Explore a comprehensive analysis of self-supervised learning in artificial intelligence through this 59-minute video lecture. Delve into the concepts of supervised learning, self-supervised learning, and common sense in AI systems. Examine the process of predicting hidden parts from observed parts and compare self-supervised learning applications in language and vision. Investigate energy-based models, joint-embedding models, and contrastive methods. Learn about latent-variable predictive models and GANs. Gain insights from Yann LeCun and Ishan Misra's research at Facebook AI, discussing the potential of self-supervised learning as a key approach to developing AI systems with improved background knowledge and common sense capabilities.
Self-Supervised Learning - The Dark Matter of Intelligence
Overview
Syllabus
- Intro & Overview
- Supervised Learning, Self-Supervised Learning, and Common Sense
- Predicting Hidden Parts from Observed Parts
- Self-Supervised Learning for Language vs Vision
- Energy-Based Models
- Joint-Embedding Models
- Contrastive Methods
- Latent-Variable Predictive Models and GANs
- Summary & Conclusion
Taught by
Yannic Kilcher
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