https://youtu.be/Fe2adi-OWV0?si=X98W4nDxgfPxJ_-N
So I think one of my favorite parts of the film is that part where the team has just told you, well we could just find the structures for all the proteins and just release those.
영화의 가장 좋아하는 부분 중 하나는 팀이 당신에게 방금 말한 부분인데, 우리는 모든 단백질의 구조를 찾아서 그냥 공개할 수 있다고요.
I and then you release them to the world and you see the the map of the globe light up as people in real time are getting all of those structures.
그리고 당신이 그것들을 세상에 공개하면, 실시간으로 사람들이 그 구조들을 받으면서 지구본 지도가 밝아지는 것을 보게 됩니다.
What was that like?
그건 어땠나요?
Tell me what was the what was the feeling that you had?
당신이 느꼈던 감정이 어땠는지 말해 주세요.
I mean look there there was so many amazing moments and the team will remember this of um but that was one of the big highlights.
그러니까, 정말 많은 놀라운 순간들이 있었고 팀이 이것을 기억할 테지만, 그건 큰 하이라이트 중 하나였어요.
It was very satisfying to see that this sort of idea that we maybe if we crack this really important problem you know potentially millions of researchers around the world will will make use of it and um you know to see that sort of lighting up all across the globe um is really a kind of humbling and amazing experience.
이 중요한 문제를 풀면 어쩌면 전 세계 수백만 명의 연구자들이 이를 활용할 수 있다는 아이디어를 보는 건 매우 만족스러웠고, 지구 전역에서 불이 켜지는 것을 보는 건 정말 겸손하고 놀라운 경험이었어요.
I came here for the uh AI for science forum which you held and I think this the thing that shocked me is that for 50 years the work of tens of thousands of scientists revealed the structures of 150,000 proteins.
저는 당신이 주최한 AI for science 포럼에 왔는데, 저를 놀라게 한 건 50년 동안 수만 명의 과학자들의 작업이 150,000개의 단백질 구조를 밝혀냈다는 거예요.
That was the grand sum of human effort.
그게 인간 노력의 총합이었죠.
And then in a few years your team small 15 20 people was able to find the structures of 200 million.
그리고 몇 년 만에 당신의 작은 팀, 15~20명 정도가 2억 개의 구조를 찾아냈어요.
Yeah.
네.
Well look I mean first of all the first thing to say is we couldn’t have done it without the first 150,000 right.
우선, 첫 번째로 말할 건 우리가 첫 150,000개 없이는 할 수 없었을 거라는 거예요.
So that incredible, you know, we need to thank the the structural biology community, you know, thousands of researchers painstakingly putting together these structures using very exotic and pretty expensive and complicated equipment um over 50 years like you say and the sum totals 150,000 but it was enough to uh kickstart us to be able to create a system like Alphold to learn from those 150,000 and then actually uh learn further from its own predictions, the best ones of its own predictions and sort of feeding that back into the system and then eventually being good enough to kind of understand something about protein, something fundamental about protein structure.
그 놀라운 일로, 우리는 구조 생물학 커뮤니티에 감사해야 해요. 수천 명의 연구자들이 50년 동안 당신이 말한 대로 매우 이국적이고 꽤 비싸고 복잡한 장비를 사용해 이 구조들을 힘들게 모았고, 총합이 150,000개였지만, 그것이 우리를 시작하게 해 AlphaFold 같은 시스템을 만들 수 있게 했고, 그 150,000개로부터 배우고, 실제로 자신의 예측 중 최고의 것들로부터 더 배우고, 그것을 시스템에 다시 피드백하며, 결국 단백질에 대한 무언가, 단백질 구조에 대한 근본적인 것을 이해할 만큼 충분히 좋아졌어요.
So then eventually we could do all 200 million and and I think as John says in the film, you know, it usually takes a PhD student their their whole PhD.
그래서 결국 우리는 2억 개 모두를 할 수 있었고, 영화에서 존이 말한 대로, 보통 박사 학생이 전체 박사 과정을 통해 하나의 단백질 구조를 찾는 데 걸려요.
That’s kind of a rule of thumb of like to find the structure of one protein.
그게 하나의 단백질 구조를 찾는 대략적인 규칙이에요.
So you know 200 million times 5 years a billion years of PhD time which is quite something you know to have done in in a year.
그래서 2억 개 곱하기 5년은 10억 년의 박사 시간인데, 그걸 1년 만에 해냈다는 건 정말 대단한 일이에요.
