쉐도잉 연습: An ex-OpenAI researcher just deleted language from the LLM... - 영상으로 영어 말하기 배우기

레슨 만드는 중...
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Last week, everything about AI changed forever.
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I realize everything about AI changes forever almost every week, but this time, AI changed forever more than usual.
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Because one of the OpenAI researchers behind the instruction following work
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that eventually became ChatGPT just released the next big frontier model after two years in stealth development.
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A model that can't talk, a model that can't write code, a model that can't write your college essays, and a model that will never tell you you're absolutely right.
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Its name is Jev.
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My name is Jev.
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And this is a huge deal because large language models have one fatal flaw.
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They won't shut the hell up.
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You give Fable or Astra a simple instruction like return true or false, and it'll discover a third option after thinking for 4 ,000 tokens and then charge your credit card 11 cents.
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Jev fixed this problem with a radical solution.
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It deleted language from the large language model, and the result is a new type of classifier that's 200 times faster,
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400 times cheaper, with free output tokens and zero hallucinations.
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This sounds too good to be true, so in today's video, we'll take a look at Jev's code, it's TrustMeBroBenchmarks, and the dude who says he built an open -source Jev over a year ago.
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It is September 21st, 2026, and you're watching the Code Report.
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The big AI duopoly is literally shaking right now, because Jev is a cheaper, faster way to solve basically any AI problem that requires a quick gut -instinct decision.
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It's afraid.
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But the first thing you need to know is that Jev was created by an ex -open AI researcher, Diogo Almeida, and his company TypeSafe AI, which just raised $40 million.
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But the company name is the first clue to what JEV really is.
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Like a regular large language model, you send it a question and some context, like a bunch of unstructured text.
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However, it differs because it behaves more like a TypeSafe programming language, like TypeScript.
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The question you send to the model is a strongly typed question that must return a specific shape.
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One of three shapes actually, a choice, a score, and a null, which is basically just a yes or no. Its schema matching is guaranteed,
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and a type error would be mathematically impossible to produce.
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They call Jev a System 1 model, which is a name that comes from Daniel Kahneman's Thinking Fast and Slow.
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A System 1 model is fast and goes from gut instinct, while a System 2 model is slow and deliberate,
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like these old antique reasoning models like GPT -6 and Claude Fable that burn 40 ,000 tokens to name a variable,
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but the difference is huge for app developers like myself who want to integrate fast, cheap AI into their applications.
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Like on Horse Tinder, we recently had an issue of some donkeys trying to use the app, which is strictly forbidden in the terms of service.
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Thanks to Jev, we implemented an AI moderation step that will instaban any account that is not a horse, which is accomplished by returning a null response to is this a horse.
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Not only is it extremely fast if we are to believe these TMBBs, but more importantly, it's off the charts cheap, like 440 times cheaper than one of the big brand models.
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In fact, it's so fast and cheap that you can even use it for real -time applications, like developers are already using it to implement NPC behavior in video games.
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And this guy even used it to build the world's first real -time AI calculator.
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But just because the output is type safe, that doesn't mean it's always correct.
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And it's not even deterministic, like you could send it the exact same question and the exact same context and get different results, just like any regular large language model.
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But to get an idea of the response quality, it returns something called the calibrated confidence number.
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Chap models are trained to police human raiders, and humans love confidence, which is how we got models that are wrong with the confidence of Kanye.
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Jeb gained its confidence through a technique called RLCD, or reinforcement learning for calibrated decisions.
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This means every response provides a confidence value, like say 60%, which means 60 % of the time, it's right every time.
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But the big question is how does Jeb actually work?
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Well, nobody knows for sure, because the CEO says the architecture is staying close to the chest, with a paper possibly coming in the future, maybe.
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But Jev also has some doubters.
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Some people say it's no different than zero -shot classifiers of the past, but the company gives no credit to the original pioneers of this technique,
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like Jin Yang, who were building zero -shot classifiers over a decade ago.
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In addition, this guy claims his paper he released a year ago is the exact same thing as Jev, and another developer already built OpenJev,
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which reproduces the entire interface by reading option probabilities off a frozen Quinn 4B model in a single forward pass.
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It requires no new training and can run on a 3090.
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And there's even a web GPU demo you can run in your browser right now.
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It's an awesome time to be a developer, which is why you need to check out MUX, the sponsor of today's video.
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Their highly customizable API is by far the easiest way to
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add video features to your application without getting jump scared by FFmpeg.
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We've used it for years to handle all the hosting and streaming for our courses, but it does a lot more than just infrastructure.
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When you upload a video to MUX, you automatically get transcripts, storyboards, thumbnails, and clips, along with structured data about what's actually in the video.
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That powers MUX robots, which is their AI -hosted workflows that can translate your audio into other languages.
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moderate content, and lots more without you needing to host a model or maintain a pipeline.
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You can automate all this with directives where you define a workflow once and it runs on every new upload, and you only pay for the jobs that actually run.
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Perplexity, Patreon, and many other prestigious companies all trust MUX, and their free plan includes 10 videos and 100 ,000 delivery minutes per month with no credit card required.
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And you can get an extra $50 credit at the link below.
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This has been The Code Report, thanks for watching, and I will see you in the next one.

이 비디오로 배울 실용적인 영어 회화 기술

이 비디오에서는 빠른 속도로 진행되는 자연스러운 영어 발화를 듣고 따라할 수 있어 영어 쉐도잉 실력이 크게 향상됩니다. 특히, "AI changed forever more than usual"와 같은 긴 문장에서의 리듬과 강세를 파악하여 IELTS 스피킹에서 요구되는 유창성과 자연스러움을 연습할 수 있습니다. 또한, "TrustMeBroBenchmarks"와 같은 신조어나 전문 용어를 포함한 대화를 이해함으로써 실제 상황에서의 영어 이해력과 반응 속도를 키울 수 있습니다.

듣고 주목해야 할 발음 특징

  • 연결 발음(Linking): "changed forever"에서 "d"와 "f"가 연결되어 [tʃeɪnd fərˈɛvər]처럼 발음됩니다. 이를 통해 자연스러운 영어 발화의 흐름을 체감할 수 있습니다.
  • 축약 발음(Reductions): "it'll"은 [ɪtl]로, "can't"는 [kænt]가 아닌 [kɑːnt]로 발음되어 구어체의 특징을 확인할 수 있습니다.
  • 강세(Stress): "radical solution"에서 "radical"은 첫 번째 음절에, "solution"은 두 번째 음절에 강세가 가해져 의미를 강조합니다. 이를 통해 문장의 중심을 파악하는 기술을 연습할 수 있습니다.

원어민처럼 말하는 방법: 리듬과 강세

원어민처럼 말하기 위해선 shadowspeak 기법을 사용하여 비디오의 발화를 정확히 따라하는 것이 중요합니다. 특히, "the big AI duopoly is literally shaking right now"와 같은 문장에서 "literally"를 강조하여 화자의 감정과 강조점을 전달하세요. 또한, "200 times faster, 400 times cheaper"와 같은 수치 표현에서는 빠른 속도로 발음하면서도 각 단어의 구분을 명확히 하여 청취자가 쉽게 이해할 수 있도록 하세요. shadowspeaks를 통해 반복 연습하면 자연스러운 리듬과 강세를 익힐 수 있으며, 영어 회화 연습에서 큰 진전을 볼 수 있습니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.