Reinventing Entropy | Compression is Intelligence Part 1
202633 min
How to study the compressibility of language.
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Animation credit:
Manim scenes by Aaron Gostein and Grant Sanderson
Shannon’s story, as well as those for various pi creatures, by Mitchell Zemil.
Lunar robot and prediction/compression coin by Paul Dancstep
NanoGPT animations by Clayton Rabideau
The way of visualizing entropy shown here is something I first came across in this excellent post by Chris Olah:
https://colah.github.io/posts/2015-09-Visual-Information/
Shannon’s “A Mathematical Theory of Communication”
https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf
Shannon’s “Prediction and Entropy of Printed English”
https://www.princeton.edu/~wbialek/rome/refs/shannon_51.pdf
Scientific American article that mentions the story with Von Neumann suggesting the name Entropy:
https://www.esalq.usp.br/lepse/imgs/conteudo_thumb/Energy-and-Information.pdf
Timestamps:
0:00 - On “Compression is intelligence.”
3:28 - The warmup example
10:46 - What perfect compression looks like
14:47 - Defining information
17:40 - Information of language
24:29 - Defining Entropy
31:14 - 3b1b Talent
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These animations are largely made using a custom Python library, manim. See the FAQ comments here:
https://3b1b.co/faq#manim
Music by Vincent Rubinetti.
https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown
https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u
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