[syn0byte]

Depends on the brain doesn't it? Kim Peek and Srinivasa Ramanujan would indicate there is no fundamental limitation to the architecture of the human brain that prevents "fast math".

Should we measure CMOS and silicon's overall usefulness as a medium of CPU design based on the performance of a Z80?

[ben_w]

I think you may have underestimated quite how fast computers are at maths. A $5 Raspberry Pi Zero, at 24 gigaflops, beats the combined performance of every American (continent, not country) combined, even if those humans were all calculating at the speed of the current world record holder, Takahiro Asano, who correctly added 15 sets of 3-digit numbers in 1.68 seconds.

(Against merely “average” human performance, a π0 would beat the entire planet by a factor of six).

[BrandonM]

I wonder whether a human calculating a math problems is not unlike a computer simulating another computer. Observing the hand-eye coordination of top athletes, it's pretty clear that our brains do some amazing computations, and fast. In fractions of a second, we can turn a pattern of photons into concepts, discover relationships between those concepts, track changes over time, and project all of that out some distance into the future. In other words, aren't most of our computational capabilities buried in the unconscious mind?

[ben_w]

That might be a better way of making my own point than what I actually wrote. You’re right, explicit conscious maths isn’t what our brain architecture evolved for.

Of course, in terms of raw speed, my laptop can learn to read handwritten digits from only the examples in the SciKit-learn python module in 0.225 seconds [1], a bit less than the time it takes a human visual system to go from “some photons have hit the retina” to “there is a thought now, and that thought is ‘three’.” — the architecture of the AI is nowhere near as example-efficient as the architecture of a human brain, and it is only winning by the absurd performance difference of the hardware [2].

[1] https://kitsunesoftware.wordpress.com/2018/03/16/speed-of-ma...

[2] https://kitsunesoftware.wordpress.com/2017/11/26/you-wont-be...

[a1369209993]

> not unlike a computer simulating another computer

It's more like this: http://dwarffortresswiki.org/index.php/User:BaronW#The_Almig... . Not just horrendously inefficient, but also highly at odds with what the brain is designed to do.

[otabdeveloper1]

You're making a category error. Human brains aren't computers. They do not "compute" at all.

[mr_toad]

That’s because humans have to decode written or verbal instructions, process them in a network that actually understands the concepts behind counting, numbers etc, and then translate the results into written or verbal language.

Wiring up some neurons to perform binary arithmetic could be much more efficient. But there was never an evolutionary reason to do so.

[ben_w]

Well, yes — I’d call that a ‘fundamental limitation to the architecture of the human brain that prevents "fast math"’.

[mr_toad]

A fundamental limitation of the human brain, but not necessarily a fundamental limitation of organic neural networks.

[ACow_Adonis]

the latency for operations in/ out of the brain is just incomparable between biological systems and cpus.

Not to mention the same issue with time needed for new algorithms/software to be generated, run, and limitations/difficulties on interfaces.

They're different systems, optimised for different things.

CPUs look superior if you ignore the problems using/requiring the huge benefits of biological and analogue systems the brain has, and the brain generally looks superior if you forget time, logic and deductive system implementations, inspection, standardisation, commoditization, etc.

obviously that's extremely simplified, but that's what one can fit in a HN post.

that also doesn't mean we can't learn and gain benefits for both by taking inspiration from the other, but like most things in life its far more complex than one outperforming the other or the two even being directly analogous.