Lex Fridman Podcast #206 – Ishan Misra: Self-Supervised Deep Learning in Computer Vision
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Jul 31, 2021 Ishan Misra, a research scientist at FAIR, specializes in self-supervised visual learning. He explores the mechanics of computer vision and how AI can learn from data with minimal human input. The conversation dives into the limitations of traditional supervised learning and emphasizes the innovative potential of self-supervised methods. Misra also discusses the relationship between neural networks and human cognition, the significance of data augmentation, and the philosophical implications of AI consciousness, making for an enlightening dialogue on the future of machine learning.
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Learning Paradigms
- Supervised learning involves humans labeling data for AI systems, teaching them to mimic input-output pairs.
- Semi-supervised learning uses both labeled and unlabeled data, while self-supervised learning leverages the data itself as supervision.
Self-Supervision Signals
- Self-supervised learning uses data as its own supervision, extracting signals from the data itself.
- Examples include predicting masked words in sentences or forecasting the next frame in a video.
Self-Supervised Learning & Common Sense
- Self-supervised learning is crucial for AI to develop common sense and learn things hard to label.
- It enables agents to infer information from observations without explicit human instruction.