[YZF]

Right. Humans are a biological computer. They have a state and they compute an output. I had to look this up (and use AI) but an estimate for the state of a human mind is about 5 peta-bits (10^15) and the estimated processing power is about 1 exa-FLOP (10^18). Compare this to the largest models at ~5 tera-bits (10^12) of state space and ~2 x 10^14 FLOPS (for one session with some reasonable token rate).

Assuming the above is anywhere near true (I think there's a lot of debate about the capacity of the human mind, where data is actually stored, and where compute happens) then we are talking about 3 orders of magnitude win for humans in state and 4 orders of magnitude in compute. And we're doing all that pretty energy efficient as well.

The other big difference in humans is that we learn and the model only "learns" in context. Out "learn" space is much larger than the 1M tokens that frontier models struggle with.

Anyways, point is that a computer can appear to be alive. If we simulate the human brain perfectly and train it like a human then we'll have something that has human capabilities. LLMs have interesting capabilities but at least at this point not fully human ones (and the delta-state/compute would be a hint that there is still a large gap to cover).

[Nathanba]

human context/memory could just be an Agents.md file too that gets read instantly before your next token prediction runs. The AI can make multiple such memory files and read on demand depending on what the topic is, kind of like how as a human when you try to remember a math problem you don't go to your childhood bicycling Agents.md file either.