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Comment by burnte | Hacker News Reader

Give me a lookup table of 50 petabytes, let me seed that LUT with billions of files, and I, too, can write an amazingly efficient compression algorithm.

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Sure. But you can only use text files in your seed.

What's the difference between 0x2e and .? You can easily transform between representations of data, be that text or binary in any encoding you'd like. The model input token vector (or LUT index) is still the same. I don't see a difference there.

You are missing the point. LLM are not trained on image data, but on human text data. This is not the same as encoding the image as ASCII characters.

I don’t see why video chat apps shouldn’t do something like this. Maybe the encoding/decoding is too slow.

Accessing a giant lookup table is about the most cache-unfriendly thing you could do. This is not a good approach when latency matters.

Smaller LUTs are fun to think of, especially if you eliminate the lossless angle. I'm really not that interesting to watch during a videoconference. When I'm not talking, it would probably be better for viewers to make up a stream of me sitting still with a dumb look on my face. It should be loosely matched to my actual position/pose, so that when I start talking or my dog attacks me or the bomb explodes and wipes out my room, the transition back to actual data is relatively seamless.

(And if you set the threshold high enough, maybe I don't need to be in the room at all...)

This is a technology described in the book "A Fire Upon the Deep" by Vernor Vinge. It's discussed fairly close to the end.

But it isn’t generalized like this is supposed to be

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