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Introducing System One Models and Jev

typesafe.ai

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Re: Introducing System One Models and Jev

#381

Post by apexalpha »

qingcharles wrote: post #321
wg0 wrote: post #319

Earlier quoted context omitted.

That's good. Side note - just like most people don't need an intelligent personal assistant to manage and respond their emails and book their flights, most people also don't need smart homes. Century old toggle switches are more than enough in a 3 room apartment or 5 room house unless you have a mention.

My primary beef with smart home (having tried it) is that every person that visits your home ends up confused about some element of it. A light switch that goes up and down is universally understood.

My smarthome has regular switches and wifi.

There's no reason to not do both.

Also a quick NFC sticker in each room taking you to a small HTML site containing settings (temp, ventilation, lights, shutters, setting a alarm by the lights) has been golden.

No one wants to: download Shelly app + AC app + look for ventilation IR controller + figure out how casting works for the TV + figure out how to use the Shelly app to turn lights into an alarm. It's too much friction for little gain. But a quick tap? Great.

But tapping your phone on a NFC sticker bringing all those controls together per room in stead of per category (all lights in Shelly app. Person in room #1 has no interests in the lights in room #4 at the same time.).

IF you tap it while not on Wifi yet it just tells you to connect to Wifi. :-)

One "all house" sticker next to the front door allows any last person leaving or first person entering to put the entire house in active / idle mode.

Works wonders. And as soon as local AI is quick enough the stickers will be a microphone!

Re: Introducing System One Models and Jev

#384

Post by vintermann »

copperx wrote: post #322
wg0 wrote: post #317

Can I put it as Air Traffic Controller? With similar error rates as humans? That would be the litmus test. "Does not hallucinate" is not the same as "is never wrong". So the ATC test could be the benchmark.

Not hallucinating is easy when you don't produce strings.

Hallucinating as we use the word really only applies to generative AI. Non generative AIs can't hallucinate, they can just be wrong.

Re: Introducing System One Models and Jev

#385

Post by vintermann »

zenlikethat wrote: post #161
dinobones wrote: post #131

This is a good product but the naming/branding is pretty unfortunate. Typesafe.AI sounds like some typescript/structured output type of tool… What even is “system one” ? IMO the product/tech is really there, just needs better communication.

The model can't reason comprehensively (e.g., like Sol XHigh would to solve a complicated problem), but it's designed to be able to answer anything a human reasonably could quickly and intuitively, i.e., system one thinking: https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow

I wonder how well it can play chess, or go.

Re: Introducing System One Models and Jev

#386

Post by hangrymoon01 »

edot wrote: post #235

Very cool! Can you explain when I would use this vs. training a standard ML model on my data? Suppose I had a fraud dataset with features like customer ID, amount, merchant, online or in-person, etc. - I can't imagine that a general model like Jev would predict this more accurately or cheaply than even a basic XGBoost model trained on my dataset (one that I could build in a few minutes by asking Codex to build it). W…

you will need to collect data for every decision/usecase and then train a model. But this can be used for different use cases with just a prompt.

Founders response to a similar question on X: https://x.com/CompleteSkeptic/status/2100067328620896408?s=2...

pasting it here: zero-shot + general == programmable

I would assume any extreme scale narrow task could then be fine-tuned for, but we'll see - I suspect putting it all in shared cognitive core has bit maintainability/generalization benefits

Re: Introducing System One Models and Jev

#387

Post by hdjrudni »

jacobgold wrote: post #81

First, congrats to the team on launching something genuinely interesting and new. Seems like a more accurate title would be "Jev: Trading general purpose generation for fast typed inference" or something like that. This is interesting, but the speed comparison seems misleading? A generative model that can output code in a Turing-complete language can do anything a computer can do. Jev can only generate structured out…

> Assuming there's no funny business, the Doom demo is cool.

The Doom demo seems very funny business. They're not feeding it video, they're feeding it a text description of what's going on in the game. It's not reading pixel data.

I think LLMs would play a lot better with that input too but Jev does seem to have a huge speed advantage; I don't know if the other models could do that in real-time.

Re: Introducing System One Models and Jev

#388

Post by mary776 »

flowerboy-t wrote: post #367

do you all see the use cases being similar to what you might use Fastino's Gliner models for? i see similar differentiation from general purpose LLMs in the sense that they can take natural-language input and return outputs adherent to a user-defined schema. https://fastino.ai/blog/gliner2-5-span-free-information-extr... im thinking about how well Jev could be used to replace a current LLM-as-Judge evaluation workflo…

definitely seems like a modified version of GLiNER2 or 2.5: - encoder-based (no text generation) - multiple tasks in a single forward pass - deterministic outputs - constraint-based classification

Re: Introducing System One Models and Jev

#389

Post by hdjrudni »

baq wrote: post #363
eru wrote: post #335

Earlier quoted context omitted.

That seems like a weird standard. I would be happy enough with: only produces what it can verify with sources. If you eg try to remember a court case (ie produce the reference via LLM token generation only), it's easy enough to check with your data whether it really exists. Similar for following links and other references. If your data or sources are wrong, obviously your report about them will be wrong. But I wouldn…

There isn’t a single human in this world and hasn’t ever been that meets your happy-enough standard. Make of it what you will.

Why is that at all relevant?

Humans are known to hallucinate a lot. Ask 10 different witnesses at a crime scene what they saw and they'll all report different things.

A good, non-hallucinating LLM would only report things for which it has evidence. It would consult the facts every single time.

It's a pain in the butt for humans to fact-check everything but LLMs can quickly look up all kinds of stuff. That's what makes them useful.

Re: Introducing System One Models and Jev

#390

Post by [deleted] »

nullbio wrote: post #354
latteren wrote: post #341

Looking at the example Jev use cases, it almost feels like Jev's incredible cost/task can make it competitive as a generalized "poor man's ranking" algorithm that can be useful for lean startups or any fast paced development org. I need to rank 1000 articles and pick the 5 most relevant for the user? Jev. I need to audit and strip out content because my user is affected by regional privacy laws (without hallucinating…

More like: I need to ...? -> Open-weight model. I'm sure someones working on this as we speak using an open-weight LLM base (Qwen or something would be a perfect fit). This sort of task is a perfect fit for a very small model capable of semantic parsing. You can get away with a LOT less parameters without all the autoregressive generation and long-context reasoning.

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