are cordially invited to the following talk in the Developing Minds global online lecture series (https://sites.google.com/view/developing-minds-series/home): Josh Tenenbaum, MIT "Reverse Engineering Human Cognitive Development: What do we start with, and how do we learn the rest?“ This live event will take place on: January 27, 2022 16:00 UTC (Coordinated Universal Time) 17:00 CET (Central European Time) 11:00 EST (Eastern Standard Time) 01:00 JST, January 28 (Japan Standard Time) Abstract: What would it take to build a machine that grows into intelligence the way a person does — that starts like a baby, and learns like a child!? AI researchers have long debated the relative value of building systems with strongly pre-specified knowledge representations versus learning representations from scratch, driven by data. However, in cognitive science, it is now widely accepted that the analogous “nature versus nurture?” question is a false choice: explaining the origins of human intelligence will most likely require both powerful learning mechanisms and a powerful foundation of built-in representational structure and inductive biases. I will talk about our efforts to build models of the starting state of the infant mind, as well as the learning algorithms that grow knowledge through early childhood and beyond. These models are expressed as probabilistic programs, defined on top of simulation engines that capture the basic dynamics of objects and agents interacting in space and time. Learning algorithms draw on techniques from program synthesis and probabilistic program induction. I will show how these models are beginning to capture core aspects of human cognition and cognitive development, in terms that can be useful for building more human-like AI. I will also talk about some of the major outstanding challenges facing these and other models of human learning. Bio: Josh Tenenbaum is Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences, the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds and Machines (CBMM). He received his PhD from MIT in 1999, and taught at Stanford from 1999 to 2002. His long-term goal is to reverse-engineer intelligence in the human mind and brain, and use these insights to engineer more human-like machine intelligence. His current research focuses on the development of common sense in children and machines, the neural basis of common sense, and models of learning as Bayesian program synthesis. His research group's papers have been recognized with awards at multiple conferences in Cognitive Science, Computer Vision, AI, Reinforcement Learning and Decision Making, and Robotics. He is the recipient of the Distinguished Scientific Award for Early Career Contributions in Psychology from the American Psychological Association (2008), the Troland Research Award from the National Academy of Sciences (2011), the Howard Crosby Warren Medal from the Society of Experimental Psychologists (2016), the R&D Magazine Innovator of the Year award (2018), and a MacArthur Fellowship (2019). He is a fellow of the Cognitive Science Society, the Society for Experimental Psychologists, and a member of the American Academy of Arts and Sciences. Web: https://web.mit.edu/cocosci/josh.html To attend the talk, please register at: https://sites.google.com/view/developing-minds-series/home Kind regards, Jochen Triesch -- Prof. Dr. Jochen Triesch Johanna Quandt Chair for Theoretical Life Sciences Frankfurt Institute for Advanced Studies and Goethe University Frankfurt http://fias.uni-frankfurt.de/~triesch/ Tel: +49 (0)69 798-47531 Fax: +49 (0)69 798-47611