Microsoft and MIT have partnered on a project to fix so-called virtual'blind spots' which lead driverless cars to make errors. Roads, especially while shared with human drivers, are unpredictable places. Training a self-driving car for every possible situation is a monumental task. The AI developed by Microsoft and MIT compares the action taken by humans in a given scenario to what the driverless car's own AI would do. Where the human decision is more optimal, the vehicle's behaviour is updated for similar future occurrences.

With deepfakes expected to pose a major challenge in the upcoming 2020 election and beyond, Facebook detailed one way in which it plans to take on the problem. As part of a new partnership that involves, among others, Microsoft, MIT and the University of Oxford, Facebook plans to invest more than $10 million to take part in an industry-wide effort to fight deepfakes. The initiative is called the Deepfake Detection Challenge (DFDC). It aims to create open source tools that companies, governments and media organizations can use to better detect when a video has been doctored. Facebook's contribution to the project includes hiring actors to create videos researchers can use to test the detection tools they create.

Self-driving cars are still prone to making mistakes, in part because the AI training can only account for so many situations. Microsoft and MIT might just fill in those gaps in knowledge -- they've developed a model that can catch these virtual "blind spots," as MIT describes them. The approach has the AI compare a human's actions in a given situation to what it would have done, and alters its behavior based on how closely it matches the response. If an autonomous car doesn't know how to pull over when an ambulance is racing down the road, it could learn by watching a flesh-and-bone driver moving to the side of the road. The model would also work with real-time corrections.