"Does it mean that I wouldn't know what they are as well?" Are they explained in terms of things that you have seen in real life? Or in ways that build on tha

"Does it mean that I wouldn't know what they are as well?" Are they explained in terms of things that you have seen in real life? Or in ways that build on that? Then you can certainly gain that by building on those models. And yes, if you can feed an attention-based neural network (not an LLM, those are specifically Language-based) sense data (and preferably action data too, for how it interacts with the world) then that would give it a picture of the world. And once it can recognise a dog then you can say "And we call this a dog!". There's research in to this already - see this piece https://www.economist.com/science-and-technology/2024/06/05/robots-are-suddenly-getting-cleverer-whats-changed https://www.economist.com/science-and-technology/2024/06/05/... (you may need to use archive.is to read the archive of that page). ==== These “vision-language-action models” (VLAMs) take in text and images, plus data relating to the robot’s presence in the physical world, including the readings on internal sensors, the degree of rotation of different joints and the positions of actuators (such as grippers, or the fingers of a robot’s hands). The resulting models can then answer questions about a scene, such as “can you see an apple?” But they can also predict how a robot arm needs to move to pick that apple up, as well as how this will affect what the world looks like." Grounding the model’s perception in the real world in this way greatly reduces hallucinations (the tendency for AI models to make things up and get things wrong). ====