GPT is all you need for the back end

My theory on this is that it would confuse the dataset having to both transcribe and then "understand" what was asked. By reducing this single variable [which we all know is technically already possible: audio transcription], the dataset is allowing itself to be trained with less initial noise.

>Here's the thing: Frank went to the drugstore for condoms or chewing gum or whatever, and the pharmacist told him that his sixteen-year-old daughter had become an architect and was thinking of droping out of high school because it was such a waste of time. She had designed a recreation center for teenagers in depressed neighborhoods with the help of a new computer pogram the school had bought for its vocational students, dummies who weren't going to anything but junior colleges. It was called Palladio.

>Frank went to a computer store, and asked if he could try out Palladio before buying it. He doubted very much that it could help anyone with ihs native talent and education. So right there in the store, and in a period of no more than half an hour, Palladio gave him what he had asked it for: working drawings that would enable a contractor to build a three-story parking garage in the manner of Thomas Jefferson.

>Frank had made up the craziest assignment he could think of, confident that Palladio would tell him to take his custom elswhere. But it didn't! It presented him with menu after menu, asking how many cars, and in what city, because of various local building codes, and whether trucks would be allowed to use it, too, and on and on. It even asked about surrounding buildings, and whether Jeffersonian architecture would be in harmony with them. It offered to give him alternative plans in the manner of Michael Graves or I.M. Pei.

>It gave him plans for the wiring and plumbing, and ballpark estimates of what it would cost to build in any part of the world he cared to name.

>So Frank [the "experienced architect"] went home and killed himself the first time.

TIMEQUAKE written 1996, published 1997, by Kurt Vonnegut

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I have already been cited, myself, by Perplexity.AI [when I asked "How many transistors does the new Mac Mini M2 Pro have?" — I had provided this citation into the Wikipedia page "Transistor Density" — and this was strange because I know nothing and am now "an expert" (I am not — I just enjoy reading and talking).

When I ask http://Perplexity.AI "What did Vonnegut determine 'what most women wanted'?" and it spits out the perfect Vonnegut answer: A WHOLE LOT OF PEOPLE TO TALK TO [this is a perfect response; Vonnegut spends pages discussing how having had two daughter and two wives still limits this, but if you force him to answer, it is exactly what Perplexity deduced.

I have been playing / "teaching" technical people far-more-cabable (but less-human) than I... to play with ChatGPT-like interfaces.

It is so hard to get ONLY_BRAINS to stop asking technical questions [database] and start MAKING CONNECTIONS between their individual areas-of-expertise. To guess a human connection, and then let GPT brute-force a probabilistic response. To get an autistic 160IQ+ person to ask questions better than "why iz sky blu?" and instead be looking more at questions along "why do people care that the sky is blue?"

Because that is a better question, and provides better answers.