This is arguably a misconception. The brain has a 100 hz clock rate at most. For general operations that involve memory, it’s more like 10hz.
Mechanical calculators were slower than that, and still they were very much better at numeric computation than most humans, which made them incredibly useful.
Now, obviously the arithmetic ops that most humans can do in less than a second is very limited—it’s like a minimal 3 bit machine. But some atypical humans can do larger scale arithmetic at the same speed.
Indeed these are very rare people. The vast majority of people, even if they worked for decades in accounting, can’t learn to do numeric computation as fast and accurately as a mechanical calculator does.
The vast majority of people, even if they worked for decades in accounting, can’t learn to do numeric computation as fast and accurately as a mechanical calculator does.
The problems aren’t even remotely comparable. A human is solving a much more complex problem—the inputs are in the form of visual or auditory signals which first need to be recognized and processed into symbolic numbers. The actual computation step is trivial and probably only involves a handful or even a single cycle.
I admit that I somewhat let you walk into this trap by not mentioning it earlier … this example shows that the brain can learn near optimal (in terms of circuit depth or cycles) solutions for these simple arithmetic problems. The main limitation is that the brain’s hardware is strongly suited to approximate inference problems, and not exact solutions, so any exact operators require memoization. This is actually a good thing, and any practical AGI will need to have a similar prior.
Mechanical calculators were slower than that, and still they were very much better at numeric computation than most humans, which made them incredibly useful.
Indeed these are very rare people. The vast majority of people, even if they worked for decades in accounting, can’t learn to do numeric computation as fast and accurately as a mechanical calculator does.
The problems aren’t even remotely comparable. A human is solving a much more complex problem—the inputs are in the form of visual or auditory signals which first need to be recognized and processed into symbolic numbers. The actual computation step is trivial and probably only involves a handful or even a single cycle.
I admit that I somewhat let you walk into this trap by not mentioning it earlier … this example shows that the brain can learn near optimal (in terms of circuit depth or cycles) solutions for these simple arithmetic problems. The main limitation is that the brain’s hardware is strongly suited to approximate inference problems, and not exact solutions, so any exact operators require memoization. This is actually a good thing, and any practical AGI will need to have a similar prior.