Plenary lecture of ICONIP 2017
Beyond Deep Learning and Brain Research
Paul J. Werbos (Retired from the National
Science Foundation, USA)
Science Foundation, USA)
For many decades, mainstream AI refused to believe that deep learning with neural networks and backpropagation offer true brain-like general intelligence, despite numerous successes on tough engineering problems and mathematical advances. The tide changed in 2009 due to a $2 million grant I gave to Ng and LeCun, despite fierce objections which in today's government environment would have prevented the action. Empirical success on well-known challenge problems led to follow-ons by DARPA, then by Google, and then a flood of interest by competitors trying to keep up. This past year, new analysis of the best time-series data available for the brain shows that it fits the core principles of backpropagation and deep learning much better than it fits the Hebbian and spiking types of model inherited from the previous century. As the flood of deep learning, internet of things, RNN, BCI, CNN and security technology grows, it may grow out of control. (Click for more details). It is urgent that we move quickly to develop and implement additional new technologies and paradigms, lest the current imbalances and instabilities engulf us all.
- Paper Submission Deadline
June 15, 2017
June 30, 2017 - Notification of Acceptance
July 31, 2017 - Final Paper Submission
August 20, 2017 - Congress Date
November 14–18, 2017
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