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386 points carabiner | 7 comments | | HN request time: 1.344s | source | bottom
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intoamplitudes ◴[] No.44007496[source]
First impressions:

1. The data in most of the plots (see the appendix) look fake. Real life data does not look that clean.

2. In May of 2022, 6 months before chatGPT put genAI in the spotlight, how does a second-year PhD student manage to convince a large materials lab firm to conduct an experiment with over 1,000 of its employees? What was the model used? It only says GANs+diffusion. Most of the technical details are just high-level general explanations of what these concepts are, nothing specific.

"Following a short pilot program, the lab began a large-scale rollout of the model in May of 2022." Anyone who has worked at a large company knows -- this just does not happen.

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pixl97 ◴[] No.44007628[source]
>The data in most of the plots (see the appendix) look fak

Could a Benford's Law analysis apply here to detect that?

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1. constantcrying ◴[] No.44007841[source]
How would you apply it, why would it be applicable?
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2. tough ◴[] No.44010826[source]
Fake data is usually too clean
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3. constantcrying ◴[] No.44013365[source]
And? How is that at all a relevant observation?
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4. tough ◴[] No.44015471{3}[source]
Anyone looking at inly the data objectively should br able to comento terms that is distribution is unnatural, as it turns out to fake organic isnt as easy
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5. constantcrying ◴[] No.44015620{4}[source]
This was about Benford's law.
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6. tough ◴[] No.44016789{5}[source]
Sorry for replying to the wrong thread, should have just been a general reply indeed
7. pixl97 ◴[] No.44017835{3}[source]
And what exactly do you think Benfords law is?