Is Deep Learning the New 42?
panelist: Pedro Domingos, Dept. of Computer Science & Engineering, University of Washington
panelist: Nando de Freitas, Department of Computer Science, University of Oxford
panelist: Isabelle Guyon, Clopinet
panelist: Jitendra Malik, UC Berkeley
panelist: Jennifer Neville, Computer Science Department, Purdue University
published: Aug. 31, 2016, recorded: August 2016, views: 10125
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The history of deep learning goes back more than five decades but in the marketplace of ideas its perceived value went through booms and busts. We are no doubt at an all time high: in the last couple of years we witnessed extraordinary advances in vision, speech recognition, game playing, translation, and so on, all powered by deep networks. At the same time companies such as Amazon, Apple, Facebook, Google, and Microsoft are making huge bets on deep learning research and infrastructure, ML competitions are dominated by deep learning approaches, open source deep learning software is proliferating, and the popular press both cheerleads the progress and raises the dark specter of unintended consequences.
So is deep learning the answer to everything?
According to Douglas Adams’s famous “Hitchhiker’s Guide to the Galaxy” after 7.5 millions years of work the “Deep Thought” computer categorically found out that 42 is the “Answer to the Ultimate Question of Life, the Universe, and Everything” (although unfortunately, no one knows exactly what that question was).
Rather than wait another 7.5 million years for “Deep Thought” to answer our quest we have assembled a distinguished panel of experts to give us their opinion on deep learning and its present and future impact.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !