Comment by tpurves
7 days ago
"Latent space representation" I have been waiting for this moment in the evolution of AI. Well, waiting with some trepidation. It seems inevitable that frontier AI's will, at some point, leave behind human-comprehensible representations of language. Purely for functional reasons, it's going to start making sense for AI agents to communicate amongst themselves in much more efficient ways than borrowing the languages of flesh-bag humans as an interface medium.
I Imagine next that programming languages, interfaces and API design starts going this direction next. Being written, expressed and optimized as blobs of high dimensional vector space. As humans we might still be able to understand some abstractions of what our AI's are talking about to each other, but maybe not more so then we understand how different regions of our own brain communicate with each other.
I strongly believe that the future is the other way. New programming languages and environments designed for strong auditability and preventing bugs will dominate. Only bad actors will use latent space representation, and it might even be outlawed. But the bad actors will proliferate underground…
I actually built such a language: https://magarshak.com/U.html
Every time a state tries to regulate, it loses to the states that promote instead. Seems to be a better evolutionary strategy.
is that called rust? that is the only thing i feel safe to let agents vibe code
It has already started:
https://zerolang.ai/
I've been thinking the same! Haha
Even for like token efficiency it could make sense - like imagine if the representation were more compact
Agents acn already translate languages quite well. It doesn't seem crazy that they could work and think in a model specific language, and then translate back to English or something for the user
It seems like the most efficient method would be for LLMs to communicated by exchanged latent space representations directly. Serial language is a incredibly inefficient way to encode these, a lot like flattening a complex graph into text.
Is it really more efficient? Any token becomes huge in latent space.
Google's AI just created its own universal 'language' (2016)
https://www.wired.com/story/google-ai-language-create/
That's not really a language. It was just a reporter dumbing down the idea of "vectors in latent space" which eventually became LLMs.