Meta Goes Unsupervised for Try at Human AI

 Looking to see a good example. 

Meta’s AI Takes an Unsupervised Step Forward In the quest for human-level intelligent AI, Meta is betting on self-supervised learning      By ELIZA STRICKLAND

Meta’s chief AI scientist, Yann LeCun, doesn’t lose sight of his far-off goal, even when talking about concrete steps in the here and now. “We want to build intelligent machines that learn like animals and humans,” LeCun tells IEEE Spectrum in an interview.

Today’s concrete step is a series of papers from Meta, the company formerly known as Facebook, on a type of self-supervised learning (SSL) for AI systems. SSL stands in contrast to supervised learning, in which an AI system learns from a labeled data set (the labels serve as the teacher who provides the correct answers when the AI system checks its work). LeCun has often spoken about his strong belief that SSL is a necessary prerequisite for AI systems that can build “world models” and can therefore begin to gain humanlike faculties such as reason, common sense, and the ability to transfer skills and knowledge from one context to another. The new papers show how a self-supervised system called a masked auto-encoder (MAE) learned to reconstruct images, video, and even audio from very patchy and incomplete data. While MAEs are not a new idea, Meta has extended the work to new domains.

By figuring out how to predict missing data, either in a static image or a video or audio sequence, the MAE system must be constructing a world model, LeCun says. “If it can predict what’s going to happen in a video, it has to understand that the world is three-dimensional, that some objects are inanimate and don’t move by themselves, that other objects are animate and harder to predict, all the way up to predicting complex behavior from animate persons,” he says. And once an AI system has an accurate world model, it can use that model to plan actions.  .... '