An Analysis of DeepMind's 'Language Modeling Is Compression' Paper
A detailed analysis of the DeepMind/Meta study: how large language models achieve unprecedented compression rates on text, image, and audio data - and the implications of these results
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I wonder if this is actually comparable to the way our brains store long-term memory?
Interesting. I'm just thinking aloud to understand this.
In this case, the models are looking at a few sequence of bytes in their context and are able to predict the next byte(s) with good accuracy, which allows efficient encoding. Most of our memories are associative, i.e. we associate them with some concept/name/idea. So, do you mean, our brain uses the concept to predict a token which gets decoded in the form of a memory?
Firstly—maybe what we consider an “association” is actually an indicator that our brains are using the same internal tokens to store/compress the memories.
But what I was thinking of specifically is narrative memories: our brains don’t store them frame-by-frame like video, but rather, they probably store only key elements and use their predictive ability to extrapolate the omitted elements on demand.
This seems likely to me. The common saying is that "you hear what you want to hear", but I think more accurately it's "you remember what has meaning to you". Recently there was a study that even visual memory was tightly integrated with spoken language: https://www.science.org/doi/10.1126/sciadv.adh0064
However, there's a lot of variation in memory among humans. See: The Mind of a Mnemonist.
Yes, that makes much more sense.
No, because our brains also use hierarchical activation for association, which is why if we're talking about bugs and I say "I got a B" you assume its a stinging insect, not a passing grade.
If it was simple word2vec we wouldn't have that additional means of noise suppression.
