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Introducing System One Models and Jev
51–60 of 512 posts
Re: Introducing System One Models and Jev
#52It 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?
Re: Introducing System One Models and Jev
#53If this is true, then AI Stock Bubble burst (for Good)
Re: Introducing System One Models and Jev
#54Is the tradeoff of the parallel output that we don't get arbitrary string generation? like output # of tokens is fixed ahead of time?
Either way, really cool and impressive.
Re: Introducing System One Models and Jev
#55This sounds good but so far all claims just sound like marketing terms. I'd love to see real proof. e.g. "RLCD" and "parallel sampling" have nothing to back it up. also "70-500ms vs 3-329 seconds" are apples-to-oranges unless the LLM baseline is doing comparable work (e.g., long chain-of-thought). If Jev is skipping generation entirely for a narrow structured task, of course it's faster. Nonetheless i want this to be…
Did you see the video where it plays Doom, it made it click for me
BTW it was not multi model playing doom, it was passing structured input and getting structured output. Its not what I thought: frames of video passed and real time game play.
Re: Introducing System One Models and Jev
#56> Outputs
> LLMS > Strings / generated text. Strings are flexible and can be anything: chat responses, code, hallucinations, refusals, or even type-safe structured values. To be used by software, responses need to be parsed + validated. There is also always some risk that the AI goes off the rails.
> Jev > Type-safe structured values. Possible outputs and structure are defined in advance. The model never makes type errors. All answers are accompanied with calibrated probabilities and confidence scores.
I mean, this isn't even remotely comparable to LLMs so why compare? Also, why are they bringing up AGI given there approach is so restrictive that what they're building literally cannot have the creativity required for AGI? The video is 100% marketing slop...
The bulk of the application of LLMs is that they generate reasonably reliable text which doesn't need to be defined in advanced. I'm sure there is a niche for this and congrats to the team, but please let's not hype this as if it's the next big thing in AI...
Re: Introducing System One Models and Jev
#57Re: Introducing System One Models and Jev
#58Parallel inference where you don't want a subagent seems niche. But there is a lot of random things where businesses ultimately want some kind of score instead of generating something.
I think the interesting thing would be seeing if prompt injections still work with this kind of model.
Re: Introducing System One Models and Jev
#59[deleted]
Re: Introducing System One Models and Jev
#60> [others] Output tokens: ~5x more expensive than input tokens. > [them] Output tokens: FREE (too cheap to meter). I'm very confused by this.
They're not doing autoregression, so all the outputs are computed in one big forward pass. Very cheap.
I think OP is confused about "others" vs "them".