NeumannGod

What is the use of this arbitrary finding? There is nothing actionable from this only that I can think of. Happy to be enlightened here.

focus and expertise. apple lacks both when it comes to AI. maybe it is strategic, maybe it is stupid hubris - we will soon figure out.

in any case, they're bound to get disrupted

Wrong thing to index on. Always bet on the slope, not on points in the slope. Things will only get better and better and factor in that downward slope thought while taking actions and pick ones that are favorable. The folks who focus on a point in time will be left behind, period.

Such a click-baity name. Open challenge to anyone to use this framework and reverse engineer the internal APIs iPhone uses to talk to Siri or shortcuts or app Intents.

Whatever helps reduce RAM costs bro!

Why show an intelligent UI within a chat interface. That’s pathetically lame. Better to go away from chat interfaces to rich visual interfaces. Doesn’t sound intelligent at all to me

Is there an open standard or something for people generating images to encode them or is the standard private and only shared with frontier model builders? That's a shame if latter. But pretty cool standardizing technology.

Pretty interesting but I wonder apart from sovereign use-cases in EU, who uses Mistral and for what? How good are they compared to frontier open models like Kimi?

I agree sovereign markets themselves can be huge but just curious.

A low-latency calibrated classifier is very valuable. Speed is not at all a blocker. You could've changed the LM-head of an existing, say 3B parameter or even 30B parameter LLM or do speculation to easily get Jev like outputs - achieving speed is not at all a problem.

It is about getting calibrated outcomes. Jev is able to provide that, which is a major unlock

This is a false comparison. What Jev does is fundamentally different from what LLMs are doing as a judge.

For a quick primer, Jev is able to provide confidence scores on its classification, i.e. it is able to calibrate how well it is able to predict. Being able to predict in a distribution is different from being able to calibrate confidence of the predictions which should happen from the question or domain distribution from which the decisions are predicted - being able to do that is tough and is not same as using LLMs logit probabilities which are predictions in the vocab space. Though both are loosely correlated and might converge as LLMs keep getting better, the former is a much stronger decision-making signal than the latter. Jev not beating LLM-as-a-judge might be due to various other reasons such as world knowledge etc, but Jev as a concept will always provide more reliable decisions / outputs than LLM-as-a-judge giving a scalar score.

This was about a previous conference. The current article seems to be new information and it's rather unsettling, if true