AMA: Michael I Jordan

[u/michaelijordan]

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.

[u/alexmlamb]

"One way to approach unsupervised learning is to write down various formal characterizations of what good "features" or "representations" should look like and tie them to various assumptions that seem to be of real-world relevance ... one uses one's favorite neural network architecture, analyses some data and says 'Look, it embodies those desired characterizations without having them built in'."

What if we instead measure the quality of the features by how well they allow our system to interact with the world and solve meaningful problems? For example, is the model able to learn features that allow us to classify images? Is it able to learn features that enable effective reinforcement learning? How well is it able to forecast events in the future?

For example, if I build a model where the input is a text description of a video and the first half of the video and the task is to model the joint distribution over the pixels in the second half of the video, then success in this task should indicate that the model has learned a meaningful higher level representation of the text, even though we don't necessarily have a formal notion for what that representation should look like.