Yann LeCun and Yoshua Bengio: Self-supervised learning is the key to human-level intelligence

Self-supervised learning could lead to the creation of AI that's more human-like in its reasoning, according to Turing Award winners Yoshua Bengio and Yann LeCun. Bengio, director at the Montreal Institute for Learning Algorithms, and LeCun, Facebook VP and chief AI scientist, spoke candidly about this and other research trends during a session at the International Conference on Learning Representation (ICLR) 2020, which took place online. Supervised learning entails training an AI model on a labeled data set, and LeCun thinks it'll play a diminishing role as self-supervised learning comes into wider use. Instead of relying on annotations, self-supervised learning algorithms generate labels from data by exposing relationships among the data's parts, a step believed to be critical to achieving human-level intelligence. "Most of what we learn as humans and most of what animals learn is in a self-supervised mode, not a reinforcement mode. It's basically observing the world and interacting with it a little bit, mostly by observation in a test-independent way," said LeCun.