엔비디아 창립자와의 대화: AI의 미래는 누가 만들어갈까요?
source : A Conversation with the Founder of NVIDIA: Who Will Shape the Future of AI? – YouTube
1. 인공지능 기술 분야의 선도자와의 대화
– 인공지능 기술 분야에서 중심에 위치한 회사를 이끄는 Jensen과의 대화를 진행한다.
– 미래의 AI 기술에 대한 *기대*와 *가능성*, 그리고 이 기술이 의미하는 바에 대해 다루며, 대화를 진행한다.
– Jensen은 GTC 준비로 인해 시간이 매우 촉박하지만, 이 멋진 회의에 참석해 주셔서 감사하다고 말한다.
– 인공지능과 GTC에 관해 매년 *새로운 기술들을 선보이는 회사*이며, 지금까지는 가능성을 계속 키워나갔다고 밝힌다.
– 또한 이 문맥에서 발생하는 논점 중 하나는, *정부 리더들에게 컴퓨팅 능력과 AI를 활용한 계획을 제시*할 수 있는가에 대한 것이다.
2. ️ *컴퓨팅 패러다임의 변화*와 *가속 컴퓨팅*의 중요성
– 60년 동안 CPU로 구동되는 일반용 컴퓨팅이 지배적이었지만, 이제 가속 컴퓨팅의 시대가 도래했다. CPU만으로는 실용적이고 지속 가능한 컴퓨팅, 에너지 효율적인 컴퓨팅, 고성능 컴퓨팅, 경제적인 컴퓨팅을 할 수 없다.
– 특화된 분야별 가속 컴퓨팅이 필요하며, 이것이 우리의 성장의 기반이 되고 있다. 가속 컴퓨팅은 앞으로 컴퓨팅을 하는 가장 지속 가능하고 에너지 효율적인 방법이다.
– 이러한 가속 컴퓨팅은 AI와 같은 새로운 유형의 애플리케이션을 가능하게 한다.
– 신규로 설치된 데이터 센터뿐만 아니라, 기존에 설치된 데이터 센터들도 가속 컴퓨팅으로 대체될 것이며, 가속 컴퓨팅은 향후 소프트웨어를 지원하는 데 이상적인 아키텍처이다.
– 일반용 컴퓨팅의 전환과 동시에 가속 컴퓨팅 아키텍처의 성능도 개선될 것이며, 따라서 더 많은 컴퓨터를 구입해야 하는 것만으로 충분하지 않다. 컴퓨터의 속도도 고려해야 한다.
3. 컴퓨터의 발전과 AI 발전이 빠르게 이루어져 왔는데도 ‘불평등’ 문제가 없어질까?
– 컴퓨터 아키텍처가 계속 발전하고 있다. 그리고 지난 10년 동안이나 컴퓨팅과 AI가 발전한 것이 가장 큰 공로 중 하나였다. 우리는 그것 때문에 빠르게 발전하고 있음을 인정하기에, 앞으로 뒤이어 이루어질 수 있는 수많은 혁신도 마찬가지이다.
– 따라서, 세상을 움직이는 수요가 있다면 그것은 더욱 빠르고 효율적으로 발전할 것이기에, 반드시 많은 투자가 필요하다. 전 세계적으로 결국 최첨단 기술에 대한 투자의 차이로 ‘단지’와 ‘변화의 Ao’로 나누어질 수 있기에, 이것이 감안될 필요가 있다.
– 이에 대한 대응책으로는 기술 발전이 더 민주적으로 가속화되는 것이다. 몇몇 국가들이 더 많이 쓸 수 있게 만들어졌지만, 똑같은 기술을 더 빠르게, 더 효율적으로, 더 적은 비용으로 더 많은 사람들이 쓸 수 있다. 이것이 위 혁신에 대한 균형과 안정성을 유지하는 것이다.
– 따라서, 핵심 요약은 기술 발전은 지속적으로 발전하고 있지만, 선진 국가와 후진 국가 사이에 ‘단지’와 ‘변화의 Ao’로 구분될 수 있는 불평등 문제를 야기할 가능성이 있으며, 이러한 문제에 대한 대응책이 필요하다.
