Facebook debuted Learning from Videos, a project designed to learn audio, images and text from publicly available Facebook videos to improve its core AI systems. By culling data from hundreds of languages and countries, said Facebook, the project will also help to enable “entirely new experiences.” Learning from Videos, which began in 2020, has also helped to improve recommendations in Instagram Reels. Facebook, Google and others are focused on self-supervised techniques rather than labeled datasets to improve AI. Continue reading Facebook Using Self-Supervised Models to Build AI Systems

Facebook has been under fire for abuse on its platform, although chief executive Mark Zuckerberg often said that its AI tools have been successful at diminishing such problems. It turns out that he’s right: Facebook’s recent Community Standards Enforcement Report revealed that it removed 32+ billion fake accounts between April and September, compared to “just over 1.5 billion” during the same period last year. Largely responsible for the improvement is deep entity classification (DEC), a machine learning framework. Continue reading Facebook’s AI Technique Deletes 32 Billion Fake Accounts

Humans learn from experience to not “do dumb things,” and Facebook chief AI scientist Yann LeCun is trying to create a version of that for robots, saying that systems that learn “models of the world” are our best shot at advancing artificial intelligence. Unlike a rewards/demerits-based reinforcement learning, Facebook’s tack is to instill curiosity, by giving the robot freedom to try new things. With New York University, Facebook also dramatically reduced the number of tries to teach a robotic arm to grasp an object. Continue reading Facebook Turns to Robots to Advance Artificial Intelligence