No, it's about imitation, not simulation. The point is defining how large of a computer you would need to achieve similar performance to the human brain on "intelligence" tasks. The comparison to the human brain is because we know human brains can do these kinds of reasoning and motor tasks, so that helps us set a lower bound on how much computing power is necessary, but it doesn't presume we're going to simulate a human brain, that's just stated because it might be one way we could do it.

But still I think you're not engaging with the article properly - it doesn't say we will, it just talks about how much computing power you might need. And I think within the paper it suggests we don't have enough computing power yet, but it doesn't seem like you read deeply enough to engage with that conversation.

You're right to distinguish imitation from simulation. That's a good distinction and I think the paper is discussing imitation--using similar learning algorithms to what the brain uses, fed with realistic data from input devices. But my point still stands with imitation.

> This paper outlines the case for believing that we will have superhuman artificial intelligence within the first third of the next century. It looks at different estimates of the processing power of the human brain; how long it will take until computer hardware achieve a similar performance; ways of creating the software through bottom-up approaches like the one used by biological brains; how difficult it will be for neuroscience figure out enough about how brains work to make this approach work; and how fast we can expect superintelligence to be developed once there is human-level artificial intelligence.

The paper very clearly suggests an estimate of the required hardware power for a particular strategy of imitating the brain. And it very clearly predicts we will achieve superintelligence by 2033.

If that strategy is a non-starter, which it is for the foreseeable future, then the hardware estimate is irrelevant, because the strategies we have available to us may require orders of magnitude more computing power (or even may simply fail to work with any amount of computing power).