Lex Fridman interviews Michael Kearns in the latest episode of his podcast.

Michael Kearns is a professor at University of Pennsylvania and a co-author of the new book Ethical Algorithm that is the focus of much of our conversation, including algorithmic fairness, privacy, and ethics in general. But, that is just one of many fields that Michael is a world-class researcher in, some of which we touch on quickly including learning theory or theoretical foundations of machine learning, game theory, algorithmic trading, quantitative finance, computational social science, and more. This conversation is part of the Artificial Intelligence podcast.

CNBC takes a closer look at what’s going on with its cryptocurrency project, Libra

When Facebook first announced it was getting into the crypto business—with a basically unregulated currency called Libra—the reaction from Wall Street and government bankers was about as expected. Fast-foward a few months, and Libra is in trouble. The social media giant had lined up a long list of corporate backers for the initiative, including major players in the payments space. And in October 2019, several prominent backers began to back out. Here’s how Facebook’s crypto future got into serious trouble.

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.

PyTorch keeps growing and growing in acceptance. Here’s an interesting development from Facebook.

Reproducibility puts the science in the computer science of AI. It’s how researchers can prove their AI systems are robust and reliable. To support reproducibility for AI models, Facebook today released PyTorch Hub in beta, an API and workflow for research reproducibility and support. PyTorch Hub can quickly publish […]

Over the last decade or so, open source has blossomed into a major movement and the backbone of the tech industry. For instance, check out this project that Uber, yes Uber, has open sourced.

Ludwig is a TensorFlow-based toolbox that allows you to train and test deep learning models without the need to write any of the code. Incubated at Uber for the last two years, Ludwig was finally open sourced this February to incorporate the contributions of the data science community. With Ludwig, a data scientist can train a deep learning model by simply providing a CSV file that contains the training data as well as the YAML file with the outputs and inputs of the model.