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I... don't understand how AI related to video codecs. Maybe because I don't understand either video codecs or AI on a deeper level.

bjoli 1y ago
It is like upscaling. If you could train AI to "upscale" your audio or video you could get away with sending a lot less data. It is already being done with quite amazing results for audio.
jl6 1y ago
It has long been recognised that the state of the art in data compression has much in common with the state of the art in AI, for example: http://prize.hutter1.net/ http://prize.hutter1.net/ https://bellard.org/nncp/ https://bellard.org/nncp/
- ddtaylor 1y ago
  Some view these as so interconnected that they will say LLMs are "just" compression.
  - pjc50 1y ago
    Which is an interesting view when applied to the IP. I think it's relatively uncontroversial that an MP4 file which "predicts" a Disney movie which it was "trained on" is a derived work. Suppose you have an LLM which was trained on a fairly small set of movies and you could produce any one on demand; would that be treated as a derived work? If you have a predictor/compressor LLM which was trained on all the movies in the world, would that not also be infringement?
    - mr_toad 1y ago
      MP4s are compressed data, not a compression algorithm. An MP4 (or any compressed data) is not a “prediction”, it is the difference between what was predicted and what you’re trying to compress. An LLM is (or can be used) as a compression algorithm, but it is not compressed data. It is possible to have an overfit algorithm exactly predict (or reproduce) an output, but it’s not possible for one to reproduce all the outputs due to the pigeonhole principle. To reiterate - LLMs are not compressed data.
Retr0id 1y ago
AI and data compression are the same problem, rephrased.
- oblio 1y ago
  Which makes Silicon Valley, the TV show, even funnier.
  - chisleu 1y ago
    holy shit it does. The scene with him inventing the new compression algorithm basically foreshadowed the gooning to follow local LLM availability.
tdullien 1y ago
Every predictor is a compressor, every compressor is a predictor. If you're interested in this, it's a good idea reading about the Hutter prize (https://en.wikipedia.org/wiki/Hutter_Prize https://en.wikipedia.org/wiki/Hutter_Prize) and going from there. In general, lossless compression works by predicting the next (letter/token/frame) and then encoding the difference from the prediction in the data stream succinctly. The better you predict, the less you need to encode, the better you compress. The flip side of this is that all fields of compression have a lot to gain from progress in AI.
- rahimnathwani 1y ago
  Also check out this contest: https://www.mattmahoney.net/dc/text.html https://www.mattmahoney.net/dc/text.html Fabrice Bellard's nncp (mentioned in a different comment) leads.
deleted 1y ago
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bjoli 1y ago

It is like upscaling. If you could train AI to "upscale" your audio or video you could get away with sending a lot less data. It is already being done with quite amazing results for audio.

jl6 1y ago

It has long been recognised that the state of the art in data compression has much in common with the state of the art in AI, for example: http://prize.hutter1.net/ http://prize.hutter1.net/ https://bellard.org/nncp/ https://bellard.org/nncp/

ddtaylor 1y ago
Some view these as so interconnected that they will say LLMs are "just" compression.
- pjc50 1y ago
  Which is an interesting view when applied to the IP. I think it's relatively uncontroversial that an MP4 file which "predicts" a Disney movie which it was "trained on" is a derived work. Suppose you have an LLM which was trained on a fairly small set of movies and you could produce any one on demand; would that be treated as a derived work? If you have a predictor/compressor LLM which was trained on all the movies in the world, would that not also be infringement?
  - mr_toad 1y ago
    MP4s are compressed data, not a compression algorithm. An MP4 (or any compressed data) is not a “prediction”, it is the difference between what was predicted and what you’re trying to compress. An LLM is (or can be used) as a compression algorithm, but it is not compressed data. It is possible to have an overfit algorithm exactly predict (or reproduce) an output, but it’s not possible for one to reproduce all the outputs due to the pigeonhole principle. To reiterate - LLMs are not compressed data.

Retr0id 1y ago

AI and data compression are the same problem, rephrased.

oblio 1y ago
Which makes Silicon Valley, the TV show, even funnier.
- chisleu 1y ago
  holy shit it does. The scene with him inventing the new compression algorithm basically foreshadowed the gooning to follow local LLM availability.

tdullien 1y ago

Every predictor is a compressor, every compressor is a predictor. If you're interested in this, it's a good idea reading about the Hutter prize (https://en.wikipedia.org/wiki/Hutter_Prize https://en.wikipedia.org/wiki/Hutter_Prize) and going from there. In general, lossless compression works by predicting the next (letter/token/frame) and then encoding the difference from the prediction in the data stream succinctly. The better you predict, the less you need to encode, the better you compress. The flip side of this is that all fields of compression have a lot to gain from progress in AI.

rahimnathwani 1y ago
Also check out this contest: https://www.mattmahoney.net/dc/text.html https://www.mattmahoney.net/dc/text.html Fabrice Bellard's nncp (mentioned in a different comment) leads.

deleted 1y ago

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