The whole issue is whether a hard takeoff is possible and/or plausible, presumably with currently available computing technology. Certainly with Landauer-limit computing technology it would be trivial to simulate billions of human minds in the space and energy usage of a single biological brain. If such technology existed, yes a hard takeoff as measured from biological-human scale would be an inevitability.
But what about today's technology? The largest supercomputers in existence can maaaaybe simulate a single human mind at highly reduced speed and with heavy approximation. A single GPU wouldn't even come close in either storage or processing capacity. The human brain has about 100bn neurons and operates at 100Hz. The NVIDIA Tesla K80 has 8.73TFLOPS single-precision performance with 24GB of memory. That's 1.92bits per neuron and 0.87 floating point operations per neuron-cycle. Sorry, no matter how you slice it, neurons are complex things that interact in complex ways. There is just no possible way to do a full simulation with ~2 bits per neuron and ~1 flop per neuron-cycle. More reasonable assumptions about simulation speed and resource requirements demand supercomputers on the order of approximately the largest we as a species have in order to do real-time whole-brain emulations. And if such a thing did exist, it's not "trivially easy" to expand its own computation power -- it's already running on the fastest stuff in existence!
So with today's technology, any AI takeoff is likely to be a prolonged affair. This is absolutely certain to be the case if whole-brain emulation is used. So should hard-takeoffs be a concern? Not in the next couple of decades at least.
The human brain has about 100bn neurons and operates at 100Hz. The NVIDIA Tesla K80 has 8.73TFLOPS single-precision performance with 24GB of memory. That's 1.92bits per neuron and 0.87 floating point operations per neuron-cycle. Sorry, no matter how you slice it, neurons are complex things that interact in complex ways. There is just no possible way to do a full simulation with ~2 bits per neuron and ~1 flop per neuron-cycle
You are assuming enormously suboptimal/naive simulation. Sure if you use a stupid simulation algorithm, the brain seems powerful.
A...
I've been going through the AIFoom debate, and both sides makes sense to me. I intend to continue, but I'm wondering if there're already insights in LW culture I can get if I just ask for them.
My understanding is as follows:
The difference between a chimp and a human is only 5 million years of evolution. That's not time enough for many changes.
Eliezer takes this as proof that the difference between the two in the brain architecture can't be much. Thus, you can have a chimp-intelligent AI that doesn't do much, and then with some very small changes, suddenly get a human-intelligent AI and FOOM!
Robin takes the 5-million year gap as proof that the significant difference between chimps and humans is only partly in the brain architecture. Evolution simply can't be responsible for most of the relevant difference; the difference must be elsewhere.
So he concludes that when our ancestors got smart enough for language, culture became a thing. Our species stumbled across various little insights into life, and these got passed on. An increasingly massive base of cultural content, made of very many small improvements is largely responsible for the difference between chimps and humans.
Culture assimilated new information into humans much faster than evolution could.
So he concludes that you can get a chimp-level AI, and to get up to human-level will take, not a very few insights, but a very great many, each one slowly improving the computer's intelligence. So no Foom, it'll be a gradual thing.
So I think I've figured out the question. Is there a commonly known answer, or are there insights towards the same?