诺贝尔奖得主、剑桥大学校友戴密斯·哈萨比斯爵士（Sir Demis Hassabis）认为，我们正步入“数字生物学”的新时代。在这个时代，人工智能（AI）可以以“数字化速度”重塑药物发现的基本原理。

Nobel laureate and Cambridge alumnus Sir Demis Hassabis believes we are entering a new era of ‘digital biology’, where AI can help us reimagine the principles of drug discovery at ‘digital speed’.

在剑桥大学举行的一场探讨人工智能如何加速科学发现的特别活动中，这位谷歌DeepMind首席执行官兼联合创始人表示，尽管量子计算的崛起备受瞩目，但传统计算机系统仍然具有利用人工智能推动知识进步的潜力，甚至有一天可能帮助我们揭示现实的本质。

Speaking at a special event in Cambridge, exploring how AI can accelerate scientific discovery, the Chief Executive Officer and Co-founder of Google DeepMind also said that despite the rise of quantum computing, classical computer systems still have the potential to advance knowledge using AI and could one day even help us uncover the true nature of reality.

去年，戴密斯与谷歌DeepMind的同事约翰·贾姆珀博士（Dr John Jumper）因其进行的人工智能研究在蛋白质结构预测方面做出的杰出贡献而共同获得了诺贝尔化学奖。他向剑桥的学生和校友表示，人工智能有潜力成为“完美描述生物学的语言”。

Demis, who was last year jointly awarded the Nobel Prize in Chemistry with Google DeepMind colleague Dr John Jumper for their AI research contributions for protein structure prediction, told Cambridge students and alumni that AI was potentially the “perfect description language for biology”.

他说：“目前，开发一种新药平均需要10年时间，而且成本极其高昂，动辄数十亿美元。所以我在想，为什么不能利用这些技术，把时间从数年缩短到几个月？甚至有一天，缩短到几周？就像我们已经把蛋白质结构的解析时间从可能需要数年缩短到了几分钟甚至几秒钟一样。”

 “Right now it takes an average of 10 years for a drug to be developed, and it’s extraordinarily expensive, billions and billions of dollars,” he said. “And so I’m thinking, why can’t we use these techniques to reduce that down from years to months? Maybe even, one day, weeks? Just like we reduced down the discovery of protein structures from potentially years down to minutes and seconds.”

在巴贝奇报告厅（Babbage Lecture Theatre）发表演讲时，戴密斯提到这也是他约30年前以学生身份听第一堂课的地方，并回顾了自己至今的人工智能职业与研究生涯，并分享了人工智能未来可能的发展方向，包括通用人工智能（Artificial General Intelligence，AGI）的发展——这还是一个理论阶段的人工智能系统，能够和人类执行同样的认知任务。

During his talk at the Babbage Lecture Theatre – where he told guests he attended his first lecture as a student almost 30 years ago – Demis recounted his AI career and research up to now, and also provided fascinating glimpses of how the technology might evolve, including the development of Artificial General Intelligence, a theoretical AI system that can do the same kinds of cognitive tasks that a human can do.

他说：“剑桥是个非常奇妙的地方，实话实说，剑桥激励了我的整个职业生涯，希望它也能为在座的各位带来同样的体验。”

He said: “Cambridge is an amazing place. It has inspired my whole career actually, and hopefully it is going to do the same for many of you students in the room.”

1990年代，戴密斯在皇后学院（Queens’ College）攻读计算机科学本科。从剑桥毕业后，他于2010年参与创立了DeepMind，这是一家为热门游戏开发表现卓越的人工智能模型的公司。2014年，DeepMind被谷歌收购，两年后，该公司因创造了当时许多人认为是人工智能领域“圣杯”的成就而受到全球关注：击败了世界上最古老棋类游戏之一围棋的冠军。

After graduating from Cambridge, where he studied Computer Science at Queens’ College as an undergraduate in the 1990s, he co-founded DeepMind in 2010, a company that developed masterful AI models for popular games. The company was sold to Google in 2014 and two years later, DeepMind came to global attention when the company achieved what many then believed to be the holy grail of AI: beating the champion player of one of the world’s oldest boardgames, Go.

他说：“我的人工智能之旅始于游戏，具体来说是国际象棋。我从四岁开始下棋，这让我开始思考‘思考’的本身——我们的头脑是如何制定计划、构思创意的？我们是如何解决问题的？又如何能够改进？对我而言，最吸引我的，也许甚至比游戏本身更吸引人的，是背后的思维过程。

 “My journey on AI started with games, and specifically chess,” he said. “I was playing chess from the age of four and it got me thinking about thinking itself – how does our mind come up with these plans, with these ideas, how do we problem solve, and how can we improve? What was fascinating to me, perhaps more fascinating than even the games, was the actual mental processes behind it.”

