[00:00] compressibility of English text depends on how at understanding the probability of each new His earliest experiments involved looking at
[00:13] tracking the statistics of what tended to follow. where you see the letters TH and build The problem here is that this completely
[00:27] most notably those that never show up But longer sequences give more context to guide its most predictable and hence most compressible.
[00:40] of language, he needed some other way So he turned to one of the most readily to him in the 1940s: His wife, Betty.
[00:53] each new letter from a given passage. and every time she guessed correctly, His idea was that this new string of text
[01:06] the same information, in the sense that it duplicate of his wife to fill in the entire text. of information requires knowing the
[01:20] so he needed something better. of Printed English, Shannon outlined an instead of just logging whether their guess
[01:32] many guesses were necessary for his human Separately, he combined the idea of statistics to make an estimate at the implicit probabilities
[01:46] letter, based on their number of guesses. least 100 characters of context, be compressible down to around 1 bit per
[01:59] estimate, he was forced to go beyond pure data Today, more than 75 years later, to this limit is not through merely probing at
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