I completely agree but I do think humans have a language model, and considering how we use that to encode and decode the human experience might be useful in figuring out how we improve things like GPT-3.
Personally I feel that embodiment of some form, in which there is some vector space for a 'world model' that can be paired up to a language model, is a route forward. For example, if you have a Boston Dynamics (for example) robot that has a model for gravity, mass, acceleration, force, object manipulation, etc and you incorporate those into a language model, there is going to be a much richer latent space from which associations can be made between terms. If you ask GPT-3 the difference between various gaits, e.g. walk, trot, gallop, it's going to have associations with other contexts and adjectives used in the vicinity of those terms. However, if you enrich it with data from a Spot Mini that can actually execute those gaits, you're going to have information around velocity, inertia, power consumption and budget, object detection rates, route planning horizon, etc.
Personally I feel that embodiment of some form, in which there is some vector space for a 'world model' that can be paired up to a language model, is a route forward. For example, if you have a Boston Dynamics (for example) robot that has a model for gravity, mass, acceleration, force, object manipulation, etc and you incorporate those into a language model, there is going to be a much richer latent space from which associations can be made between terms. If you ask GPT-3 the difference between various gaits, e.g. walk, trot, gallop, it's going to have associations with other contexts and adjectives used in the vicinity of those terms. However, if you enrich it with data from a Spot Mini that can actually execute those gaits, you're going to have information around velocity, inertia, power consumption and budget, object detection rates, route planning horizon, etc.