See like I feel like I didn’t get it before I came here and I heard those numbers and I was like oh things have fundamentally changed and I don’t think the world gets it yet.
저는 여기 오기 전에는 이해하지 못했는데, 그 숫자를 듣고 나니 아, 상황이 근본적으로 변했다는 걸 알았고, 세상이 아직 이해하지 못한 것 같아요.
Um so I think that’s one of the exciting things about this film.
그래서 이 영화의 흥미로운 점 중 하나라고 생각해요.
And I think, you know, another thing that’s really important to keep in mind is you figured out all 200 million now they’re out there, but the discoveries and the breakthroughs that are going to come from that, they’re going to take decades, but we are going to be reaping the the rewards of that for for decades, centuries, I think.
그리고, 또 하나 중요한 건 당신이 2억 개 모두를 밝혀냈고 이제 그것들이 공개됐지만, 그로부터 나올 발견과 돌파구는 수십 년이 걸릴 거예요, 하지만 우리는 그 보상을 수십 년, 수백 년 동안 누릴 거예요.
So, I mean, it’s sort of opened up uh and and this is why we put out there into the world.
그래서, 이건 세상에 열린 거예요, 그리고 이게 우리가 세상에 공개한 이유예요.
We knew we could only think of a tiny fraction of what the entire scientific community might do with it.
우리는 전체 과학 커뮤니티가 그걸로 할 수 있는 것의 아주 작은 부분만 생각할 수 있다는 걸 알았어요.
And it’s really gratifying to see the whole range of things that people are already doing.
사람들이 이미 하고 있는 다양한 것들을 보는 건 정말 기쁜 일이에요.
Over two and a half million researchers from pretty much every country in the world working on their really important biology and medical uh problems and making great progress with that.
전 세계 거의 모든 나라에서 250만 명 이상의 연구자들이 정말 중요한 생물학 및 의학 문제에 대해 작업하고 큰 진전을 이루고 있어요.
And and right now I think it’s super well known in the scientific community but as you say I don’t think it’s it’s appreciated yet in the general public what this is going to do.
그리고 지금은 과학 커뮤니티에서 매우 잘 알려져 있지만, 당신이 말한 대로 일반 대중은 이게 무엇을 할지 아직 제대로 인식하지 못한 것 같아요.
And I think that will come in the next 5 10 years as we start getting uh you know AI designed drugs that were helped by things like Alpha Fold and many many other amazing things for society that will come as a downstream consequence of us knowing what these structures are.
그리고 그건 다음 5~10년 안에 올 거예요, AlphaFold 같은 것들이 도운 AI 설계 약물이 나오면서, 그리고 우리가 이 구조들을 아는 데서 오는 사회를 위한 많은 놀라운 것들이요.
Now can you think of any examples that have happened since the film?
영화 이후에 일어난 예시를 생각해 볼 수 있나요?
Uh well there’s many in fact a few of them were were were mentioned in those headlines you know these these ideas of um designing enzymes which are types of proteins you know that catalyze certain reactions and maybe we could uh modify some of these enzymes to help deal with some environmental issues we have like the amount of plastics in the oceans or perhaps doing even carbon capture things like this um I think incredible opportunity and obviously the main reason I was I I was interested in doing uh protein folding was to accelerate drug discovery.
음, 많아요, 사실 그 헤드라인에서 몇 가지가 언급됐어요, 효소 설계 아이디어예요, 효소는 특정 반응을 촉매하는 단백질 종류예요, 그리고 우리는 이 효소 일부를 수정해 바다의 플라스틱 양 같은 환경 문제를 다루거나, 심지어 탄소 포획 같은 걸 할 수 있을 거예요, 놀라운 기회라고 생각해요, 그리고 분명히 제가 단백질 폴딩에 관심을 가진 주된 이유는 약물 발견을 가속화하기 위해서예요.
And uh and we spun out a company, a sister company called Isomorphic Labs that actually is developing other technologies around AlphaFold and and the newer versions of AlphaFold to actually start not only do you know do you understand the structure of a protein, but then you could design a drug compound to bind to the right part of the protein surface once you understand what it structure, what its function is.
그리고 우리는 Isomorphic Labs라는 자매 회사를 분사했어요, AlphaFold 주변의 다른 기술과 AlphaFold의 새로운 버전을 개발 중이에요, 단백질 구조를 이해하는 것뿐만 아니라, 구조와 기능을 이해하면 단백질 표면의 올바른 부분에 결합하는 약물 화합물을 설계할 수 있어요.
