Last Week in AI

Every week, my team at Invector Labs publishes a newsletter to track the most recent developments in AI research and technology. You can find this week’s issue below. You can sign up for it below. Please do so, our guys worked really hard on this:

From the Editor: Data Privacy and AI

Data privacy is one of the biggest challenges of modern machine learning applications. In order to build machine learning models, researchers need to have complete access to datasets that often contain sensitive data. The idea of models that work effectively with encrypted datasets has long been an elusive goal of the machine learning space. While research in areas such as homomorphic encryption or secure, multi-party computation has been rapidly advancing, its adoption in machine learning stacks remains limited at best.

Incorporating data privacy techniques in mainstream deep learning frameworks is essential to unlock many scenarios in regulated industries or even mobile applications. In the past, we have seen companies such as DeepMind pioneer some efforts to build data privacy frameworks for deep learning solutions. This week, Facebook took an important step open sourcing a new project that brings data privacy capabilities to PyTorch. Certainly, in the next few years data privacy frameworks should become a common component of deep learning stacks.