> 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...

If this is true means, AI Stock bubble burst. (For good)

It looks like a specialized encoder-only(-ish) transformer with scalar and ordinal output heads. Acausal in effect, maybe? Probably not even autoregressive?

I'd use this as a tool an LLM can use for specialized tasks. It's not AI in itself.

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TLDR: Like an LLM, the input is a string, but the output is not a completion. The output is a ranking of elements from a certain enum (e.g, [Yes/No], [A/B/C/D]). They use a technique called Reinforcement Learning for Calibrated Decisions (RLCD) instead of RLHF. Also, inference is a lot faster.

https://docs.typesafe.ai/primitives has a much better explanation

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Finally a fast solution to isOdd / isEven :) /s