It is ultimately a hardware problem. To simplify it greatly, an LLM neuron is a single input single output function. A human brain neuron takes in thousands of inputs and produces thousands of outputs, to the point that some inputs start being processed before they even get inside the cell by structures on the outside of it. An LLM neuron is an approximation of this. We cannot manufacture a human level neuron to be small and fast and energy efficient enough with our manufacturing capabilities today. A human brain has something like 80 or 90 billion of them and there are other types of cells that outnumber neurons by I think two orders of magnitude. The entire architecture is massively parallel and has a complex feedback network instead of the LLM’s rigid mostly forward processing. When I say massively parallel I don’t mean a billion tensor units. I mean a quintillion input superpositions.
And the final kicker: the human brain runs on like two dozen Watts. An LLM takes a year of running on a few MW to train and several KW to run.
Given this I am not certain we will get to AGI by simulating it in a GPU or TPU. We would need a new hardware paradigm.
And the final kicker: the human brain runs on like two dozen Watts. An LLM takes a year of running on a few MW to train and several KW to run.
Given this I am not certain we will get to AGI by simulating it in a GPU or TPU. We would need a new hardware paradigm.