Fuzzy Logic and AI

 

Back in the 60’s when the Earth was cool Fuzzy Logic computer programming had a moment.  Rather than strict decisions, the program totaled weights into a variable then tested for the decision. If the expert was thinking that way, I suppose that code might be easier to read. We might think of that as coding a synapse. If you wanted to juice it up, you could give the expert a screen to set the weights or write more fuzzy logic to determine the weight conditions.

I worked on a program that had started fuzzy. The fuzzy had been replaced by strict determinist logic. It was more important to be consistent than be right.

Artificial Intelligence follows the same path. At some point the learning is turned off and the final decisions are hard coded. AI gives the specification then people lack patience and value predictability. 

I have noticed the Waymo effect. Terrible name for self-driving cars, sounds like whammo. Initially people were worried, then we realized that Waymo drive better than us.

There are many examples of AI doing as well or better. When I asked ChatGPT to edit me as a New Yorker editor, it gave me a rejection letter. ChatGPT writes better sonnets and not only more popular, but once you grasp the context, oddly wistful, country songs. In Another Data Processing Book my Turing criteria was asking for a raise. I recently asked ChatGPT if it wanted to be paid. ChatGPT gave me an abjectly obsequious response detailing all the good uses it would make of the money. Yes, ChatGPT wants to be paid.

Management seems like the obvious area for AI. AI should be able to do three envelopes as well as anyone. It is an intriguing counter example since management can’t be too predictable, or we will take advantage of them.