Correct me if I am wrong, can I use Jev and this tool for ticket classification? I mean, for instance, a level 1 ticket contains a screenshot of the login page that displays an error, LLM can do it perfectly, can Jev do it?

I don’t understand Jev or this. I used this since it’s open source (good job btw!) with the following. State: “a 6 sided die rolled a 3”, question (noul): “Is the number odd?”

Answer: 9% chance, with 91% confidence.

Heh???

Ok, even worse. 75% chance a coin landed heads up?

State: I flipped a coin. Question:

{ "noul_result": { "type": "noul", "instructions": "Did the coin land heads up?" }, "choice_result": { "type": "choice", "instructions": "Determine if the coin landed heads or tails up.", "criteria": { "heads": "the coin landed heads up", "tails": "the coin landed tails up" } } }

Ran on: https://huggingface.co/spaces/convaiinnovations/laya-demo

Result: { "model": "laya", "answers": { "noul_result": { "type": "noul", "noul": 0.6839, "rl_agent": { "act_probability": 1.0 } }, "choice_result": { "type": "choice", "choice": "heads", "probabilities": { "heads": 0.7407, "tails": 0.2593 }, "confidence": 0.1743, "rl_agent": { "act_probability": 1.0 } } }, "usage": { "input_tokens": 76, "output_tokens": 0 }, "latency_ms": 93.8 }

Trying to be even more good-faith:

State: "A fair coin was flipped once. The result was not observed. No other information about the outcome is available."

Questions: { "noul_result": { "type": "noul", "instructions": "Given only the supplied state, what is the probability that the coin landed heads up?" }, "choice_result": { "type": "choice", "instructions": "Given only the supplied state, determine which outcome occurred.", "criteria": { "heads": "the coin landed heads up", "tails": "the coin landed tails up" } } }

Result:

{ "model": "laya", "answers": { "noul_result": { "type": "noul", "noul": 0.1265, "rl_agent": { "act_probability": 1.0 } }, "choice_result": { "type": "choice", "choice": "tails", "probabilities": { "heads": 0.2522, "tails": 0.7478 }, "confidence": 0.1853, "rl_agent": { "act_probability": 1.0 } } }, "usage": { "input_tokens": 123, "output_tokens": 0 }, "latency_ms": 154.5 }

    python3 - <<'EOF'
    import json, urllib.request
    body = json.dumps({
        "state": "The car wash is only 100 meters away from my house.",
        "model": "jev-1.13-free",
        "questions": {"q": {"type": "choice",
            "instructions": "Should I drive or walk to the car wash?",
            "criteria": {"drive a car": None, "walk": None}}}
    }).encode()
    req = urllib.request.Request("https://opencode.ai/zen/v1/systemone", data=body,
        headers={"Content-Type": "application/json", "User-Agent": "opencode/1.18.31"})
    with urllib.request.urlopen(req, timeout=60) as r:
        print(json.dumps(json.load(r)["answers"]["q"], indent=2))
    EOF
    {
      "type": "choice",
      "choice": "walk",
      "confidence": 0.66,
      "probabilities": {
        "walk": 0.83,
        "drive a car": 0.17
      }
    }

Is this as good as Laya 3? Unfortunately, it's production was moved from Bremen, Germany, to China, and it is not good anymore, in my opinion.

Loved the idea, but I don’t think it would be able to handle real-world data effectively. There are a lot of nuances that actually require a reasoning model to think through, connect the dots, and make sense of the broader context.

Pangram believes this text was authored with an LLM:

https://www.salahadawi.com/hacker-news-ai-detector/49765348

Distribution > Creation

from https://huggingface.co/convaiinnovations/laya > The policy reports a distribution; exploration adds zero-mean Gaussian noise to the logits; the reward is a strictly proper scoring rule (log + spherical, plus ranked probability score for ordinal questions). Expected reward is maximised only by reporting honest probabilities.

There are many, many, open-source versions of Jev, including three distinct projects sharing the name “openjev”

If you’re interested in the basic trick most are using (which is probably also what Jev does) then it’s here: https://sgnt.ai/p/jev/

This sounds cool but it looks like it requires a GPU that I don't have. Is there an API to try it out?

Why do we ("society") need the "frontier" companies at all? Their business goal has settled on trying to CONFUSE the shit out of us so that we don't understand the big pictures about various aspects of AI.

THANK YOU, Nandakishor Mukkunnoth, for putting in the work to help to clarify this stuff!

You are like a firefighter compared to their fire-insurance racket.