4. ️️ Nvidia는 연구원들에게 고성능 컴퓨팅을 인기 있는 게임용 그래픽카드로 제공하여 혁신의 속도를 가속화시키고 전 세계의 연구원들에게 이 기술을 민주화시켰다.
– Nvidia는 세계의 연구원들에게 고성능 컴퓨팅 기술을 민주화시켰다.
– 캐나다의 Jeff Hinton 교수, 미국 New York 대학의 Yan Lun 교수, 그리고 Stanford의 Andrew Ang 교수는 모두 Nvidia의 그래픽카드의 기술 덕분에 AI 연구 분야에서 유명해졌다.
– 최신 그래픽카드의 성능이 증가함에 따라, 현재로서는 슈퍼컴퓨터의 구매비용은 거의 무시할 수준이 되었다.
– 이러한 민주화된 기술이 혁신의 속도를 가속화시킨다.
– Nvidia는 이 기술을 민주화시키는 것이 필수적이라고 생각한다.
5. ️인공지능은 새로운 산업혁명의 시작으로, 각 국가가 자신들만의 인공지능을 소유해야 한다.
– 최근 6개월 동안 모든 국가는 인공지능에 놀라거나 두려워하지 않고, 그것을 활용하기 위해 스스로를 동원해야 한다는 깨달음이 생겼다.
– 이 산업혁명은 에너지나 식품의 생산이 아닌 지능의 생산에 관한 것이다.
– 따라서 각 국가는 자신의 지식과 문화를 담은 데이터를 소유해야 하며, 이를 활용하여 국가 자체의 지능을 구축해야 한다.
– 따라서 국가는 인프라를 구축하여 연구자, 기업, 정부가 인공지능을 활용할 수 있도록 해야 한다.
– 가상국가를 예로 들어 개발도상국이라고 가정한다면, 먼저 인프라를 구축해야 한다. 식량 생산을 활성화하려면 농장을 구축해야 하고, 에너지 생산을 활성화하려면 발전기를 구축해야 한다.
6. ️인프라를 만들어 AI를 자동화하는 것이 중요하다
– AI를 자동화시키기 위해서는 인프라를 구축해야 한다.
– 전 세계 기업들이 이를 공포하고, 신비화하고 있다. 하지만 실제로는 그저 컴퓨터 뿐이다. 물론 비용과 노력이 필요하지만 보유하고 있는 전문성을 활용하여 실현 가능하다.
– AI 구현을 위해 먼저 대규모 언어 모델 구축이 필요한데, 사우디 알바크오와 Core 42, SAD가 이를 위해 아라비아어 자연어처리 기술을 개발하고 있다.
– 하지만 AI가 언어 외에 개념의 자동화도 가능하다는 점에서, *언어 모델 구축 뿐만 아니라 여러 방면의 구축이 중요하다는 것*을 인지해야 한다.
7. 여러 분야에서 AI가 큰 발전을 이루면서 인프라 구축의 중요성이 대두되고 있다.
– AI가 현재 여러 분야에서 발전하고 있으며, 이를 위한 인프라 구축이 중요하다.
– 언어, 생물학, 물리학, IoT 등 많은 분야에서 AI가 발전하고 있으며, 해당 분야의 연구자들이 모이면 AI를 적용할 수 있는 인프라 구축이 필수적이다.
– 따라서 현재는 인공지능에 대한 허황된 기대와 실제로 엄청난 혁신을 가져올 가능성을 정확하게 판단해야 한다.
– 그러나 이러한 검증 작업을 위해 컴퓨터 과학 분야나 전기 분야를 조절하는 것은 지극히 어렵다.
– 따라서 우리는 AI의 사용 사례에 대해 규제하는 방안을 모색해야 한다.
8. 인공지능 기술에 대한 규제 필요, 데모크라타이즘을 지향하자
– 어떤 새로운 기술이든 안전하게 개발하고, 안전하게 적용하며, 안전하게 사용해야 한다.
– 항공기, 자동차, 제조 시스템, 의학 등 모든 산업은 이미 규제를 받고 있으므로 인공지능 역시 규제가 필요하다.
– 그러나 새로운 기술에 대해 사람들을 불안하게 하고 미스티파이하며, 그 기술에 대해 아무것도 하지 말라고 주장하는 것은 잘못된 접근이다.
