7th February, 2014 / 11.00am - 4.00pm
2nd March, 2018
Research Centre for Machine Learning
In the first half of this talk, I’ll introduce and describe Private Deep Learning, which is an approach to training neural networks in an encrypted state such that it’s growing intelligence (and the underlying data) is protected from theft. This will include a description of Federated Learning and Multi-Party Computation.
In the second half of this talk, I’ll be discussing the significant impacts that Private Deep Learning has when combined with recent advancements in Blockchain and Peer-to-Peer technology into a new open-source platform called OpenMined. This will include a live demo showing how to train a neural network on a large, distributed grid of machines all around the world.
Andrew Trask is a PhD Student at the University of Oxford studying Deep Learning. He is also the author of Grokking Deep Learning, a Manning Publications introductory book which has sold over 6000 copies, an instructor in Udacity’s Deep Learning Nanodegree, and the author of a popular machine learning blog http://iamtrask.github.io . Previously, Andrew was a researcher and analytics product manager at Digital Reasoning where he trained the world’s largest artificial neural network with over 160 billion parameters and helped guide the analytics roadmap for the Synthesys AI platform deployed to many Enterprises such as Goldman Sachs, UBS, HCA (the largest hospital network in North America), various members of the Intellignece Community, and the US Military. Andrew lives on a boat in Oxford with his wife Amber and plays piano in his spare time.