Law enforcement agencies like the New Orleans Police Department are adopting AI based systems to analyze surveillance footage. WSJ’s Jason Bellini gets a demonstration of the tracking technology and hears why some think it’s a game changer, while for others it’s raising concerns around privacy and potential bias.

Lex Fridman interviews Rosalind Picard, a professor at MIT, director of the Affective Computing Research Group at the MIT Media Lab, and co-founder of two companies, Affectiva and Empatica. Over two decades ago she launched the field of affective computing with her book of the same name. This book described the importance of emotion in artificial and natural intelligence, the vital role emotion communication has to relationships between people in general and in human-robot interaction.

Federated Learning (FL) is a distributed approach to machine learning that enables training on a large corpus of decentralized data residing on devices like mobile phones.  FL employs the approach of “bringing the code to the data, instead of the data to the code.” Additionally, it addresses the fundamental problems of privacy, ownership, and locality of data.

Here’s a more in depth look at the approach.

There’s a good high-level overview of federated learning on Google’s AI blog. Devices download the current model, improve it by learning using data local to the phone, and then send a small focused model update back to the cloud, where it is averaged with other user updates to improve the shared model. No individual updates are stored in the cloud, and no training data leaves the device.

Yesterday’s post about GDPR and Blockchains had me wondering about potential fixes for this issue.

This video on Channel9 provides an overview of some of the more popular privacy features employed on private consortiums to enable sharing data only with specific participants in a network.  

It turns out that there are a variety of ways and the architecture of these are discussed with a brief demo using the Quorum blockchain.