– 그 대신 인공지능 기술을 데모크라타이즘으로 접근하여 보편화시키길 원한다.
9. LAMA 2 및 다른 오픈소스 모델이 지역의 AI 연구자들을 활성화시켰다.
– LAMA 2 및 Falcon, M-TRELL, SMOG 등 다양한 오픈소스 모델이 안전성, 설명 가능성 등 다양한 이유로 혁신을 이루고 있다.
– 이러한 오픈소스 언어들 덕분에 투명성과 함께 다양한 혁신이 가능해지고 있다.
– AI 기술의 발전에 모든 지역과 국가가 참여하는 것이 중요하다고 생각한다.
– UAE에서는 오픈소스 시스템에 초점을 맞추어 다른 개발자들에게 기회를 제공하고 있다.
– 기술의 향후 발전은 GPU에 의존할 것인지 혹은 미래의 차세대 기술이 뚜렷한 전환점이 될 것인지는 아직 알 수 없다.
10. Nvidia GPU – 모든 플랫폼에서 누구나 이용할 수 있는 유일한 플랫
– NVIDIA GPU는 딥러닝(AI)과 관련된 플랫폼 중 유일하게 모든 플랫폼에서 이용 가능하다.
– 따라서 미국의 모든 클라우드와 데이터 센터, 현재는 자율주행 시스템 등 어디서든지 NVIDIA GPU를 이용할 수 있다.
– 거기에 NVIDIA GPU의 유연한 아키텍처 덕분에 다른 아키텍처와도 호환되며, 다양한 transformers 아키텍처를 둘러싸고 있다.
– 이러한 이유로 연구원들은 NVIDIA GPU를 활용하여 새로운 모델을 개발할 수 있기 때문에 Nvidia GPU는 독특하다고 할 수 있다.
11. ️인공지능은 진화하고 있는데 NVidia는 항상 주요해
– 인공지능은 짧은 시간 동안 많은 변화와 진화를 겪고 있다. 과거에 사용된 인프라와는 전혀 다른 현재의 NVidia의 기술을 강조한 것이었다.
– NVidia는 변화하는 인프라 속에서도 항상 주요하다고 강조하며, 혁신하고 나아갈 수 있는 능력을 갖췄다.
– 교육에 관한 이야기로 전환되면서 인공지능을 배울 필요성에 대해 언급하였다. 컴퓨터 과학을 배우는 것이 중요하다는 일반적인 생각과는 다른 의견을 제시하였다.
12. ️인공지능으로 세상의 모든 사람이 프로그래머가 됨!
– 인간이 아닌 기술이 프로그래밍하고 모든 사람이 프로그래머가 되는 것이 인공지능의 기적이다.
– 기술적인 격차가 완전히 해소되어 인공지능에 참여하는 사람들이 많아졌다.
– 인공지능의 등장으로 기술 리더십이 재정립되었다.
– 현재는 모든 기업의 구성원이 기술적인 역량을 갖춘 기술 인사이더가 될 수 있다.
– 기술 활용의 가능성과 중요성을 인식하고, 모든 사람의 역량을 높이는 것이 필수적이다.
13. 인간 생물학의 복잡성과 영향력, 라이프 사이언스의 중요성
– 대학 전공으로 추천하는 과목은 인간 생물학이다. 복잡성과 다양성으로 어려운 이 분야는 매우 복잡하고 이해하기 힘들다. 뿐만 아니라 매우 영향력이 크다.
– 라이프 사이언스는 부분적으로 기술과 과학이 얽히기 때문에 엔지니어링이라고 보는 것이 적합하다. 하지만 라이프 사이언스는 급변하는 현상이다.
– 컴퓨터 과학, 전통 산업 등에서는 ‘발견’이라는 용어를 사용하지 않고 엔지니어링으로 인정받는 반면, 라이프 사이언스는 ‘발견’이라는 용어로 인식되고 있다.
– 그렇기 때문에 라이프 사이언스는 매우 중요한 분야이며, 매년 그 발전이 이루어지고 있다.
