Here’s an interesting look at how AI is revolutionizing astronomy.

The researchers didn’t just flip a switch and have an AI capable of sifting through data to spot planets. They had to train the neural network with data from confirmed exoplanets and false positives so it could identify those telltale signs in new data. The 50 exoplanets confirmed by the University of Warwick run the gamut from Neptune-sized gas giants to rocky worlds smaller than Earth. It’s particularly difficult to confirm smaller planets using the transit method, so that speaks to the accuracy of the AI.

Facebook recently open-sourced Opacus, a library for training PyTorch models with differential privacy that’s ostensibly more scalable than existing methods.

With the release of Opacus, Facebook says it hopes to provide an easier path for engineers to adopt differential privacy in AI and to accelerate in-the-field differential privacy research.

Typically, differential privacy entails injecting a small amount of noise into the raw data before feeding it into a local machine learning model, thus making it difficult for malicious actors to extract the original files from the trained model. An algorithm can be considered differentially private if an observer seeing its output cannot tell if it used a particular individual’s information in the computation.