And that’s the beginning of understanding disease and maybe trying to cure some of these terrible diseases.
그게 질병을 이해하고, 이 끔찍한 질병 일부를 치료하려는 시도의 시작이에요.
And we’re working on, you know, uh, cancers and cardiovascular diseases, all sorts of things, you know, more than a dozen drug programs.
우리는 암과 심혈관 질환, 모든 종류의 것들을 작업 중이에요, 12개 이상의 약물 프로그램요.
And one day, I hope, uh, you know, we’ll be able to reduce drug discovery down from taking like 10 years on average to go from understanding a target to having a drug in in the clinic to, you know, maybe a matter of months, perhaps even weeks, just like we did with the protein structures.
그리고 언젠가, 평균 10년 걸리는 약물 발견을, 타겟 이해에서 클리닉에 약물을 두는 데까지, 몇 개월, 어쩌면 몇 주로 줄일 수 있기를 바래요, 단백질 구조처럼요.
Yeah, that’s extraordinary.
네, 그건 놀라워요.
I wanted to ask you about your origin story.
당신의 기원 이야기를 묻고 싶었어요.
um you know something that occurred to me well here’s my thinking right AI in a way is not new dates back to the 40s and maybe 50s and it went through a series of sort of booms and then busts or AI winters as people refer to them um I think in the film you said there’s no point in being born you know ahead of your time 50 years ahead of your time so I think that my question for you is when you were graduating from Cambridge that was kind of an AI winter Um, did you see something that other people didn’t see that led you to know the time for AI was coming or were you just obsessed with this idea of intelligence and just ridiculously lucky to be born in this moment?
음, 제게 떠오른 게 있어요, AI는某种 의미로 새롭지 않아요, 40년대나 50년대로 거슬러 올라가고, 붐과 bust, 또는 사람들이 AI 겨울이라고 부르는 걸 거쳤어요, 영화에서 당신이 말한 대로, 당신의 시대보다 50년 앞서 태어나는 건 의미가 없다고요, 그래서 제 질문은 케임브리지 졸업할 때가 AI 겨울이었는데, 다른 사람들이 보지 못한 걸 보고 AI의 시대가 올 걸 알았나요, 아니면 지능 아이디어에 집착하고 이 순간에 태어난 게 엄청난 행운이었나요?
Well, look, it’s a bit of both, I would say.
음, 둘 다 조금씩이라고 할게요.
So, and actually there’s many people in the audience, many of my colleagues and friends who’ve been with me that almost that entire journey.
그래서, 실제로 청중 중 많은 사람들, 제 동료와 친구들이 거의 그 전체 여정을 함께했어요.
you saw some of them, David Silver, Ben Copy and Shane Le and um they’ll remember this very well and Tim Stevens and it’s um look I I have to be honest I would have done it no matter what because um I when I was growing up and you saw that with the chess and other things I just felt that intelligence and and therefore artificial intelligence was the most fascinating thing one could work on.
당신이 본 사람들 중 데이비드 실버, 벤 코피, 셰인 레, 그리고 팀 스티븐스, 그들은 이걸 아주 잘 기억할 거예요, 솔직히 말할게요, 저는 무슨 일이 있어도 했을 거예요, 왜냐하면 제가 자랄 때 체스와 다른 것들로 본 대로, 지능과 따라서 인공 지능이 일할 수 있는 가장 매력적인 것이라고 느꼈어요.
I always wanted my passion was was to try and understand the universe around us you know sometimes call it the nature of reality all the big questions.
저는 항상 제 열정이 우리 주변 우주를 이해하려는 거였어요, 때때로 현실의 본질이라고 부르는 모든 큰 질문들요.
So physics was my favorite subject at school and all the big physicists Richard Feman and Steven Weinberg all the great physicists Carl Sean.
그래서 물리학이 학교에서 가장 좋아하는 과목이었고, 리처드 파인만, 스티븐 와인버그, 모든 위대한 물리학자 칼 션.
Um but I sort of thought that we needed another helping hand like a tool that could help us help us as human scientists understand the world better around us.
하지만 우리는 인간 과학자로서 주변 세계를 더 잘 이해할 수 있는 도구 같은 또 다른 도움의 손이 필요하다고 생각했어요.
And uh and that for me was obvious to me from the beginning as I was when I was a teenager that um it would be AI and it would be the you know not only the most uh maybe most powerful tool to help us do science but the most interesting uh thing to develop in itself you know interrogate what intelligence is and try to understand what it is uh and while you’re trying to build something that is intelligent.