14. 디지털바이오를 포함하는 생명공학에 있어서 미래 엔지니어링 중요성
– 앞으로는 생명공학이 엔지니어링의 한 분야로 자리 잡을 것이며, 디지털바이오는 과학만이 아닌 엔지니어링이라는 것을 깨닫게 될 것이다.
– 이를 토대로 단백질, 화학물질, 효소, 재료 등을 엔지니어링하는 일을 즐기는 사람들이 많아져서, 더욱 에너지 효율적이고, 경감률이 높아지며, 더 튼튼하고 지속가능한 제품들을 개발할 수 있을 것이다.
– 이를 통해 앞으로 직면할 난제들, 예를 들면 질병이나 자원 제한과 같은 문제점들을 해결할 수 있을 것이며 이러한 발전은 과학적 발견만이 아닌 엔지니어링도 수반될 것이다.
– Jess의 진솔한 인사이트가 담긴 오늘의 대화, 영감을 받아 이 분야에 새로운 엔지니어링 세대들이 탄생할 수 있기를 기원한다.
완벽한 영상요약 Lilys AI
https://lilys.ai/digest/268246?sId=8Pm2xEViNIo&source=video&result=summaryNote&isBlogRequested=false&s=1
스크립트
It’s my pleasure and privilege to be sitting in front of all of you here today to moderate a pioneer, not just in the technology space, but in the artificial intelligence space as well. Artificial Intelligence Space Jensen, who is leading probably the company that’s at the center of the eye of the storm when it comes to artificial intelligence, the hype, the possibilities, and what this technology will mean. Jens, it’s a pleasure being with you on stage here. Thank you. It’s great to be here at this amazing conference. I just want to say that we really appreciate you taking the time, especially since you have GTC in 6 weeks. In six weeks, I’m going to tell everybody about a whole bunch of new things we’ve been working on, the next generation of AI. Every single year, they just push the envelope when it comes to artificial intelligence and GTC. So we’re hoping to get a few snippets out of this. Okay. So I’d like to start with a question that was going on in my mind: how many GPUs can we buy for 7 trillion? Well, apparently, all the GPUs. I think this is one thing I’m waiting to ask Sam about because it’s a really big number. Talk about ambition. We have a lot of ambition here in the USA. We don’t lack ambition. But is there a view that you Can you give government leaders today advice on compute capabilities and artificial intelligence? How can they plan well? Where do you think the deployment is going to make sense? And what advice do you have? Well, first of all, these are amazing times..
These are amazing times because we’re at the beginning of a new Industrial Revolution, production of energy through steam, production of electricity, and information revolution with PCs and the internet. And now, artificial intelligence. We are experiencing two simultaneous transitions, and this has never happened before.
The first transition is the end of general-purpose computing and the beginning of accelerated computing. It’s like specialized computing using CPUs for computation. As the foundation of everything we do, it’s no longer possible. And the reason for that is because it’s been 60 years since we invented central processing units in 1964 with the announcement of the IBM System/360. We’ve been riding that wave for literally 60 years now, and this is now the beginning of accelerated computing. If you want sustainable computing, energy-efficient computing, high-performance computing, cost-effective computing, you can no longer rely solely on CPUs. Longer do it with general-purpose computing. You need specialized, domain-specific acceleration, and that’s what we’re driving at the foundation of our growth, Accelerated Computing. It’s the most sustainable way of doing computing going forward. It’s the most energy-efficient. It is so energy-efficient, it’s so cost-effective, it’s so performant that it enabled a new type of application called AI. The question is, what’s the cart and what’s the horse? You know, first is Accelerated Computing and enabled a new new application..
There’s a whole bunch of applications that are accelerated today, and so now we’re in the beginning of this new New Era. And what’s going to happen is there’s about a trillion dollars’ worth of installed base of data centers around the world, and over the course of the next four or five years, we’ll have two trillion dollars’ worth of data centers that will be powering software around the world, and all of it is going to be accelerated. And this architecture for Accelerated Computing is ideal for this next generation of software called generative AI. And so that’s really at the core of what is happening.
