쉐도잉 연습: What Is Jev? The AI Model That Doesn't Generate Text - 영상으로 영어 말하기 배우기

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all right i want to talk about jev jev's a new ai model from a startup called type safe

and it's a bit unusual because it's an ai model

that doesn't actually write any text it actually answers questions by picking from a list

and then giving a probability for each one of his answers

now type safe calls this a new type of ai model it's a system one model

which is a name borrowed from Daniel Kahneman in his book one of my favorites thinking fast

and slow Kahneman describes two modes of thinking human thinking

that is and the first of those modes is called system one thinking

and system one thinking is fast and automatic

so what's two times two you instantly know the answer is

four without having to really work it out in your head that's system one thinking the other type

of thinking is classified as system two thinking and that's slow

and deliberate so

if i ask you what's 17 times 24 well you're probably going to need to work through

that one step by step to figure it out

or just look at the answer on your teleprompter

which is uh 408 there we go got it now most

of the ai models we use are built to write stuff

and a chatbot produces its answers one token at a time

in a ford pass now a reasoning model goes a bit further than

that and it actually writes out its chain of thought as we're going through this

so you can actually kind of see what it was thinking

which is basically as close to a system two model as ai gets

but but a lot of decisions inside software are not really system two they're just kind of quick judgment calls

so let me give you an example of that let's say

that we have got a email for customer support and

that email says that I was charged twice this month please fix it all right

so how do we respond to this email well the software

that's handling this email probably has to look at three types

of question one of the questions is is this a refund request yes

or no another question might be

which team needs to process this email maybe the billing team

or maybe it's the technical support team or maybe it's the sales team

and then also how urgent is this request how quickly do we need to respond

now a person just skimming an email like that they would instantly know

that refund yeah it's yes this is a refund request that's kind of a system one bit of work

and it's that kind of work that jev is built for

so a common way to automate a support email problem like

this today using an llm would be to send this email to the llm

and ask for the answers back the answers to these things in a structured format

so maybe we would go through this large language model output some tokens

and these tokens would represent a format maybe in json now

that mostly works

but it doesn't come back with a reliable sense of how

sure the model is how confident is a large language model

that it got the answer to these questions right well we could ask the model

but whatever it responds with wouldn't necessarily actually match the real

probabilities the model used to come up with this answer

but jev well jev does come up with probabilities so

if we send these questions to JEV we're going to get a very different looking response

so it takes in two things it takes in what's called state

which is the data the decision is about so

that might include our support email

that might include some recent customer charges as well all of

that is input into the JEV model

and it also takes in each of these questions as well into the model as input

and our three questions are actually three different types of questions so refund that is a yes

or no question

which type safe calls a null a team this team question it really comes down to a choice as to

which team is this so picking one option from a list

and then urgent

that really comes down to a score on a scale from

low to critical now all three of these questions go in in a single request

and whereas an llm would write its json answer out one token at a time jeff doesn't generate any text

so all three answers they all come back at once

and that's a big part of why this is so much faster

and cheaper than an llm on these types of questions now

we provide questions as input what is coming back i said

jeff doesn't give you text what it gives you coming back our probabilities as the output

so for refund it's going to be a single number let's

say it's going to come back with 0 .9 meaning there is a 90 chance that yes this is a

refund request for the team it's a probability for each one of the options that were part of this choice.

So for example, the billing choice, which is one of the three, may come back with a probability score of 0 .85.

And by the way, the answer to choice can only be the options that we give it.

So for team, it's always going to be billing or technical or sales.

And then urgency, well, that's going to come back if we kind of put this on a scale, pretty high to critical here so this is quite important.

Now, the model can still pick the wrong options or score any of these questions incorrectly,

of course, but the probability tells us how likely that is.

So how do you train a model to give accurate probabilities like this?

Well, let me go back to training traditional LLMs for a moment.

Most language models, they start with a stage known as pre -training.

So you pre -train a model first.

That means the model reads a huge amount of text and it learns to predict the next token.

Now that creates a model that knows a lot of stuff but it isn't much use to talk to yet.

So if we want to use this as a chatbot then we need to go to a second stage, which is called post -training.

Now, a lot of post -training uses a technique called reinforcement learning.

The model produces an answer, something scores that answer, and the model kind of gets nudged towards the answers that score well.

so the question is what's doing the scoring well one way to do that is with a technique called rl

hf reinforcement learning from human feedback

so people are doing the scoring there human raters look at two responses from the model

and they pick the one they like best

and those choices train a reward model and then

that chatbot is going to be trained to give answers

that the reward model scores highly people tend to like answers

that sound confident so an unfortunate side effect of rlhf is

that a model can learn to sound like really sure of itself even

when it's wrong

but that's just one form of reinforcement learning another form is r l v r that's reinforcement learning with verifiable rewards.