그리고 그건 제게 십대 때부터 분명했어요, 그게 AI일 거고, 과학을 하는 데 가장 강력한 도구일 뿐만 아니라, 그 자체로 개발할 가장 흥미로운 거예요, 지능이 무엇인지 탐구하고 이해하려고 하면서, 지능적인 것을 만들려고 하면서요.
So I think I was always going to do that.
그래서 저는 항상 그럴 거라고 생각했어요.
Um but also when you look at these AI winters and you look at the state of technologies you find it you have to have a good reason why you think you might be able to try it in a new way those winters are are in a way learning you know opportunities to learn why did those methods not work those deep blue methods that we saw that beat Gary Kasparov amazing they could win the chess but really was a little bit of a dead end because they were hard programmed hardcoded to only do that one thing play chess so it wasn’t some sense was missing the essence of intelligence in in many ways this this general generalness and this learning cap capability and we knew we had these these techniques they were very nent you know neural networks became deep learning and then reinforcement learning as you heard we we knew those techniques um could potentially scale why did we know that because actually the the the human brain is a form of those you know we’re a neural network obviously that’s what inspired neural artificial neural networks in the first place was was was you know neurons in the brain.
하지만 AI 겨울을 보고 기술 상태를 보면, 새로운 방식으로 시도할 수 있을 거라는 좋은 이유가 있어야 해요, 그 겨울은 학습 기회예요, 왜 그 방법들이 작동하지 않았는지 배우는 거요, 우리가 본 딥 블루 방법이 게리 카스파로프를 이겼지만, 체스를 이길 수 있었지만, 정말 막다른 골목이었어요, 왜냐하면 그 하나만 하기 위해 하드코딩됐으니까요, 그래서 어떤 의미로 지능의 본질을 놓쳤어요, 일반성과 학습 능력, 우리는 그런 기술들이 있었고, 신경망이 딥 러닝이 되고 강화 학습이 됐어요, 그 기술들이 확장될 수 있다는 걸 알았어요, 왜냐하면 인간 뇌가 그 형태니까요, 우리는 신경망이고, 그게 인공 신경망을 처음 영감 준 거예요, 뇌의 뉴런이요.
And reinforcement learning is one of the main ways that animals including humans do learn.
그리고 강화 학습은 동물包括 인간이 배우는 주요 방법 중 하나예요.
You know the dopamine system in the brain implements this form of reinforcement learning.
뇌의 도파민 시스템이 이 형태의 강화 학습을 구현해요.
So you know in the limit this must be possible using these types of learning techniques.
그래서 한계에서 이 학습 기술을 사용하면 가능해야 해요.
But of course you don’t know at that point if you’re 50 years ahead or not right with your time.
하지만 그 시점에서 당신이 50년 앞서 있는지 아닌지 모르죠.
But I just want to be clear on what you’re saying.
하지만 당신이 말하는 걸 명확히 하고 싶어요.
In essence, you’re saying that the AI models that you’re currently working with are in some sense analogous to the human brain or the human brain is analogous very very loosely speaking they’re inspired by the same types of techniques and approaches uh you know biological learning systems use right that’s the key it’s the learning and the generality do you think then at some point AI will be conscious well that’s a that’s a huge question and and obviously you know we have to you know they’re not not necessarily agreed upon definitions of consciousness obvious Obviously there are aspects of it like self-awareness and things that are agreed upon.
본질적으로, 당신이 현재 작업 중인 AI 모델이 어떤 의미로 인간 뇌와 유사하거나, 인간 뇌가 유사하다고 말하는 거예요, 아주 느슨하게 말해서 같은 유형의 기술과 접근으로 영감을 받았어요, 생물학적 학습 시스템이 사용하는 거요, 그게 핵심이에요, 학습과 일반성, 그러면 AI가 언젠가 의식을 가질 거라고 생각하나요, 그건 큰 질문이에요, 의식의 정의가 꼭 합의된 건 아니에요, 분명 자아 인식 같은 측면은 합의됐지만요.
Um I think that’s part of the I always felt actually answering that question was one of the things that will come about being on this journey with AI trying to build artificial minds and then comparing them to what we know about about uh uh the human brain and then seeing what the differences are if any and those differences might tell us what uh and certainly help us understand our own minds better.