While we’re replacing the installed base of general-purpose. Computing, remember that the performance of the architecture is going to be improving at the same time. So you can’t assume just that you will buy more computers, you have to also assume that the computers are going to become faster. Therefore, the total amount that you need is not going to be as much. Otherwise, the mathematics, if you just assume that computers never get any faster, you might come to the conclusion we need 14 different planets and three different galaxies and four more suns, to fuel all this.
But obviously, computer architecture continues to advance. In the last 10 years, one of the greatest contributions – and I really appreciate you mentioning that – the rate of innovation. One of the greatest contributions we made was advancing Computing and advancing AI by 1 million times in the last 10 years..
And so, whatever demand that you think is going to power the world, you have to consider the fact that it is also going to do it one million times larger, faster, and more efficiently. Don’t you think that creates a risk of having a world of halves and Have Nots, since we need to constantly invest to ensure that we have The Cutting Edge and to ensure that… We are able to create the applications that are going to reshape the world and governments as we know them. Do you think that there’s going to be an issue of countries that can afford these GPUs and countries that can’t? And if not, because you know, it would be surprising if you said the answer is no, if not, what are going to be the drivers of equity? Excellent question. Um, first of all, when something improves by a million times and the cost or the space or the energy that it consumed did not grow up by a million times, in fact, you’ve democratized the technology.
Um, researchers all over the world would tell you that Nvidia singlehandedly democratized high-performance computing. We put it in the hands of every researcher. It is the reason why AI researchers, Jeff Hinton in the University of Toronto, Yan Lun, I think Yan’s going to be here, University of New York, um, Andrew Ang in Stanford, simultaneously discovered us. They didn’t discover us because of supercomputers. They discovered us because of gaming GPUs that they used for deep learning..
We put accelerated computing or high-performance computing in the hands of every single researcher in the world. And so, when we accelerate the rate of innovation, we’re Democratizing the technology. The cost of building or purchasing a supercomputer today is really negligible, and the reason for that is because we’re making it faster and faster and faster. Whatever performance you need, costs a lot less today than it used to. It is absolutely true, we have to democratize this technology, and the reason why is very clear.
There’s an awakening of every single country in probably the last six months that artificial intelligence is a technology you can’t be mystified by, you cannot be terrified by it. You have to find a way to activate yourself to take advantage of it, and the reason for that is because this is the beginning of a new industrial revolution. This industrial revolution is about the production not of energy, not of food, but the production of intelligence. And every country needs to own the production of their own intelligence, which is the reason why there’s this idea called sovereign AI. You own your own data, nobody owns it. Your country owns the data, it codifies your culture, your society’s intelligence, your common sense, your history. You own your own data; you, therefore, must take that data, refine that data, and own your own national… Intelligence, you cannot allow that to be done by other people, and that is a real realization..
Now that we’ve democratized the computation of AI, the infrastructure of AI, the rest of it is really up to you to take initiative, activate your industry, build the infrastructure as fast as you can so that the researchers, the companies, your governments can take advantage of this infrastructure to go and create your own AI. I think we completely subscribe to that vision. That’s why the UAE is moving aggressively on creating large language models, mobilizing compute, and maybe work with other partners on this.
Let’s try to flip the paradigm a little bit. Let’s today assume that Jensen Huang is the president of a developing nation that has a relatively small GDP, and you can focus on one AI application. What would it be? Let’s call it a hypothetical nation and say that you know you have so many problems that you need to deal with.
What is the first thing that you’re going to approach, if you’re going to mobilize artificial intelligence in that scenario? The first thing you have to do is you have to build infrastructure. If you want to mobilize the production of food, you have to build. Farms, if you want to mobilize the production of energy, you have to build AC generators. If you want to operationalize information, digital. If you want to digitalize your economy, you have to build the internet.
If you want to automate the creation of Artificial Intelligence, you have to build the infrastructure. It is not that costly, it is also not that hard..
Companies all around the world, of course, want to mystify, terrify, glorify, you know all of those ideas, but the fact of the matter is, they’re computers. You can buy them off the shelf, you can install it. Every country needs, already has, the expertise to do this. And you surely need to have the imperative to go activate that.
The first thing that I would do, of course, is I would codify the language, the data of your culture, into your own large language model. And you’re doing that here, Core 42, Saudi Aramco, SAD, really doing important work to codify the Arabic language and creating your own large language model. But simultaneously, remember that AI is not just about language.
