Filling the Gaps
Self-supervised learning emerges as a promising approach to developing intelligence, where machines predict future events based on video segments. By filling in the gaps—whether in language or visual information—machines can learn to model the world around them. While this method has proven effective in natural language processing, challenges remain in applying it to video learning, highlighting the complexity of visual representation.In this clip
From this podcast
Lex Fridman Podcast
Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning | Lex Fridman Podcast #258
Related Questions