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adamgordonbelltoday at 8:31 PM0 repliesview on HN

Small world. I just did a podcast on this same topic, but coming at it from a different direction, ie. me and my neighbor trying to beat the hutter prize for compression.

Hutter Prize being where you are paid if you can compress wikipedia small enough. LLMs do very well at that, if, big if, you ignore the cost of initial weights.

A cool Claude Shannon story:

    Shannon wanted to measure how much information is actually contained in ordinary
  English text. His 1948 theory said such a number must exist, but he had no way to
  calculate it, because the patterns in English reach across dozens of letters and no
  equation or frequency table captures all of them at once.

  So instead of calculating it, he ran an experiment on a person.

  He took a passage from a novel that the subject had not read, and covered it with a
  card so only the text already guessed was visible. He asked the subject to name
  the first letter. If the guess was wrong, he asked again, and kept asking until the
  subject named the correct letter. He wrote down how many guesses it had taken,
  revealed the letter, and moved the card one position to the right. Then he repeated
  the process for the next letter, and the next, through the whole passage.

  What this produced was not a sequence of letters but a sequence of numbers — one
  number per letter, recording how many guesses that letter required. Most of the
  numbers were 1, because someone fluent in English, seeing the preceding text,
  usually names the next letter correctly on the first attempt.

  Shannon then argued that this sequence of numbers contains exactly as much
  information as the original passage.
Sounds a lot like next token prediction to me.

https://corecursive.com/the-hutter-prize/

http://prize.hutter1.net/

https://github.com/hkust-nlp/llm-compression-intelligence

https://www.princeton.edu/~wbialek/rome/refs/shannon_51.pdf