Andrew Ng : 2011 Plenary Session


Tuesday, April 12, 2011
Location: Fisher Conference Center, Arrillaga Alumni Center

"Machine Learning and AI via Brain Simulations"
11:15am - 11:45am


By building large-scale simulations of cortical (brain) computations, can we enable revolutionary progress in AI and machine learning?

Machine learning often works very well, but can be a lot of work to apply because it requires spending a long time engineering the input representation (or "features") for each specific problem. This is true for machine learning applications throughout Silicon Valley, including ones in video, images, audio, and text.

To address this, recently researchers have developed "deep learning" algorithms that can learn good representations automatically, thus bypassing most of this time-consuming engineering. Most of these algorithms are based on simple simulations of neuronal computations in the cortex (brain), and build on such ideas as sparse coding and deep belief nets. These algorithms have significantly surpassed the previous state-of-the-art on a number of problems in vision, audio, and text. In this talk, I will describe the key ideas behind these algorithms, and they are being applied for current applications. I will also discuss how they might enable significant progress in AI, especially in problems in computer perception (such as vision/audio/text).

This talk will be broadly accessible, and will not assume a machine learning background.


Andrew Ng is an Associate Professor of Computer Science, and works on machine learning and AI. His previous work includes autonomous helicopters that can fly stunt maneuvers, the STanford AI Robot (STAIR) project, and ROS (probably the most widely used open-source robotics software platform today). His current work focuses on neuroscience-informed deep learning and unsupervised feature learning algorithms. His group has won best paper/best student paper awards at ICML, ACL, CEAS, 3DRR. He is also a recipient of the Alfred P. Sloan Fellowship, and the 2009 IJCAI Computers and Thought award.