Yann LeCun: "A Path Towards Autonomous AI", Baidu 2022-02-22
Channel: Yann LeCun
Date: 2022-02-25

Technical talk by Yann LeCun:
"A Path Towards Autonomous AI"
Hosted virtually by Baidu on 2022-02-22.

Video of Q&A sessions here: https://youtu.be/Qgh2IU_fRMs
 
TL;DR: 
- autonomous AI requires predictive world models
- world models must be able to perform multimodal predictions
- solution: Joint Embedding Predictive Architecture (JEPA)
- JEPA makes prediction in representation space, and can choose to ignore irrelevant or hard-to-predict details.
- JEPA can be trained non-contrastively by (1) making the representations of input maximally informative, (2) making the representations predictable from each other, (3) regularizing latent variables necessary for prediction.
- JEPAs can be stacked to make long-term/long-range predictions in more abstract representation spaces.
- Hierarchical JEPAs can be used for hierarchical planning.

Explanatory blog post: https://ai.facebook.com/blog/yann-lecun-advances-in-ai-research/

Topics: 
- How to get machines to learn like humans and animals?
- Challenges in AI: self-supervised learning, reasoning, hierarchical planning
- Learning models of the world
- architecture for autonomous AI: world model, cost, actor, perception, configurator, short-term memory.
- perception-action cycle: Mode-1 (reactive) and Mode-2 (planning)
- Intrinsic Cost and Trainable Cost modules
- building and training a world model
- self-supervised learning (SSL)
- Energy-Based Models: 
- contrastive and regularized training methods
- EBM architectures: Joint Embedding Predictive Architecture (JEPA)
- contrastive methods for training JEPA (bad)
- regularized (non-contrastive) methods for training JEPA (good)
- VICReg: Variance Invariance Covariance Regularization
- hierarchical JEPA for world models.
- hierarchical planning under uncertainty with hierarchical JEPA