Lecture by Andrew Trask in January 2020, part of the MIT Deep Learning Lecture Series.

Website: https://deeplearning.mit.edu
Slides: http://bit.ly/38jzide
Playlist: http://bit.ly/deep-learning-playlist

LINKS:
Andrew Twitter: https://twitter.com/iamtrask
OpenMined: https://www.openmined.org/
Grokking Deep Learning (book): http://bit.ly/2RsxlUZ

OUTLINE:
0:00 - Introduction
0:54 - Privacy preserving AI talk overview
1:28 - Key question: Is it possible to answer questions using data we cannot see?
5:56 - Tool 1: remote execution
8:44 - Tool 2: search and example data
11:35 - Tool 3: differential privacy
28:09 - Tool 4: secure multi-party computation
36:37 - Federated learning
39:55 - AI, privacy, and society
46:23 - Open data for science
50:35 - Single-use accountability
54:29 - End-to-end encrypted services
59:51 - Q&A: privacy of the diagnosis
1:02:49 - Q&A: removing bias from data when data is encrypted
1:03:40 - Q&A: regulation of privacy
1:04:27 - Q&A: OpenMined
1:06:16 - Q&A: encryption and nonlinear functions
1:07:53 - Q&A: path to adoption of privacy-preserving technology
1:11:44 - Q&A: recommendation systems

CONNECT:
- If you enjoyed this video, please subscribe to this channel.
- Twitter: https://twitter.com/lexfridman
- LinkedIn: https://www.linkedin.com/in/lexfridman
- Facebook: https://www.facebook.com/lexfridman
- Instagram: https://www.instagram.com/lexfridman