컴퓨팅 업계에서 AI 에이전트의 부상

### 엔터프라이즈 AI 시대의 여명: 도구를 에이전트로 전환하기
* 🤖 젠슨 웡은 **1조 달러 규모의 도구 산업에서 '에이전트'가 도구를 활용하여 효과적으로 협업**하고 문제를 해결하는 기술 중심의 산업으로 변화하고 있다고 강조합니다.
* 🚀 에이전트는 업무를 이해하는 것뿐만 아니라 **추론하고 협력하여 문제 해결의 새로운 패러다임**을 만들 수 있기 때문에 잠재력은 엄청납니다.
* 📈 비지도 학습의 획기적인 발전으로 데이터 라벨링에서 인간의 한계를 뛰어넘어 **AI 역량이 기하급수적으로 성장**할 수 있었습니다.
* 💻 현재 컴퓨터 기술의 발전은 **무어의 법칙을 능가**하고 있으며, **CPU, GPU, 머신러닝 소프트웨어의 발전**으로 피드백 루프가 생겨나면서 발전을 가속화하고 있습니다.
* 🔒 안전과 윤리적 고려 사항이 가장 중요하며, **AI 대응을 미세 조정**하고 기술이 핵심 가치와 가이드라인을 준수하도록 하는 데 중점을 두고 있습니다.
* 🌱 웡은 더 폭넓은 이해와 접근성을 위해 **AI를 이해하기 쉽게 설명하는 것**이 중요하며, 개인이 직접 기술에 참여할 수 있도록 장려해야 한다고 강조합니다.
* 💡 컴퓨팅을 재창조하려는 야망은 오랜 과제를 해결한다는 흥분으로 이어져 **엔터프라이즈 AI의 혁신적 시대**가 시작되었습니다.
* 🌍 새로운 단계에 접어들면서 사회의 자동화와 혁신의 잠재력은 **전례가 없는 수준**이며, 웡은 앞으로의 여정에 대한 열정을 표명합니다.

젠슨 웡은 AI 에이전트가 산업 전반의 생산성과 협업을 획기적으로 향상시키는 혁명적인 시대를 상상합니다.

please welcome Jensen Wong. Jensen, come on out to have you. Our industry is, you know, call it about a trillion dollars, and it's an industry of tools, you know, computers, software, their industry of tools. For the very first time, this is going to be an industry of skills, and you capture that phrase, and you call it agents, but it's going to be for the very first time agents sitting on top of tools, agents using tools, and the opportunity for agents is gigantic, as we know. You know, we now know. I'm hoping so, by the way. Yeah, it sounds insane, but here's the amazing thing. We're going to have agents that obviously understand the subtleties of the things that we ask it to do, but it can also use tools, and it can reason, and it can reason with each other and collaborate with each other. And you know, going to, we're going to give a problem to

agent force and uh uh agents are going to go find other agents uh that that can help achieve this Mission. uh They'll work together, assemble together, and work together and solve this problem. And and so I I think the the the Breakthrough uh for for me was was at a moment when when um we realized that unsupervised learning was going to be possible because the ability for humans to to uh be able to L label data at scale. uh We would become the limit limiters of these digital digital AI to to uh uh expand their capabilities. And uh when when unsupervised learning came along which allowed us to use language models to create language models which codify human prior knowledge using that to now learn multimodality multimodal uh uh data. uh From that point forward the scale was going to be exponential. Now the you know for for everybody here this is this is an extraordinary time because uh in no time in history has computer technology not only uh uh moved faster than Mo's law. I mean we're Mo's law for

example over the course of a decade would be about 100x. We are probably advancing at somewhere near 100. That was our other neighbor in Hawaii, Gord Moore. Oh, is that right? Great person, great leader, great person. And so we're at a stage now, we're in an era now where we're moving way faster than Moore's law, and arguably easily Moore's law squared. And the reason for that, of course, is at every single layer. Computers went from CPUs to GPUs, from human-engineered software to machine learning software, and now this feedback loop that allows us to create new AIs. And these new AIs are helping us create new computer systems, and these new computer systems are advancing at such incredible rates, which allows us to create even better AI. That feedback, that flywheel is really flying now. And so I think the progress that you're going to have with

a agents over the next year or two is going to be spectacular and surprising. The challenges that we have in front of us are many and worthy to be tackled right away. Um, of course, some of the most inspiring advances has to do with fine-tuning supervised um uh guard and guard railing and um uh and uh um all of the work that's surrounds safety um everything from using AI to curate the data to create safe curriculum to teach the AI which then fine-tunes the AI on particular skills to grounded on values. Then then all of this wonderful AI technology to guardrail it um and guard railing and then reflection uh using using chain of thought to reflect on the quality of the answers that it's producing. You know, it's no longer AI this one shot producing information. It's now reasoning about is this is the the the uh the answer I'm generating uh sufficiently safe and proper and is it the best possible answer I could be I could be

