“we don’t need to prove the 2020 election was stolen, it’s implied because trump had bigger crowds at his rallies!” -90% of trump supporters
Another good example is the Monty Hall “paradox” where 99% of people are going to incorrectly tell you the chance is 50% because they took math and that’s how it works.
Just because something seems obvious to you doesn’t mean it is correct. Always a good idea to test your hypothesis.
Trump Rallies would be a really stupid sample data set for American voters. A crowd of 10,000 people means fuck all compared to 158,429,631. If OpenAI has been training their models on such a small pool then I’d call them absolute morons.
A crowd of 10,000 people means fuck all compared to 158,429,631.
I agree that it would be a bad data set, but not because it is too small. That size would actually give you a pretty good result if it was sufficiently random. Which is, of course, the problem.
But you’re missing the point: just because something is obvious to you does not mean it’s actually true. The model could be trained in a way to not be biased by our number choice, but to actually be pseudo-random. Is it surprising that it would turn out this way? No. But to think your assumption doesn’t need to be proven, in such a case, is almost equivalent to thinking a Trump rally is a good data sample for determining the opinion of the general public.
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“we don’t need to prove the 2020 election was stolen, it’s implied because trump had bigger crowds at his rallies!” -90% of trump supporters
Another good example is the Monty Hall “paradox” where 99% of people are going to incorrectly tell you the chance is 50% because they took math and that’s how it works.
Just because something seems obvious to you doesn’t mean it is correct. Always a good idea to test your hypothesis.
Trump Rallies would be a really stupid sample data set for American voters. A crowd of 10,000 people means fuck all compared to 158,429,631. If OpenAI has been training their models on such a small pool then I’d call them absolute morons.
I agree that it would be a bad data set, but not because it is too small. That size would actually give you a pretty good result if it was sufficiently random. Which is, of course, the problem.
But you’re missing the point: just because something is obvious to you does not mean it’s actually true. The model could be trained in a way to not be biased by our number choice, but to actually be pseudo-random. Is it surprising that it would turn out this way? No. But to think your assumption doesn’t need to be proven, in such a case, is almost equivalent to thinking a Trump rally is a good data sample for determining the opinion of the general public.