그건 제가 항상 느꼈던 부분인데, 그 질문에 답하는 건 AI 여정에서 인공 마음을 만들고, 인간 뇌에 대해 아는 것과 비교하고, 차이를 보고, 그 차이가 우리 마음을 더 잘 이해하게 해줄 거예요.
things like dreaming, emotions, creativity, and things like consciousness, all the mysteries of the mind uh and uh and then uncover help us understand them and then maybe understand how special they are to the substrate that we’re in.
꿈, 감정, 창의성, 의식 같은 마음의 모든 미스터리를 이해하고, 우리가 있는 기질에 얼마나 특별한지 이해할 수 있어요.
You know, we’re carbon based versus the silicon based systems that we’re building.
우리는 탄소 기반이고, 우리가 만드는 건 실리콘 기반이에요.
You started DeepMind here in London and you had certain forces, investors maybe trying to pull you to Silicon Valley, but you resisted.
당신은 런던에서 DeepMind를 시작했고, 투자자들이 실리콘 밸리로 끌어당기려 했지만, 저항했어요.
Tell me what it was about this place or the culture that that made you want to stay here.
이 장소나 문화가 당신을 여기 머무르게 한 게 뭐였나요?
Well, look, I I I’ve been I I was born in London.
음, 저는 런던에서 태어났어요.
I’ve lived in London my whole life and you know, I think there’s a lot of amazing things about the cultures that I was immersed in.
평생 런던에 살았고, 제가 몰입한 문화에 대해 놀라운 점이 많아요.
You know, you saw me going to Cambridge and the sort of golden triangle of Oxford, Cambridge and Imperial as we’re nearby and UCL, all these august institutes.
케임브리지 가는 걸 봤듯이, 옥스포드, 케임브리지, 임페리얼의 황금 삼각형, 그리고 UCL, 이 모든 위대한 기관들요.
I think um the UK has always been very strong in science and innovation.
영국은 항상 과학과 혁신에서 강했어요.
We punch well above our weight.
우리는 우리의 무게 이상으로 펀치를 날려요.
There’s also obviously a rich history in computers with Charles Babage and Alan Turing.
찰스 바베이지와 앨런 튜링으로 컴퓨터의 풍부한 역사도 있어요.
So I feel we’re trying to carry on in that tradition.
우리는 그 전통을 이어가려고 해요.
But there was some practical reasons.
하지만 실질적인 이유도 있었어요.
One is that uh uh I at the time when we started in 2010, there was a lot of talent trained by these top places that um unless they wanted to go and work for a hedge fund or something in the city in finance, they wanted to do something really intellectually challenging.
하나가 2010년에 시작할 때, 이 최고 기관에서 훈련된 재능이 많았는데, 헤지 펀드나 금융에서 일하지 않으면 지적으로 도전적인 걸 하고 싶어했어요.
There weren’t there aren’t that many companies doing that kind of stuff in the UK or actually in Europe really.
영국이나 유럽에 그런 회사가 많지 않아요.
So I felt that we could um gather a lot of talent together very quickly that was probably being underutilized in in Europe and that that’s how it transpired.
그래서 우리는 유럽에서 활용되지 않던 재능을 빠르게 모을 수 있을 거라고 느꼈고, 그렇게 됐어요.
But the second reason was that I think AI is so important.
하지만 두 번째 이유는 AI가 너무 중요하다는 거예요.
It’s going to affect the whole world.
전 세계에 영향을 줄 거예요.
Obviously you’ve heard me talk about in the film that you know I think it’s going to be one of the most important things ever invented.
영화에서 제가 말한 대로, 가장 중요한 발명품 중 하나가 될 거예요.
I felt that I do think it’s needs the international sort of approach and cooperation around what we want to do with this technology.
국제적인 접근과 협력이 필요하다고 느꼈어요, 이 기술로 무엇을 할지.
how we want it to be deployed, how we want it to um affect our society.
어떻게 배포할지, 사회에 어떻게 영향을 줄지.
I it’s going to affect everyone in all countries.
모든 나라의 모든 사람에게 영향을 줄 거예요.
Um so I don’t I think it needs to be uh uh built with more uh voices and stakeholders uh than just sort of 100 square miles of um California, you know, in Silicon Valley and also beyond technologists and the scientists just building it.
그래서 실리콘 밸리의 100제곱마일보다 더 많은 목소리와 이해관계자로 구축해야 해요, 기술자와 과학자 너머로요.
think it needs um social scientists, economists, psychologists, you know, governments, academia, all to be involved um in in in defining how this this this enormously transformative technology should go.
