A lot of AI stuff is like that, actually. GPT 2 and 3 were mainly curiosities that would get articles on tech news sites but didn’t attract much attention from the general public. But after RLHF the UX improved dramatically and ChatGPT became mainstream. A lot of MLEs underestimate how much UX matters, and the same is true for Jev.

> how do these models calculate probabilities?

The short answer is: more and better data.

The long answer is: probably the same way any other classifier does. Take your ground truth probabilities and your predicted probabilities. The gap between those is called cross entropy, which is used to push the model towards the correct probabilities.

Dont under estimate the power of branding/positioning/marketing. For more than a year I have been using a RL'd Qwen fine tune optimised for just taking a decision and outputting the decision number, initially I didnt have much training data so after a few manual examples I asked deepseek to make them for me and had codex train it. I compared it to jev when it first came out and it was mostly neck and neck for my use case. I can't have been the only one in fact, I'm sure there must have been other people who probably even open sourced, but I couldn't find anything. I think the question going forward will be how does one keep abreast of interesting stuff happening, now that its de-coupled from direct human labour so de-bottlenecked on production capacity - just bound by how residual time/attention one has (Terence Tao was talking about this exact predicament in the latest tyler cowen podcast).

The natural path of things, I suppose, is those that have a soapbox/distribution will just get larger...

I think its more like how LLMs caught everyone by surprise. A reasonable person would not think such a simple system would have been this effective, and yet here we are. Distilling that system down even further into a decision model again does not seem like it should work. But it seems to.

The whole system is foreign to us, and makes us rethink what "intelligence" actually is.

That's how the original Transformer architecture (LaMDA) should have been released back in the day, but Google thought it would cannibalize their search revenue.

Basically there is a whole host of tarpit usage of LLMs in application backends today. This is simply another case of LLMs enabling people to build what they cannot productionize or maintain.

Today developers without any AI/ML skillset can prototype a fancy new feature with LLMs, what used to require collecting and curating data to train a pipeline of ML models then serving them in production.

The problem comes when it's time to take the new feature into production. That $20 cost with prototype/beta workload is now $30k per month in LLM costs, and those hallucinations that never happened on their 200 test samples is happening 600 times per day.

They are then stuck, because they lack the knowledge and budget to build and run the production ML pipeline properly, the infra stack necessary to handle production workload. Management also doesn't understand why they have to invest in rebuilding something that "seems to work" with "outdated methods".

These system one or decision models are a step up from LLMs when it comes to this usage. Problem is that they are still difficult to work with (limited in expression power) for many application logic. Despite the initial popularity, I hear about a lot of frictions refactoring LLM based backend with them, and multiple cases of production rollbacks to LLMs already.

I think system one models is a sensible paradigm, but there is a lot more work needed above the model layer to make this work.

That also makes me wonder: what other things are achieved by now and are up for grabs, and that will seem obvious in retrospect, but we don't notice it yet?

Roughly speaking, has AGI actually been achieved two years ago and we go around don't noticing that?

> Which makes me wonder: how do these models calculate probabilities?

By unprincipled methods. To calibrate confidence in an answer you need a task specific calibration set. You can't pre-calibrate for tasks unknown ahead of time, which is the case for most uses of decision models.

We were doing zero and few-shot classification with LLMs since before they could reliably complete whole sentences. It’s an easier task than generating coherent text. The model needed to be tuned for classification (not generation) and for the domain, but it would often succeed at new classification tasks with a simple description.

I guess given the huge advances in LLM since then, these models probably achieve much better accuracy on a wide range of domains with lower effort compared to what we were doing, which makes it compelling as a product. And yeah, the models probably were already good enough a few years ago too. Other factors include how easy it is to release products at high speed now, and the desire in the industry to reduce token spend.

You can do it with LLMs by looking at the logprobs of ('yes', 'Yes', 'YES'...) vs. ('no'...). This works for bool or whatever they call it in jev space nool I think?

You can do it manually or use ready made libs. I know there's one in python on top of vllm that can do something like this.

> I'm sort of surprised it took Jev for everyone to realize "oh yeah, this is a useful thing that we should have in our product portfolio".

Because it's especially parsimonious with token use.

Why introduce a feature that would cut token use by 1000x?

OpenAI was already working on a Decisions API, and Jev launched theirs quickly enough to have 1 week in the spotlight before OpenAI put theirs in beta (which works great btw).