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Being good at something, and being efficient at something, in general, is not equivalent.

Earliest computers were designed for calculating missile trajectories, and they were good at that task. Not efficient (required a large room, cost millions, and consumed megawatts of power), but fast enough to produce results faster than any other known method. Later, this design, which was inefficient even for the original task, was used for other tasks, and it was of course even more inefficient there. Modern computers have become more efficient at the original task (numeric calculations), but they are still horribly inefficient for hosting forums, because of the limits of vNA. If you want to have something that is more efficient, the answer is easy - build a specialized processor, designed to host forums. It won't be able to do anything else, but if will be orders of magnitude more efficient at hosing forums. You have to choose having one computer that is inefficient at many different tasks, or many computers that are efficient at single tasks.




I don't see where I ever disputed that specialized hardware is better than general purpose hardware at the task for which it is specialized; in fact, I specifically made note of that. Is there a reason you think it refutes a part of the thesis I was presenting?

The claim under contention is that there's some root deficiency of the vNA at computing in general, that perhaps could be surpassed by some ingenious FP-inspired model. If the only "limit" or "inefficiency" of vNA is that it doesn't achieve ASIC level efficient on every task, that's not much of a criticism. Even applied "horribly" to hosting discussion forums, the vNA is light years beyond all other general hardware.

I was under the assumption that a more substantial criticism of vNA was being offered. But the above criticism makes no sense unless it were somehow economical to rearchitect a computer for every distinct problem you plan to work on.


I was disputing your claim that brains are less efficient than vNA based computers. My point is that for vast majority of tasks we face on a daily basis, our brains are vastly more efficient than our computers. Note that a brain is a general purpose, non vNA based hardware. My conclusion: we should try to build a brain-like hardware out of silicon, as soon as we learn enough about how brains work.




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