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>The human brain is a great example – its processing power is estimated at about 38 petaflops, about two-fifths of that of TaihuLight.

Huh? So we now have computers more powerful than the human brain? I thought that was still some decades off. And how would one even measure such a thing? In the apples-to-apples comparison, a stupid human trick floating-point calculation savant might manage 1 flop/s.



In speed yes, in terms of continuous parallel computing power at low energy levels, technology hasn't come even close. In terms of sensory input and processing, not even close.

I find it amusing that there is much hype about computer systems beating humans in very specialised areas, such as go and chess.

But the missing piece here is that the human is still doing this while continuously processing all the sensory input that is occurring to that human, dealing with so much more than what the computer system is dealing with. The computer system is dealing with one and only one subject matter at a speed many magnitudes faster and is only just getting ahead.


> one and only one subject matter at a speed many magnitudes faster and is only just getting ahead.

+1

Kasparov didn't simulate 200 million moves per second to make his move.


If you see an estimation of human brain computation power, it's probably best to assume it's nonsense. There are wildly different estimates, and as computers have gotten faster, the estimates seem to have risen, which suggests ego is involved.


I recall checking a few years ago, and it would have taken about 40,000 high-end GPUs to match the common estimates of the computing power of the brain. It's no doubt much lower now.

The problem is that it takes far more than raw computing power to make AI. We have sufficient computing power but we don't know how to use it, not even close.

As for how it's measured, it's basically a matter of guesstimating the computing power of a single neuron based on its inputs, outputs, and the computation it appears to do to map between them, and then multiplying by the number of neurons in the brain. This is horribly imprecise so estimates vary a lot (describing it as "38" gives the estimate way too much credit, should probably say 10 or 100 instead) but they're probably in the very rough ballpark.




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