P N AMIN & ASSOCIATES | I got to an extreme to explain what I thought, but it was wrong
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I got to an extreme to explain what I thought, but it was wrong

I got to an extreme to explain what I thought, but it was wrong

I got to an extreme to explain what I thought, but it was wrong

Ok, agreed with you all =). I just think there are some restrictions above the arguments, but Jonathan already said it in details.

Thanks for putting this together. I do however caution people from making assumptions as to what the nature/design of hardware and software systems may be ten years from now. That’s adequate time for a whole new technology/discoveries to come to being and alter our definition of a human brain or a computer is.

Forgive me if this seems high-level and uninformed, but entirely new circuit materials and design *might* be right around the corner. Just the devil’s advocate in me.

Parallelism, concurrency and distribution are huge areas of research now of days, primarily due to the fact that the hardware industry has reached a plateau with the serial arch

Chris, I agree with all your points, but I can’t help thinking ‘straw man’ as I read this. I mean, that may not be quite the right word for it, but isn’t most of this already pretty well assimilated into common thinking about the brain? You have to go back pretty far in time for Minksy Papert. With the exception of #7 (I do think a lot of writers equate neurons with big wet transistors), I don’t think I’ve read anything that compares brain functions with computer circuitry in any kind of a literal sense. (Of course, I’m excluding certain philosophers when I say that – there’s just no accounting for what some of them will argue. )

Now, I will admit that I’m not really that well-read on the subject, so maybe I’ve just been lucky so far.

As someone whose specialty happens to be computer science, I would have to say that I agree overall with your overview, except for a few points.

This is a great overview and vey educational

You could grant that computers are probably not as massively parallel as human brain architecture, but that’s really a question of scale and not essence. And as well, there is a good reason that parallelism hasn’t been a hugely popular field up until now: parallel machines are notoriously difficult to program. (Even with the comparatively minor levels of multi-threading and task distribution being used with new multi-core processors on the market, software dev schedules are being doubled and sometimes tripled to assimilate the requirements.)

Other than that, I don’t have many complains. But when it comes to fields like A.I., I personally find their value from a purely CS centric perspective questionable. As technology, A.I. and adaptive computing have been beneficial in many ways, but I don’t see them as being especially valuable to actually researching more “natural” computing devices (like the brain).

In the end, I see as somewhat akin to human flight. Our air traversing machines are certainly technically different than those produced by nature, but mostly because nature’s ad hoc, Rube Goldberg designs didn’t prove very usefulputing is the same https://yourloansllc.com/personal-loans-md/ way, IMO. The technical value of A.I. should be able to stand on it’s own.

This post is a healthy and much-needed rebuttal to the weird idea that in 20 or so years machine intelligence may exceed human intelligence (the so-called Singularity). The proponents of this idea seem to be basing their belief largely on extrapolating on Moore’s Law.

But if Moore’s law holds up for the next 20 years computers will be only about 4,000 times more capable. (And yes, I’m using the bastardized version of Moore’s law that presumes increasing component density [what Moore was really talking about] correlates in a 1:1 way with increased processing power.) But if artificial intelligence requires only (or chiefly) on increased hardware power than it would already exist. It would just take 4,000 times longer than we would deem practical.

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