Face-recognition technology is quickly becoming more common. Should we be concerned?

It’s being used to unlock phones, clear customs, identify immigrants and solve crimes. In the Video Op-Ed above, Clare Garvie demands the United States government hit pause on face recognition. She argues that while this convenient technology may seem benign to those who feel they have nothing to hide, face recognition is something we should all fear. Police databases now feature the faces of nearly half of Americans — most of whom have no idea their image is there. The invasive technology violates citizens’ constitutional rights and is subject to an alarming level of manipulation and bias.

Here’s a talk, that looks at third party tracking on Android.

From the video description:

We’ve captured and decrypted data in transit between our own devices and Facebook servers. It turns out that some apps routinely send Facebook information about your device and usage patterns – the second the app is opened. We’ll walk you through the technical part of our analysis and end with a call to action: We believe that both Facebook and developers can do more to avoid oversharing, profiling and damaging the privacy of their users.

If any company takes the idea that “data is the new oil” to heart, it’s Facebook. Here’s a sobering interview with Yael Eisenstat, a former Facebook employee, by WIRED Magazine about the consequences of it all.

The titans of social media are trapped, and we’re all suffering for it. As free services, Facebook, Twitter, and YouTube monetize you by keeping you engaged, so they can show you more ads. The services are designed to exploit our brain chemistry, flashing us notifications and giving us one more hit of algorithm-recommended video. If they didn’t, their revenue would dwindle and shareholders would be unhappy.

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.