Comment by mdp2021

> In particular, AMI is building world models that leverage JEPA (Joint Embedding Predictive Architecture), a neural network architecture that LeCun pioneered and that teaches models to predict data in a representational space within a neural network’s middle layers, rather than generating raw pixels, as many competing world models do, or words, as LLMs do. // The company’s primary focus, for now, is industrial applications. “It’s AI for the physical world, so it’s not language-related,” LeCun said. “It’s systems that understand the real world, like a manufacturing plant or turbojet engine.” Some of the main applications are anomaly detection or robotics: “If you have a machine and all of a sudden it makes a strange noise and starts breaking, you would’ve wanted to detect that as early as possible.” He also gave the example of a system that might understand the world like a cat does, for example, which knows that if it pushes a vase off the counter it will fall.

What about the management of concepts? The world is not just made of physical entities to be inserted in a model. What about their translation into words (to e.g. express assessments)?