Comment by theGnuMe
2 years ago
>high-dimensional latent-space embedding to a Markov model
That's what we call a hidden Markov model.
>There's a school of thought that says that lossy compression doesn't just require intelligence, it is intelligence, and LLMs can be seen as an example of that equivalence.
SVD is used to implement lossy compression as does JPEG encoding... these algorithms are in no way intelligent.
They’re doing highly specific tasks where the intelligence can come from the designer of the algorithm.
In particular, JPEG has intelligence encoded about how graphics are displayed, what detail we won’t notice is missing, and what artifacts we won’t notice are present, much like the psychoacoustic models behind lossy music compression schemes like MP3.
But we had to feed an encoder that by way of algorithmic design. It’s hardcoded intelligence, like any other function, but with a lot more outside knowledge required to do it right than a sort or swap.
I’d call an LLM a more general problem solver. It can write cogent limericks, convincingly screw up math, summarize papers it’s never seen before, generate book plots or character arcs based on specific requests, translate to a language you just made up and explained in the prompt, etc.
The intelligent bits are emergent and can do something reasonable with novel input, even if it doesn’t closely resemble the exact material it was trained on.
The comparison would be a process that could lossy-compress any kind of sensory media possible with no perceptible loss, based solely on its training on human capabilities and how the reproduction devices work—i.e. it could create the JPEG algorithm, not just perform it.
SVD is used to implement lossy compression as does JPEG encoding... these algorithms are in no way intelligent.
You'll have to take that up with people above my pay grade. It's not that simple, apparently. Call me when a Markov model can explain why it's equivalent to an LLM.