LLMs solve for the next word. Human intelligence solves for survival with many types of input, visual, audio etc. You can't create an AGI if you don't solve for the problems that created human GI.
Digital data is all 1s and 0s, whether it encodes words, sounds, or pictures. Why do you think transformers only work for predicting words, when they're already successfully being used for other applications as well?
I think much like with a basic Turing machine definition compute is possible on a variety of substrates that some kind of intelligence can be created with a whole class of implementations, transformers included. Indeed the video and image input of LLMs is one of the most exiting use cases.
They’re trained to optimize guessing the next word. What they solve for to get this good at predicting the next word is an open question with answers hidden in plain sight in the weight blob.
Why not? For a hypothetical example - if we assume that simulating a human is AGI, and we have some hypothetical space-age magic tech bruteforce the problem by simulating every neuron and connection in the brain... why would being "embodied" factor into this?
Because I think intelligence formed in human beings is connected directly to embodiment and not some kind of abstraction that can be simulated. My guess is that the best AI developments will ultimately come from mimicking the processes of how humans learn from their environment, and not from merely simulating (or trying to simulate, as I don’t really buy the positivist approach) human brains.
Absolutely. The physical world is the input that creates the feedback loop for learning.
I would propose a definition of AGI. "A model capable of effecting the physical world through speech or physical action in a manner indistinguishable from a human."
