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Event Type:  Lecture (This is an NIH Science event)
Title:  AI and Deep Learning
Description:  Deep learning has arisen around 2006 as a renewal of neural networks research allowing such models to have more layers. It is leading the charge of a renewal of AI both inside and outside academia, with billions of dollars being invested and expected fallouts in the trillions by 2030. Theoretical investigations have shown that functions obtained as deep compositions of simpler functions (which includes both deep and recurrent nets) can express highly varying functions (with many ups and downs and different input regions that can be distinguished) much more efficiently (with fewer parameters) than otherwise. Empirical work in a variety of applications has demonstrated that, when well trained, such deep architectures can be highly successful, remarkably breaking through previous state-of-the-art in many areas, including speech recognition, object recognition, playing games, language models, machine translation and transfer learning. In terms of social impact, the most interesting applications probably are in the medical domain, starting with medical image analysis but expanding to many other areas. Finally, we summarize some of the recent work aimed at bridging the remaining gap between deep learning and neuroscience, including approaches to implement functional equivalents to backpropagation in a more biologically plausible way, as well as ongoing work connecting language, cognition, reinforcement learning and the learning of abstract representations.
Series Name:  Machine Learning in Brain Imaging and Neuroscience
Videocast:  Event will not be videocast
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Tuesday, January 30, 2018   2:00pm - 3:00pm Add To Outlook Calendar     Add To iCal Calendar     Add To Entourage Calendar
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Name:   Yoshua Bengio
Title:   Professor and Director
Organization:   Montréal Institute for Learning Algorithms, Université de Montréal
City/Province:   Montréal
Country:   Canada

Organization(s):  [NIH] National Institute of Mental Health (NIMH)

Location:  On the main NIH Campus
Building:  Building 10 (Clinical Center)
Room:  Lipsett Amphitheater
Street Address:  10 Center Drive
City:  Bethesda
State:  Maryland
Zip Code:  20892

Name:   Daniel Handwerker
Phone:   301-402-1359
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