Paul J. Werbos is a Fellow of the IEEE and one of the first recipients of the IEEE Neural Network Pioneer Award, in recognition of the original development of backpropagation and of adaptive dynamic programming in the 1960’s and 1970s. These are the foundations which made deep learning possible, including reinforcement learning systems more powerful than those used in Alpha-Go.

From 1988 to 2015, he ran the leading research in neural networks at NSF, and organized the crosscutting research initiative (see “COPN” at www.nsf.gov) which funded the award to Ng and LeCun, which led to the revolution in deep learning and “the new AI” starting in 2009. (See www.werbos.com/Mind.htm for links to statements by Sergey Brin and others on that revolution.)

The International Neural Network Society granted him its highest award, the Hebb award, recognizing his work showing how these mathematical tools can explain key aspects of learning in biological brains. He also serves on the Planning Committee of the Millennium Project, and has been active in IEEE-USA; for example, for IEEE-USA he gave a major talk in Rayburn to over 200 Congressional staffers, which helped prepare the State of the Union message which led to the Energy Information and Security Act of 2007. In 2009, he served as a Brookings Fellow in the office of Senator Specter, responsible for climate, energy, space policy and cybersecurity.