사회 과학자, 경제학자, 심리학자, 정부, 학계가 모두 참여해 이 엄청난 변화 기술이 어떻게 가야 할지 정의해야 해요.
Yeah.
네.
Well, it’s clearly going to be very powerful and one of the issues that the the film addresses is the morality and ethics around that and I think particularly the safety of it.
분명히 매우 강력할 거고, 영화가 다루는 문제 중 하나가 도덕과 윤리예요, 특히 안전요.
What keeps you up at night when you think about AI?
AI를 생각할 때 당신을 밤새 깨우는 건 뭐예요?
Well, many things and and um you know, I don’t get much sleep these days, but I I for many reasons, but I think um Shane and I, you know, will remember this is that we we actually uh when we started out 2010, um it’s only 15 years ago.
많은 것들이요, 요즘 잠을 많이 못 자지만, 여러 이유로요, 하지만 셰인과 저는 2010년에 시작할 때를 기억할 거예요, 겨우 15년 전이에요.
It’s kind of amazing to see how the world’s changed.
세상이 어떻게 변했는지 보는 건 놀라워요.
And in 2010, no one was talking about AI.
2010년에 아무도 AI에 대해 이야기하지 않았어요.
Nobody was doing industry.
아무도 산업을 하지 않았어요.
Um but we knew that this was a, you know, this had the kernel of something incredibly important.
하지만 우리는 이게 엄청나게 중요한 무언가의 핵심이라고 알았어요.
And uh and we planned for success.
그리고 우리는 성공을 계획했어요.
So, we thought it was going to be a 20-y year journey and often when you do that in technology and in startups and and hard sciences that that it always stays 20 years away, right?
20년 여정이라고 생각했어요, 기술과 스타트업, 하드 사이언스에서 종종 20년 멀리 떨어져 있죠.
So, somehow, but for us, it’s it was actually it really has been 20 years and we’re sort of 15 years in now.
어쩌다 보니 우리에게는 정말 20년이었고, 지금 15년 차예요.
Um, and we planned for success, but we knew that success meant all these amazing things, curing diseases, you know, um, solving energy crisis, climate, using AI to help, all of these things.
성공을 계획했지만, 성공이 질병 치료, 에너지 위기 해결, 기후, AI를 사용해 모든 걸 의미한다는 걸 알았어요.
Um but also it came with these risks, risks of harm, enormous risks of misuse.
하지만 위험도 왔어요, 해악 위험, 오용의 거대한 위험요.
And so from the beginning we’ve been very cognizant of that responsibility.
그래서 처음부터 그 책임을 아주 인식했어요.
Um but also trying to push that debate and be role models about how to develop this technology in a responsible way.
하지만 그 논의를 추진하고, 이 기술을 책임 있게 개발하는 롤 모델이 되려고 해요.
Is this potentially unstable in that you could have a hundred companies who have the utmost ethics and morality and they think about safety to an extreme level and you have one actor who doesn’t.
이게 잠재적으로 불안정할 수 있나요, 100개 회사들이 최고의 윤리와 도덕을 가지고 안전을 극도로 생각하는데, 한 명의 행위자가 그렇지 않으면요.
Yeah.
네.
And then it ruins it for everyone.
그러면 모두를 망치죠.
Yeah.
네.
Well, that’s the huge that’s one of the huge risks that I worry about today is, you know, so-called race dynamics, right?
그게 오늘날 제가 걱정하는 거대한 위험 중 하나예요, 소위 경쟁 역학이요.
Race to the bottom.
바닥으로의 경쟁.
You know there’s many uh examples of this in history right and even if all the actors are good in that environment let alone if you have some bad actors you know that can drag everyone to to to to rush too quickly to um cut corners these kinds of things because in individual it’s a sort of tragedy of the common situation for any individual actor sort of makes sense but but as an aggregate it doesn’t and um and I’ve been saying that for a long time and Shane and others many others uh Helen and people work on respons responsibility at deep mind.
역사에 이런 예가 많아요, 모든 행위자가 좋더라도, 나쁜 행위자가 있으면 모두를 서두르게 하고 코너를 자르게 해요, 개별적으로는 공공의 비극 상황처럼 이치에 맞지만, 집합적으로는 아니에요, 저는 오랫동안 말해왔고, 셰인과 다른 사람들, 헬렌과 딥마인드의 책임 팀이요.
We’ve been talking a lot about this and that’s why I was so pleased to see some of these international summits being set up.
