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The latter premise can seem plausible for reasoning, but it is preposterous for sensing. The sounds and images processed by human ears and eyes represent megabytes per second of raw data, itself enough to overwhelm computers past and present. Text, speech and vision programs derive meaning from snippets of such data by weighing and reweighing thousands or millions of hypotheses in its light. At least some of the human brain works similarly. Roughly ten times per second at each of the retina's million effective pixels, dozens of neurons weigh the hypothesis that a static or moving boundary is visible then and there. The visual cortex's ten billion neurons elaborate those results, each moment appraising possible orientations and colors at all the image locations. Efficient computer vision programs require over 100 calculations each to make similar assessments. Most of the brain remains mysterious, but all its neurons seem to work about diligently as those in the visual system. Elsewhere I've detailed the retinal calculation to conclude that it would take on the order of 100 trillion calculations per second of computing -- about a million present-day PCs -- to match the brain's functionality.

That number presumes an emulation of the brain at the scale of image edge detectors: a few hundred thousand calculations per second doing the job of a few hundred neurons. The computational requirements would increase (maybe a lot) if we demanded emulation at a finer grain, say explicit representation of each neuron. By insisting on a fine grain we constrain the solution space and outlaw global optimizations. On the plus side, by constraining the space we simplify the search! No need to find efficient algorithms for edge detection and other hundred-neuron-scale nervous system functions. If we had good models for neurons and a wiring diagram of a brain, we could emulate it as a straightforward network simulation. The problems of Artificial Intelligence would be reduced to merely instrumentally- and computationally-daunting work.