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khalic

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Qwen 3.8 4 days ago

It’s tempting to associate both events, but when a sector is strung up like RL (representation learning) is right now, we’re bound to see things appearing at the same time. It happens a lot in frontier research, some people even publishing identical claims, independently, with just hours or days between them

Are you discarding the utility of Gaussian functions in analysis simply because the independent variable is time? A Gaussian curve can be used as a descriptive model without claiming that the observations themselves are a probability distribution

The fit does not prove causation, but it does show that the decline was already well described by a trend that began years before generative AI. If the claim is that 2023 created a separate structural break, it's different claim then the title describes

Fitting a mathematical function to a dataset does not implicitly adopt the ontological baggage of probability theory. That is a fundamental misunderstanding of applied mathematics

A bell curve is not an "amortised function." Amortization applies to accounting and algorithmic time complexity, not probability distributions. You're likely thinking of a Probability Density Function (PDF). If you are going to police terminology, it helps to use the correct words. Second, fitting a curve with an R^2 of 0.911 is the exact opposite of "making shapes out of clouds.

I’ll always remember Opus going full sarcastic last year, when I asked it to scrape a few tests it had just written:

Of course, let’s delete these perfectly fine tests and replace them with your latest idea…