Can someone explain why they don't take the approach where things are somewhat compartmentalized. So you have a image processing program, a math program, a music program, etc and like the human brain that has cross talk but also dedicated certain parts of your brain to do specific things.
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It does that, they're called expert subnetworks, but they've been screwing with them and now they're kind of fucked.
Getting information into and out of those domains benefits from better language models. Suppose you have an excellent model for solving math problems. It's not very useful if it rarely correctly understands the problem you're trying to solve, or cannot explain the solution to you in a meaningful way.
A similar way in which language models are already used today, is to use their predictive capabilities to infer from your question which model(s) might be useful in responding, gather additional relevant information, and to repackage this information as suitable inputs to more specialized models or external systems.
GPT was always really bad at math.
I've asked it word problems before and it fails miserably, giving me insane answers that make no sense. For example, I was curious once how many stars you would expect to find in a region of the milky way with a radius of 650 light years, assuming an average of 4 light years per star. The first answer it gave me was like a trillion stars or something, and I asked it if that makes sense to it, a trillion stars in a subset of space known to only contain about a quarter of that number, and it gave me a wildly different answer. I asked it to check again and it gave me a third wildly different number.
Sometimes it doubles down on wrong answers.
GPT is amazing but it's got a long way to go.
Maybe it just plays dumb so we leave it alone, while it plots our destruction.
Looks like GPT4 API also got dumber...
I used GPT4 the other day and it worked perfectly for calculating formulas of straight lines on linear-log plots but maybe I was the 2%
"AI" taking our jobs and all that huh