AI Bibliography
AI Bibliography |
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Dawid, A., & Lecun, Y. (2023). Introduction to latent variable energy-based models: A path towards autonomous machine intelligence. arXiv preprint arXiv:2306.02572. |
| Resource type: Journal Article BibTeX citation key: Dawid2023 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Decision Theory, General Subcategories: Autonomous systems, Decision making, Deep learning, Human learning, Machine learning Creators: Dawid, Lecun Publisher: Collection: arXiv preprint arXiv:2306.02572 |
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Current automated systems have crucial limitations that need to be addressed before artificial intelligence can reach human-like levels and bring new technological revolutions. Among others, our societies still lack Level 5 self-driving cars, domestic robots, and virtual assistants that learn reliable world models, reason, and plan complex action sequences. In these notes, we summarize the main ideas behind the architecture of autonomous intelligence of the future proposed by Yann LeCun. In particular, we introduce energy-based and latent variable models and combine their advantages in the building block of LeCun's proposal, that is, in the hierarchical joint embedding predictive architecture (H-JEPA).
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