alt Hacker NewsIs it, even when applied to trivial classifiers (possibly "classical" ones)?
I feel that we're wrong to be focusing so much on the conversational/inference aspect of LLMs. The way I see it, the true "magic" hides in the model itself. It's effectively a computational representation of understanding. I feel there's a lot of unrealized value hidden in the structure of the latent space itself. We need to spend more time studying it, make more diverse and hands-on tools to explore it, and mine it for all kinds of insights.
Replies
ohhh yeah that’s the interoperability game. not just crank model size and pray it grows a brain. everyone's hyped on scale but barely anyone’s thinking glue. anthropic saw it early. their interop crew? scary smart folks, some I know personally. zero chill, just pure signal.
if you wanna peek where their heads at, start here https://www.anthropic.com/research/mapping-mind-language-mod... not just another ai blog. actual systems brain behind it.
I agree. Isn't this just utilizing the representation learning that's happened under the hood of the LLM?
For this and sibling -- yes. Essentially, using the output of any model as an input to another model is transfer learning.