[VL-JEPA, EBM] Decoding the World Model: Energy, Entropy, and the JEPA. Boltzmann Distributions. скачать в хорошем качестве

[VL-JEPA, EBM] Decoding the World Model: Energy, Entropy, and the JEPA. Boltzmann Distributions.

The era of "reconstruction" is ending. For years, we’ve trained AI to obsess over every pixel, every character, and every frame—trying to recreate the world in order to understand it. But as we move toward true autonomous machine intelligence, the industry is making a pivot. Energy Based Models - EBM. We’re moving away from pixels and toward Energy. Welcome to The Latent Space. Today’s episode is a deep dive into the mathematical backbone of the next generation of AI: Unified Energy Landscapes. We are tracing the lineage from the 19th-century physics of the Boltzmann Distribution to Meta’s cutting-edge VL-JEPA—the Vision-Language Joint-Embedding Predictive Architecture. Think about it: Why try to predict the exact color of every leaf on a tree when you can predict the concept of the wind? This is the core of the Energy-Based Model (EBM) philosophy. By using the Boltzmann distribution as our bridge, we can translate raw, scalar "compatibility scores" into sophisticated probability densities. It’s the difference between a model that just mimics what it sees and a World Model that understands how the world fits together. In this session, we’re breaking down: The Boltzmann Foundation: How a simple formula for thermal equilibrium became the secret sauce for modern latent spaces. The Death of Generative Overkill: Why architectures like VL-JEPA are ditching "infilling" for "predicting" in semantically dense spaces. The Synthesis: How aligning linguistic concepts with visual perceptions isn’t about matching data—it’s about minimizing energy. If you want to understand how AI is finally starting to "see" the world like a human—prioritizing the signal and ignoring the noise—this is the episode you’ve been waiting for. Let’s decode the world model.