providing its reasoning with itself and it's uh it's reflecting on the answers before it answers and so I I think the progress that we're making in safety is phenomenal isn't it so important that people can actually understand uh this is what I call they need to get their hands in the soil they need to understand the basic ideas to make this possible and that's what I hope you know happen with the thousands of people here that they can get in the code while they're here yeah to really understand so when they're going back to their offices you know that exactly what you're saying that we've demystified the AI that you know they don't have to worry about how they're going to DIY their AI that they can somehow put all the hard work that your team has done right into some very practical use because this leap of faith that you've had this strategic motion of your company has enabled so much of this now how do we put it in the hands of

everybody to like just get to work in building an agent should not be some computer science fair project. It should be something that we can easily do because it's going to be a lot more like onboarding employees than writing software. So, I think the ambition of wanting to reinvent computing to create what we now know is such a great endeavor. So, somewhere between being inspired by all the stimulus, the incredible challenge ahead of us, you know, it's kept us propelled, inspired, and fired up for a long, long time. And now here we are. We now have the instruments, the tools, this capability called artificial intelligence that can solve all of those other problems that we've, you know, been excited about ever since we were kids. And so, that's pretty exciting times. Yeah, the summation of that whole thing, right? Because now that I know, like, you were there when the TR80 model 1 was there, and we were at Radio Shack, and we're working on that, you know, BASIC and

then we're moving into the 6502 computers, and the Commodore, and the Ataris, and the Apples, and all of that. And then all of a sudden, yeah, we're under these mini computers and mainframes. And now, all of a sudden, we're in this next-generation systems that can do things that we could have never imagined. And that's right. If somebody had asked us when we were in high school to start programming on those things, we'd be like, Whoa, yeah, unbelievable times. And so, I think for all of the industry, this is what we're all fired up about because we're now seeing it, and we get the benefit of being inside the industry, looking at the early indications of all this work. And I'm excited to be here today, helping to celebrate the beginning of the Enterprise AI ERA. This is a big deal. Don't forget, you know, we say Enterprise is a name of an industry or, you know, but the fact of the matter is this is how society is

built. This is how industries are built, and we're going to bring a level of automation capability that the world's never seen. And I love that you codified it as agents, gave it a theme song, um.

Watch Nvidia’s Jensen Huang Talk AI Agents at Dreamforce in 8 Minutes

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기술 산업에서 도구에서 에이전트로의 전환

젠슨 웡은 기술 산업이 도구 중심에서 에이전트 중심으로 전환되고 있으며, 에이전트가 도구를 사용하고 협업하여 문제를 해결하고 있다고 설명합니다.

이러한 에이전트들은 공동의 목표를 달성하기 위해 함께 추론하고 협력할 수 있기 때문에 엄청난 기회를 얻을 수 있습니다.

비지도 학습의 획기적인 발전과 언어 모델의 사용은 디지털 AI 기능의 확장을 가속화하여 기하급수적인 성장으로 이어졌습니다.

현재 AI 개발은 새로운 AI와 컴퓨터 시스템 간의 피드백 루프에 힘입어 무어의 법칙을 뛰어넘는 빠른 속도로 발전하고 있습니다.

머신 러닝, 미세 조정 및 안전 조치의 사용은 AI의 성장을 형성하고 가치에 부합하도록 보장하는 데 핵심적인 역할을 합니다.

향후 몇 년 동안 AI 기술은 놀랍고 놀라운 발전을 이룰 것으로 예상됩니다.

안전과 발전을 위한 AI 이해의 중요성

연사는 AI의 구현을 가능하게 하기 위해 사람들이 AI의 기본 개념을 이해해야 한다고 강조합니다.

또한 AI를 이해하기 쉽게 설명하여 누구나 실질적으로 사용할 수 있도록 하려는 회사의 전략적 움직임을 강조합니다.

연사는 AI 에이전트 구축이 직원을 온보딩하는 것만큼 쉬워야 한다고 믿으며, 오랜 문제를 해결할 수 있는 AI의 잠재력에 대해 기대감을 표합니다.

컴퓨팅의 흥미로운 진화와 엔터프라이즈 AI의 부상

컴퓨팅의 여정은 6502 컴퓨터에서 미니 컴퓨터와 메인프레임으로 발전하여 상상할 수 없는 기능을 갖춘 차세대 시스템으로 이어졌습니다.

연사는 이 변혁의 시기에 업계의 일원이 된 것에 대한 흥분을 표현하고 엔터프라이즈 AI 시대의 시작을 축하합니다.

전례 없는 수준의 자동화를 약속하며 사회와 산업의 판도를 바꿀 엔터프라이즈 AI의 영향력을 강조합니다.