913 karma · joined February 3, 2012
There's your one line change.
EDIT from https://arcprize.org/media/ARC_AGI_3_Technical_Report.pdf:
> We seek to fight two forms of overfitting that would muddy public sensefinding:
> Task-specific overfitting. This includes any agent that is created with knowledge of public ARC-AGI-3 environments, subsequently being evaluated on the same environments. It could be either directly trained on these environments, or using a harness that is handcrafted or specifically configured by someone with knowledge of the public environments.
200 years ago text was much more expensive, and more people memorized sayings and poems and quotations. Now text is cheap, and we rarely quote.
Circular reasoning: that's true only if the posterior is normal, or if your "optimal" is defined by second moments. In infinite variance cases, the best estimator can be median or an alpha moment for alpha < 2, but yikes the math is much more difficult.
-- A mathematician who has indeed fallen into the beauty trap
Some things with heavy tails:
token occurrences
comment thread upvotes
startup IPOs
social follower counts
network latency
github stars
git diffs
power station size
weather eventshttps://en.wikipedia.org/wiki/Central_limit_theorem#The_gene...
The causal chain is: the math is simple -> teachers teach simple things -> students learn what they're taught -> we see the world in terms of concepts we've learned.
The central limit theorem generalizes beyond simple math to hard math: Levy alpha stable distributions when variance is not finite, the Fisher-Tippett-Gnedenko theorem and Gumbel/Fréchet/Weibull distributions regarding extreme values. Those curves are also everwhere, but we don't see them because we weren't taught them because the math is tough.