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Hutter Prize: At the end of 2022, what will be the best bits-per-character compression of a 1GB sample of Wikipedia?

The Hutter Prize is a 500'000€ Prize for Compressing Human Knowledge. The competition's stated mission is "to encourage development of intelligent compressors/programs as a path to AGI." Since it is argued that Wikipedia is a good indication of the "Human World Knowledge," the prize often benchmarks compression progress of algorithms using the enwik9 dataset, a representative 1GB extract from Wikipedia.

Since 2006, the Hutter Prize has galvanized not only data scientists but also many AI researchers who believe that image/text compression and AI are essentially two sides of the same coin. Compression algorithms are based on the premise of finding patterns in data and are predictive in nature. Furthermore, many machine learning researchers would agree that systems with better predictive models possess more "understanding" and intelligence in general.

The Algorithmic Information Theoretic (AIT) philosophy of this contest is that compression is induction (aka comprehension) and decompression is inference (aka prediction). According to AIT, lossless compression is adequate to avoid both confirmation bias and over-fitting. (Of Algorithmic Information Theory, Marvin Minsky's final advice was that, "Everybody should learn all about it and spend the rest of their lives working on it.") See here for an interesting podcast interview.

We ask:

What will be the best bits-per-character compression of the Hutter Prize at the end of 2022?

To calculate the current bits-per-character(*), click here for the Hutter Prize records table, look in the "Total Size" column for most-recently awarded value, multiply by 8 and divide by .

(*) Strictly speaking, this is "bits-per-byte" but this relaxation of definitions is quite common in computerdom.


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