And the scoring here is automatic.

100

Did the model get the right answer to the math problem?

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Does the code it wrote pass its unit tests?

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Those sort of things.

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And that's a big reason that reasoning models today have got so good at math and coding.

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But the reward here only checks whether the final answer was right.

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It doesn't reward the model for knowing how sure it should be.

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And those models spend a

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while working through their chain of thought before they come up with an answer

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and that can make them a little bit slow and expensive to run.

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Now JEV is trained with a different form of reinforcement learning.

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It's called RLCD, reinforcement learning for calibrated decisions.

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Now TypeSafe haven't published much about JEV's architecture

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so i'll stick to what they have said well we know that jeff gives a probability

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for each option and it's rewarded when those probabilities turn out to be right

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so that's what the C is all about it's saying the model is calibrate it.

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And a model that's calibrated kind of basically looks like this.

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So along the bottom axis here if we map probability

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and then along the side axis here we can map whether the model is correct.

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So a well calibrated model is going to have kind of a diagonal line much like this.

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So when we say that there is a 80 probability that 80 probability is going to be correct about 80

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of the time that's calibration so back to the the refund question

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that was the first of the three questions we posed to jeff about

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that support email so

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if we have probabilities we can use them to inform code using a threshold like this

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so we've got a probability threshold here one is certainty zero is definitely not kind of thing

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so if a probability score is above let's say 0 .9

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so maybe our score comes back here somewhere

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that means we can treat this support email as a legit refund request

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and we can send that straight to the refund queue for processing

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if however the score comes back anywhere kind of around between here and let's say 0 .1

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well there's some uncertainty here so the code

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that received a number like this would know to actually get a human involved

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so now we need to bring in a person to actually check

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that because we're not sure and

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if a score comes in below 0 .1 well i think we can say that's not a refund request

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so keep calm and carry on we don't need to do anything with that one because the numbers are calibrated,

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the threshold also tells us roughly how often the automatic path will be wrong.

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So the more a mistake is going to cost, the higher we would set the threshold.

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Now this works for more than support emails of course.

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Jeff can act as a guardrail

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that can be used around all sorts of things like around a chat bot that's checking messages going in

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and out for things like well jailbreak attempts

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but there are limits jev only takes text in as of today it's not very good at math

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or even counting so you're going to want to leave

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that stuff to something else

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and like any ai model it can be tricked by instructions hidden in the data it's reading

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so is it time to throw away large language models

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and replace them completely with system one models no hardly

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but the two can work together so

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if you think about a workflow maybe we have jev in the workflow

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that can make quick calls like sorting

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that customer support email to see whether it's a refund or not

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and then in the workflow we could go to a large

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language model to do the slower work work like replying to the customer

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and then we could go back to use Jev to classify any response

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that came back from the customer

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which is pretty much how Danny Kahneman said we work too

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most of what we do runs on those fast automatic system one processes

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so that's these two stages here and then the slow and careful system two, which is the large language model in this case,

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that only kicks in when something needs real thought.

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Oh, and I wanted to share a thing about the name of Jev as well.

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Jev is named after William Stanley Jevons, who was an economist who pointed out in 1865

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that as steam engines got more efficient britain actually used more coal that's known as jevons paradox

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so perhaps the same thing will happen with system one models

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when a judgment call is super fast

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and super cheap like with jev well it can go into places where an llm would be too slow

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or too expensive today like in every row of a database or on every line of a log file. Who knows?

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All right, that's Jev.

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Where would a System 1 model fit into the software that you work on?

169

Let me know in the comments.

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단어	발음	뜻
probability 명사	/ˌpɹɑ.bəˈbɪl.ə.ti/	확률
refund 명사	/ɹɪˈfʌnd/	환불금, 환불
reward 명사	/ɹɪˈwɔɹd/	상, 보수
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threshold 명사	/ˈθɹɛʃ(h)oʊld/	문턱, 문지방
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format 명사	/ˈfɔːɹ.mæt/	포맷
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urgent 형용사	/ˈɜː.d͡ʒənt/	긴급한
axis 명사	/ˈæksɪs/	축
architecture 명사	/ˈɑː.kɪˌtɛk.t͡ʃə/	건축, 건축학
efficient 형용사	/ɪˈfɪʃənt/	효율적인
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