Reinforcement learning to quadrotor control
Published on Mar 3, 2017
On Deep Learning with Ian Goodfellow, Andrew Trask, Kelvin Lwin, Siraj Raval and the Udacity Team
Streamed live on Mar 17, 2017
Join us on March 17 at 6pm PST for a panel on the state of deep learning. Brought to you by Udacity's Deep Learning Nanodegree Foundation program.
Forget catastrophic forgetting: AI that learns after deployment
Published on May 16, 2017
Neurala CTO Anatoly Gorshechnikov on Lifelong Deep Learning Neural Networks. One of the major hassles of Deep Learning is the need to fully retrain the network on server every time new data becomes available in order to preserve the previous knowledge. This is called 'catastrophic forgetting' and severely impairs the ability to develop a truly autonomous AI. We present the patent pending technology that allows us to solve this problem by simply training on the fly the new object without retraining of the old. Our results not only show state of the art accuracy, but real time performance suitable for deployment of AI directly on the edge, thus moving AI out of the server room and into the hands of consumers. Imagine a toy that can learn to recognize and react to its owner or a drone that can learn and detect objects of interest identified while in flight. (Recorded at the NVIDIA GTC Conference in 2017 at San Jose.)
The Deep End of Deep Learning | Hugo Larochelle | TEDxBoston
Published on Oct 12, 2016
Artificial Neural Networks are inspired by some of the "computations" that occur in human brains—real neural networks. In the past 10 years, much progress has been made with Artificial Neural Networks and Deep Learning due to accelerated computer power (GPUs), Open Source coding libraries that are being leveraged, and in-the-moment debates and corroborations via social media. Hugo Larochelle shares his observations of what’s been made possible with the underpinnings of Deep Learning.
Hugo Larochelle is a Research Scientist at Twitter and an Assistant Professor at the Université de Sherbrooke (UdeS). Before 2011, he spent two years in the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. He obtained his Ph.D. at Université de Montréal, under the supervision of Yoshua Bengio. He is the recipient of two Google Faculty Awards. His professional involvement includes associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), member of the editorial board of the Journal of Artificial Intelligence Research (JAIR) and program chair for the International Conference on Learning Representations (ICLR) of 2015, 2016 and 2017.
The progress we've made in machine learning - Tom Dietterich
Published on Oct 31, 2017
The National Academies of Sciences, Engineering, and Medicine organized a two-day workshop on the capabilities and applications of artificial intelligence and machine learning for the intelligence community on August 9-10, 2017.
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