AI, we’re seeing several AI revolutions happening at the same time, AI for… Language AI for biology, learning the language of protein, machine, and chemicals. AI for physical sciences, learning the AI of climate, materials, energy discovery. AI of IoT, the language of keeping places safe, computer vision, and such. AI for IoT, AI for Robotics and autonomous systems, manufacturing, and such. There’s AI revolutions happening, AI great breakthroughs happening in all of these different domains. And if you build the infrastructure, you will activate the researchers in every one of these domains..
Without the internet, how can you be digital? Without farms, how can you produce food? Without an AI infrastructure, how can you activate all of the researchers that are in your region to go and create the AI models? You touched upon the issue of, I would say, authentic ignorance, the fear-mongering of AI taking over the world. And I think there is a requirement for us to clarify where the hype is real and where artificial intelligence really has the power to create a lot of disruption and to harm us. And where AI is going to be good. What do you think is the biggest issue when it comes to artificial intelligence right now? Because I think the problem of regulating AI is like trying to say we want to Regulate a field of computer science or regulate electricity? You don’t regulate electricity as an invention or as a discovery. You regulate a specific use case.
What is one use case that you think we need to regulate against and that government should mobilize towards? Excellent question! First of all, whatever new incredible technology is being created, you go back to the earliest of times. It is absolutely true. We have to develop the technology safely. We have to apply the technology safely, and we have to help people use the technology safely. Whether it’s the plane that I came in, cars, manufacturing systems, medicine, all of these different industries are heavily regulated today. Those regulations have to be extended and augmented to consider artificial intelligence..
Artificial intelligence will come to us through products and services. It is the automation of intelligence, and it will be augmented on top of all of these various industries. Now, it is the case that there are some interests to scare people about this new technology, to mystify this technology, to encourage other people to not do anything about that technology and rely on them to do it. And I think that… That’s a mistake. We want to democratize this technology.
Let’s face it, the single most important thing that has happened last year, if you were to ask me the one single most important event last year and how it has activated AI researchers here in this region, it’s actually LAMA 2. It’s an open-source model or Falcon, another excellent model. Very true. M-TRELL, excellent model. I just saw another one, a SMOG. There are so many open-source models, innovations on safety alignment, guard railing, reinforcement learning, so many different reasons. So many different innovations that are happening on top of transparencies, explainability, all of this technology that has to be built. All were possible because of some of these open-source languages. And so I think that democratizing, activating every region, activating every country to join the AI advance is probably one of the most important things, rather than convincing everybody it’s too complicated, it’s too dangerous, it’s too mystical, and only two or three people in the world should be able to do that. That, I think, is a huge mistake..
The focus, I think, that we have done in the UAE is to focus on open-source systems. Because we believe that anything we develop here should be given as an opportunity for others who can’t develop it. Most of this is developed using GPUs, which are graphic processing units that you guys are supplying to the world. What do you think the next era is going to depend on? Is it going to continuously be built on GPUs, or is there something else as a breakthrough that we’re going to see in the future, do you think? Actually, you know that in just about all of the large companies in the world, there are internal developments. At Google, there are TPUs. At AWS, there’s Transium. At Microsoft, there’s Maya. And there are chips that they’re building in China. Just about every single CSP has chips that they’re building.
The reason why you mention NVIDIA GPUs is because NVIDIA GPU is the only platform that’s available to everybody on any platform. That’s actually the observation. It’s not that we’re the only platform that’s being used; we’re simply the only platform that democratizes AI for everybody’s platform. We’re in every single Cloud, we’re in every single data center. We’re available in the cloud, in your private data centers, all the way out to the edge, all the way out to autonomous systems. Robotics and self-driving cars – one single architecture spans all of that..
That’s what makes Nvidia unique – that we can, in the beginning, when CNNs were popular, we were the right architecture because we were programmable. Our architecture has the ability to adapt to any architecture that comes along. So when CNN came along, RNN came along, along LSTM came along, and then eventually Transformers came along, and now Vision Transformers, bird’s-eye view Transformers, all kinds of different Transformers are being created. The next generation state-space models which is a probably the next generation of Transformers – all of these different architectures can live and breathe and be created on Nvidia’s flexible architecture. And because it’s available literally everywhere, any researcher can get access to Nvidia GPUs and invent the next generation.
