alt Hacker NewsCEO here - that is right!
I do agree that the comparison to LLM tokens is hard to understand (also because output tokens are not comparable).
But yes, text or structured state (like a JSON with multiple pieces of text in) -> decisions out (e.g. choice maps to "match" statement, "score" maps to sorting, "noul" short for bernoulli maps to if-statements)
Replies
here is how I attempted to explain it to my company's AI group chat, is this roughly accurate?
"instead of autoregressive string output it instead outputs structured type-safe 'decisions' with probabilities/confidence scores, each generated in parallel
so sort of more like a Large Classification Model than a Large Language Model? or, maybe better to think of it as a sort of "shift left" in the LLM's transformer architecture, allowing you to replace the predefined token vocabulary of an LLM with a prescribed set of 'decisions' that need to be made based off the input context; and exposing those probabilities directly so they can be integrated into the system logic, instead of just sampling from top-K.
all of this while still being instruction-tuned (!!!)"
It's always been possible to build classification pipelines using LLM embeddings as the input. seems like this is a much more sophisticated / useful application of that concept
For many day-to-day computing use cases, Jev seems far better suited than an autoregressive language model, if for no other reason than it is not wasting compute thinking about anything other than how to spit out a decision.
Do you have an architectural explainer yet for Jev or are you holding that close to your chest and letting the magic rip for now?
Hi - first congratulations, System One looks really promising.
The Doom demo really help me, at least, to understand how System One differs from LLMs. However the first demo (Side-by-side demonstration) - I'm struggling to understand what is going on here!
I see this super interestingly as the "subconscious" to the llms "conscious" for lack of better terms. I'm super interested in this for broad and rapid decision making in the context of consumer agents so will be signing up for sure.
It's unclear if the context extends as the conversation grows?
Small request, can we get an explanation of the naming of "noul" in the docs[0]. I tried googling, and searching the docs and didn't understand why it was called that.
(I'd also argue something like p_yes or just probability might be a simpler name, but I'm sure there's a better reason behind Bernoulli maps).
[0] https://docs.typesafe.ai/primitives/noul#noul