우리는 이걸 많이 이야기했어요, 그래서 국제 정상회담이 열리는 걸 기뻐했어요.
the first one in the UK, Bletchley Park, and then just recently in Paris uh that Macron hosted, President Macron.
첫 번째는 영국 블레츨리 파크, 최근 파리에서 마크롱 대통령이 주최했어요.
And I think we need those kinds of uh international debates uh about where this is going and um and one of the big problems is how do we uh give access to these technologies and you’ve seen with AlphaFold you know open to the world open science obviously that’s better for progress than amazing uh all the all the all the good researchers and the good people around the world can can can build on top of that work and do amazing things with it but at the same time you want to restrict access to to that same technology to would be bad actors whether that’s individuals or even rogue nations and it’s very hard balance to get right like it’s there’s no one’s yet got a good answer for how you you know do both of those things I think initially I was encouraged by the amount of effort required to develop AI so there’s many references in the film to the Manhattan project and I think it’s one of the benefits of nuclear weapons that in order to develop them you actually need basically state sponsorship or you know a huge huge undertaking and initially AI looked the same way.
우리는 이게 어디로 가는지 국제 토론이 필요해요, 큰 문제는 이 기술에 접근을 어떻게 주는 거예요, AlphaFold처럼 세계에 오픈, 오픈 사이언스는 진보에 더 좋아요, 좋은 연구자들이 그 위에 쌓고 놀라운 걸 할 수 있으니까요, 하지만 동시에 나쁜 행위자, 개인이나 불량 국가에 접근을 제한하고 싶어요, 균형 잡기 어려워요, 아직 좋은 답이 없어요, 처음에는 AI 개발에 필요한 노력 양에 고무됐어요, 영화에 맨해튼 프로젝트 참조가 많아요, 핵무기 개발에 국가 후원이나 거대한 노력이 필요하다는 게 이점이에요, 처음 AI도 그랬어요.
This is going to take you know the huge tech companies or or states to develop this but lately there’s these new developments like deepseek and there’s an Alibaba model and they look much more sort of thrifty.
이건 거대 테크 회사나 국가가 개발할 거예요, 하지만 최근 deepseek나 알리바바 모델처럼 더 검소해 보이는 개발이 있어요.
Yeah.
네.
Which I think there could be a fear that that really democratizes the access to this technology increasing the probability of a bad actor.
그게 이 기술 접근을 민주화해 나쁜 행위자 확률을 높일 수 있다는 두려움이 있어요.
Yeah.
네.
So look that that you you know you’re exactly right and I feel like this it’s it’s sort of it’s very good on the one hand you know more people accessing these technologies um you know hobbyists you know kids like I was back when I was tinkering around with theme park can now you know uh uh work on some really interesting AI systems and probably come up with amazing new uh uh applications.
그래서 당신 말이 맞아요, 한편으로는 아주 좋아요, 더 많은 사람들이 접근해요, 취미인, 제가 테마파크로 장난치던 아이처럼 이제 흥미로운 AI 시스템을 작업하고 놀라운 새 응용을 생각할 수 있어요.
Um but yeah it’s it’s sort of uh it’s you know it’s it’s it’s available to everyone and it is worrying and I feel like you know maybe we need some new uh uh approaches you know where maybe uh the market environment or something else is set where it kind of incentivizes the right behavior right so you know I was talking to some economist friends of mine and maybe they need to get involved now to set up the right incentive structures so that uh actually the players that and the actors that are are are have the right intentions, you know, backed by government society are actually the ones that that get successful and and those AI systems are more powerful and and and more productive.
하지만 네, 모두에게 이용 가능하고 걱정스러워요, 새로운 접근이 필요할 수 있어요, 시장 환경이 올바른 행동을 장려하도록요, 제 경제학자 친구들과 이야기했는데, 그들이 이제 참여해 올바른 인센티브 구조를 세워야 해요, 올바른 의도를 가진 플레이어들이 정부 사회의 지지로 성공하고, 그 AI 시스템이 더 강력하고 생산적일 수 있게요.
Um, and maybe we have to start thinking about those kinds of approaches to deal with the practicality that we’re in, which you know, I’d much rather there be a a calm CERN-like effort towards AGI, these final few steps, but given the geopolitical framework we’re in, maybe that’s not possible.
그리고 우리는 그런 접근을 생각해야 해요, 실용성을 다루기 위해요, 저는 AGI를 향한 차분한 CERN 같은 노력을 선호하지만, 지정학적 틀에서 가능하지 않을 수 있어요.
So, we have to be more pragmatic about it.
