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Yann LeCun, Yoshua Bengio: Self-Supervised Learning is Key to Human-Level Intelligence


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Yann LeCun (left) and Yoshua Bengio.

Yann LeCun and Yoshua Bengio, recipients of the ACM A.M. Turing Award, say self-supervised learning could lead to the creation of artificial intelligence programs that are more humanlike in their reasoning.

Credit: Medium.com

ACM A.M. Turing Award recipients Yann LeCun and Yoshua Bengio say that self-supervised learning could lead to the creation of artificial intelligence (AI) programs that are more humanlike in their reasoning.

Speaking at the International Conference on Learning Representation (ICLR) 2020, which took place online, LeCun, Facebook's chief AI scientist, said supervised learning systems will play a diminishing role as self-supervised learning algorithms—those that generate labels from data by exposing relationships between the data's parts, believed to be critical to achieving human-level intelligence—comes into wider use.

Meanwhile Bengio, director at the Montreal Institute for Learning Algorithms, predicts new studies will reveal the way high-level semantic variables connect with how the brain processes information, including visual information. Humans communicate these kinds of variables using language, and they could lead to a new generation of deep learning models.

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