[vlovich123]

That’s a stretch to call it lossy. To my eye the purpose of the LSTM seems indistinguishable from a traditional compression dictionary.

And that still doesn’t show how lossless compression is tied to intelligence. The example I always like to give is, “What’s more intelligent? Reciting the US war of independence Wikipedia page verbatim every time or being able to synthesize a useful summary in your own words and provide relevant contextual information such as it’s role in the French Revolution?”

[tshaddox]

These lossless compression algorithms don’t just recall a fixed string of text. Obviously that can be accomplished trivially by simply storing the text directly.

These lossless compression algorithms compress a large corpus of English text from an encyclopedia. The idea is that you can compress this text more if you know more about English grammar, the subject matter of the text, logic, etc.

I think you’re distracted by the lossless part. The only difference here between lossy and lossless compression is that the lossy algorithm also needs to generate the diff between its initial output and the real target text. Clearly a lossy algorithm with lower error needs to waste fewer bits representing that error.

[vlovich123]

There’s no immediately obvious reason that you should be able to come up with a diff correction to apply to recreate loseless that is more efficient than traditional compressors. In essence you’re still stuck with the idea that you have a compression dictionary and trying to build a more efficient dictionary. It’s not clear there’s a link between that and intelligence.