So for those of you who are non-technical and heard, you know, a foreign language there with CNNs and some of the other acronyms that are being used – the thing about artificial intelligence is it’s going through a lot of evolutions over a very short period of time. So whatever the infrastructure that was used probably 5 years ago is… Very different to the infrastructure that’s being used today, but what Jensen’s point was, I think it’s a very important point, is NVIDIA has always been relevant. Historically, we see companies that are relevant at one phase of development and then, as the infrastructure changes, they become irrelevant. But you guys were able to innovate and push through..
Let’s move to a non-air related topic for a second. I want to talk about education. So today, knowing what you know, seeing what you see, and being at the cutting edge of technology, what should people focus on when it comes to education? What should they learn? How should they educate their kids and their societies? Wow, excellent question. I’m going to say something, and it’s going to sound completely opposite, um, of what people feel. You probably recall, over the course of the last 10-15 years, almost everybody who sits on a stage like this would tell you, it is vital that your children learn computer science. Everybody should learn how to program.
And in fact, it’s almost exactly the opposite. It is our job to create computing technology such that nobody has to program and that the programming language is human. Everybody in The world is now a programmer. This is the miracle of artificial intelligence. For the very first time, we have closed the gap. The technology divide has been completely closed, and this is the reason why so many people can engage artificial intelligence. It is the reason why every single government, every single industrial conference, every single company is talking about artificial intelligence today. Because for the very first time, you can imagine everybody in your company being a technologist. And so, this is a tremendous time for all of you to realize that the technology divide has been closed..
Or another way to say it, the technology leadership of other countries has now been reset. The countries, the people that understand how to solve a domain problem in digital biology, or in education of young people, or in manufacturing, or in farming, those people who understand domain expertise now can utilize technology that is readily available to you. You now have a computer that will do what you tell it to do, to help automate your work, to amplify your productivity, to make you more efficient. And so, I think that this is just a tremendous time. The impact, of course, is great and yours. It is imperative to activate and take advantage of the technology. It is absolutely immediate, and also to realize that to engage AI is a lot easier now than at any time in the history of computing. It is vital that we upskill everyone, and the upskilling process, I believe, will be delightful, surprising. To realize that this computer can perform all these things that you’re instructing it to do and doing it so easily.
So, if I was going to choose a major and university as a degree that I’m going to pursue, what would you give me as an advice for something to pursue? If I were starting all over again, I would realize one thing. That one of the most complex fields of science is the understanding of biology, human biology. Not only is it complicated because it’s so diverse, so complicated, so hard to understand, living and breathing, it is also incredibly impactful..
Complicated technology, complicated science, incredibly impactful, for the very first time. And, remember, we call this field life sciences and we call drug discovery ‘discovery’, as if you wander around the universe and all of a sudden, hey look what I discovered. Nobody in computer science, nobody in computers, and nobody in the traditional industries. That are very large today, nobody says car discovery. We don’t say computer discovery. We don’t go home and say, Hey, honey, look what I found today, this piece of software. We call it engineering. And every single year, our science, our computer science, our software becomes better and better than the year before. Every single year, our chips get better. Every single year, our infrastructure gets better. However, life sciences is sporadic.
If I were to do it over again right now, I would realize that the technology to turn life, engineering life science to life engineering is upon us. And that digital biology will be a field of engineering, not a field of science. It will continue to have science, of course, but not a field just of science in the future. And so I hope that this is going to start a whole generation of people who enjoy working with proteins and chemicals and enzymes and materials and they’re engineering these amazing things that are more energy-efficient, that are lighter weight, that are stronger, that are more sustainable. All of these inventions in the future are going to be part of engineering, not scientific discovery.
So I think we can end with a very… On a positive note, hopefully we’re going to enter an era of discovery, an era of proliferating a lot of things that, unfortunately today, are challenges to us. Whether it’s disease, whether it’s limitations in resources. Thank you so much, Jess, for taking the time and being with us. And I know that we could have continued for another hour, but thank you for taking the stage and thank you for your insight. Thank you, thank you. [Music] Everyone…