It seems like the docs[0] are a better explanation? The comparison to llm tokens is kinda confusing.
It looks like the model takes as input a state (structured text? not sure if multi-modal) and a question (as a "Choice", "Score", or "Noul") with some additional augmentations possible. Then outputs the question's answers as appropriate (e.g. a choice, accompanying probabilities, confidence).
Edit: On the AI primer page, it looks like they do the RLCD on a pre-trained base model?
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)
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?
architecture is close to the chest for now, but we have talked about writing a paper
I don't want to shill my blog too much, but I will say data is probably far most interesting than architecture: <a href="https://www.completeskeptic.com/p/the-bitterest-lesson" rel="nofollow">https://www.completeskeptic.com/p/the-bitterest-lesson
big_toast · · focus · HN ↗
It looks like the model takes as input a state (structured text? not sure if multi-modal) and a question (as a "Choice", "Score", or "Noul") with some additional augmentations possible. Then outputs the question's answers as appropriate (e.g. a choice, accompanying probabilities, confidence).
Edit: On the AI primer page, it looks like they do the RLCD on a pre-trained base model?
[0]:<a href="https://docs.typesafe.ai/concepts/system-one" rel="nofollow">https://docs.typesafe.ai/concepts/system-one
CompleteSkeptic · · focus · HN ↗
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)
ttul · · focus · HN ↗
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?
CompleteSkeptic · · focus · HN ↗
I don't want to shill my blog too much, but I will say data is probably far most interesting than architecture: <a href="https://www.completeskeptic.com/p/the-bitterest-lesson" rel="nofollow">https://www.completeskeptic.com/p/the-bitterest-lesson
ttul · · focus · HN ↗