Tom Pittman's WebLog

2021 December 9 -- Why Is AI So Dumb?

The cover feature of the October issue of the IEEE Spectrum is a collection of a half-dozen articles that focus, in one way or another, on the cover topic. The problem is like the story of the guy during the war who noticed the lack of farm produce in the San Francisco area, so he drove his pickup to the valley and loaded up on produce and drove it back to the City. He lost money on every trip, but he expected to make it up in volume. If a thousand years of historical observation does not evolve any new biological features, a million or half-billion years can and did. If a million artificial neurons can't tell the difference between a picture of three cupcakes and "a dog and cat playing frisbee," a billion-neuron neural net (NN) surely will. Nobody in the whole issue has noticed the elephant in the room. I call it entropy: You cannot get more information out of a closed system than you put in. We all know it doesn't happen in a few hours, but surely thousands of hours on a million-GPU megaprocessor can make it happen? They all seem to think so.

This Spectrum issue had been sitting on a stack of magazines to read, staring up at me with unseeing eyes (circular shoulder joints) for several days when it suddenly occurred to me that the Goedel Incompleteness Theorem applies: any closed mathematical system -- that would include any digital computer working on digital data (such as digitized images) by applying other digital data such as the neuron weights in a NN -- cannot solve some problems that can otherwise be known to be true. At the very least, it means the computer cannot become smarter than its programmer, and likely not even close.

I think the reason people are not seeing this is Religion = believing what you know ain't so. So they dare not even go there. Their problem, not mine. It might become my problem when they start putting poorly trained autonomous cars on the road -- but Covid will protect me from most of that foolishness.


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