그래서 더 실용적이어야 해요.
For sure.
확실히요.
In the film, you talk about how the future will be radically different.
영화에서 미래가 급진적으로 다를 거라고 말했어요.
So, I want to ask for myself and for everyone in this audience.
그래서 제 자신과 이 청중 모두를 위해 묻고 싶어요.
Given that you were one of the leaders at the forefront of this, what do you think the world will be like in 5 to 10 years?
이 분야 선두 주자 중 하나로서, 5~10년 후 세계가 어떨 거라고 생각하나요?
Do you do you have an outlook on that?
전망이 있나요?
And I guess further to that, I have four kids.
그리고 더 나아가, 저는 네 아이가 있어요.
I’m like, what do I do with like do do I send them to school?
아이들을 학교에 보낼까?
Is that even worthwhile anymore?
그게 더 가치 있나요?
Like, so so you are the guy that I want to ask this question to more than anyone in the world.
당신이 세상 누구보다 이 질문을 하고 싶은 사람이에요.
Sure.
물론요.
Well, let’s start with the same question.
같은 질문부터 시작할게요.
For sure. send them to school.
확실히 학교에 보내세요.
I I I I I say my kids too.
제 아이들에게도 그렇게 말해요.
Look, I think the next 5 10 years is going to be um it’s a bit you know what I would say to kids these days is embrace the new technologies and as parents I think let your kids play with them that they’re coming and they’re going to increase productivity, creativity.
다음 5~10년은, 요즘 아이들에게 말할 건 새로운 기술을 받아들이라는 거예요, 부모로서 아이들이 그걸 가지고 놀게 하세요, 그게 오고 생산성과 창의성을 높일 거예요.
I think it’s going to be amazing.
놀라울 거예요.
It’s a bit like my era, my generation with computers, the advent of computers, you know, there was a lot of fears about that too and even gaming.
제 시대처럼, 컴퓨터의 도래, 그에 대한 두려움이 많았고, 게임도요.
And then um people work out, you know, if you’re growing up with that, it feels natural to you, second nature.
그리고 사람들이 알아요, 그걸로 자라면 자연스럽고, 제2의 천성처럼요.
And then um they’re often the ones that can extend it into new ways we couldn’t even dream of today.
그리고 그들은 오늘 우리가 꿈도 못 꿀 새로운 방식으로 확장할 수 있어요.
So I think a lot of that’s going to happen.
그런 게 많이 일어날 거예요.
So I still think it’s important to do maths and computer science because you’ll be best placed to take advantage of these frontier technologies and use them in new ways.
그래서 여전히 수학과 컴퓨터 과학이 중요해요, 프론티어 기술을 활용하고 새롭게 사용할 수 있게요.
So um so that I don’t the recommendation I think is the same as it’s always been.
추천은 항상 같아요.
um maybe just be prepared that things are going to move even faster and to learn you know about adapting and learning to learn actually learning quickly to adapt to a new technology that’s going to come out it seems like almost every week uh in terms of like society what I see happening is I mean 5 10 years is a long time in AI hard to predict that far ahead but um what I certainly imagine in the areas of science is I think a new renaissance almost a new golden age which I hope alpha fold is just the beginning of um of us understanding and making lots of breakthroughs in many areas of science uh and helping us with all the biggest questions, you know, from curing all diseases to helping with uh new energy sources and and and climate.
아마도 상황이 더 빨리 움직일 준비를 하고, 적응과 학습을 배우세요, 새 기술에 빠르게 적응하는 거요, 거의 매주 나오는 것 같아요, 사회적으로는 5~10년은 AI에서 길어요, 예측 어렵지만, 과학 분야에서 새로운 르네상스, 새로운 황금 시대가 올 거예요, AlphaFold가 시작이길 바래요, 많은 과학 분야에서 이해하고 돌파구를 만들고, 모든 큰 질문에 도움, 모든 질병 치료부터 새 에너지 원과 기후요.
And I think we’re going to start seeing that all in the next 10 years.
다음 10년 안에 그걸 시작할 거예요.
That’s extraordinary.
그건 놀라워요.
Well, I look forward to it.
기대해요.
I hope you do as well.
당신도 그러길 바래요.
Uh we’re going to leave it there.
여기서 마칠게요.
Um but yeah, congratulations on all your great work and winning the Nobel Prize and it’s just tremendous.
축하해요, 모든 훌륭한 작업과 노벨상 수상, 정말 대단해요.
Seriously,
진심으로요.