The Science of Deep Learning

The Annual Arthur M. Sackler Lecture Steps Toward Super Intelligence will be presented by Rodney Brooks, Massachusetts Institute of Technology at 6p.m. on Wednesday, March 13th.  

The 2019 Annual Arthur M. Sackler Lecture is free and open to the public, but advance registration is required, and attendees must present government-issued photo identification for admittance to the NAS Building.  First and Last Name of each attendee must be entered to be on the guest list.  

The lecture is presented in conjunction with the Sackler Colloquium The Science of Deep Learning being held March 13-14, 2017 in Washington D.C. 

Colloquium registration includes this event.  To register for the 2-day scientific meeting, use the purple Register Button. 

To rsvp for the public lecture only - use this link

 

Steps Toward Super Intelligence

Progress in Machine Learning over the last decade has led to lots and lots of applications, and to an understanding in business that getting control of large amounts of data is an important tactic. We will continue to see new ways to use large data sets and new application areas. But we should not confuse this burst of progress with being close to being able to build general human-level, or beyond, Artificial Intelligence systems. There are so many other aspects of intelligence that we still have no viable ways towards emulating. There is lots of research yet to be done. This talk outlines how we got to where we are, why we may be mistaken on how far we have come, and highlights challenges in getting toward super intelligent machines. That prospect may yet be centuries away.

Rodney Brooks

Rodney Brooks is the Panasonic Professor of Robotics (emeritus) at MIT, where he was for ten years the director of the Artificial Intelligence Lab and then the Computer Science and Artificial Intelligence Lab (CSAIL) until 1997. He received a PhD in computer science from Stanford in 1981, was a post doc at CMU and MIT, and a faculty member at Stanford, before joining the MIT faculty in 1984. His research areas have been in machine learning, computer vision, robotics, AI, and Artificial Life. He was co-founder, CTO and Chairman of iRobot. He is a member of the National Academy of Engineering.

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