这种兴趣在他接触国际象棋电脑时进一步加深。“我记得，我对这样一个事实感到着迷——有人竟然能给一块毫无生命的塑料编程，使它能够与你对弈，并且下得相当不错。于是，在我十几岁的时候，我开始用Amiga 500计算机进行实验，开发能够玩黑白棋（Othello）等游戏的人工智能程序。这实际上是我第一次尝试人工智能。从很早开始，我就决定要用我的整个职业生涯来推动这项技术的前沿发展。”

This interest continued when he moved to playing computer chess. “I remember being fascinated by the fact that someone had programmed this lump of inanimate plastic to actually play chess really well against you. And I ended up experimenting myself in my early teenage years with an Amiga 500 computer, and building those kinds of AI programs to play games like Othello. And really, that was my first taste of AI, and I decided from very early on that I would spend my entire career trying to push the frontiers of this technology.”

他认为，电子游戏是人工智能系统的“完美试验场”。在受神经科学启发开发出能够成功掌控Atari游戏合集的学习系统和研发出击败围棋世界冠军李世石的AlphaGo计算机程序后，他将注意力转向了科学领域。

Computer games were the “perfect proving ground” for AI systems, he said. And after creating learning systems – inspired by neuroscience – that mastered Atari’s catalogue of games, and developing the AlphaGo computer programme that defeated Go world champion Lee Sedol, he turned his attention to science.

“我觉得我们已经准备好了，我们掌握的技术足够成熟，现在是时候将它们应用到游戏之外的领域，去尝试解决真正有意义的问题了。”

 “I felt that we were ready, we had the techniques that were mature enough and ready to now be applied outside of games and to try and tackle really meaningful problems.”

蛋白质折叠就是一个例子，也就是从氨基酸序列预测蛋白质的三维结构。蛋白质是生命的基石，其功能被认为与其结构密切相关。因此，了解蛋白质的结构可以为药物研发和疾病研究提供重要帮助。

Protein folding – predicting the 3D structure of a protein from its amino acid sequence – was a prime example. Proteins are the building blocks of life, and the function of a protein is thought to be related to its structure. Knowing the structure of a protein could therefore help in drug discovery and disease understanding.

科学家们已经为这一难题努力了至少50年，而在2020年11月，DeepMind的AlphaFold2工具被蛋白质结构预测技术关键评估跨社区实验项目（CASP）正式宣布为该问题提供了解决方案。随后，DeepMind利用AlphaFold2解析了已知的全部2亿种蛋白质，并将该系统及这些结构向全球开放，供所有人免费使用。

Scientists had been working on the challenge for at least 50 years when, in November 2020, DeepMind’s AlphaFold2 tool was declared to have solved it by the Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction, or CASP. DeepMind went on to use AlphaFold2 to fold all 200 million proteins known to science, and made the system and these structures openly and freely available for anyone to use.

戴密斯说道：“这就像是将十亿年的博士研究时间压缩到了一年完成。想到科学研究可以得到这样的加速，真的令人惊叹。全世界几乎每个国家的两百万名研究人员都在使用AlphaFold2，它目前已经被引用了三万多次，并已然成为生物学研究的标准工具。

”

 “It’s kind of like a billion years of PhD time done in one year,” said Demis. “And it’s amazing to think about how much science could be accelerated. Two million researchers are using it from pretty much every country in the world. It’s been cited over 30,000 times now and it’s become a standard tool in biology research.”

基于对AlphaFold2所带来的重大突破的认可，戴密斯去年被共同授予了诺贝尔化学奖。

Demis was jointly awarded the Nobel Prize in Chemistry last year in recognition of the major advances made possible by AlphaFold2.

他表示，由于这些生物结构广泛存在于地球上的生命体中，这一技术为多个领域打开了新的探索方向，包括气候、农业、疾病研究和药物发现等。

And because these biological structures exist across much of life on Earth, he said new avenues of exploration had been opened up – in a wide range of fields –  including climate, agriculture, disease, and drug discovery.

“DeepMind从一开始的使命就是以负责任的方式构建人工智能以造福人类。但我们从最初表达这一使命的方式分两步：第一步——解决人工智能问题；第二步——用它来解决其他一切问题。”

 “The mission of DeepMind mind from the beginning was about building AI responsibly to benefit humanity, but the way we used to articulate it when we started out was a two-step process, step 1 – solve Artificial Intelligence, step 2 – use it to solve everything else.

“回顾过去15年的工作，从最初在游戏领域的探索到现在我们在做的科学研究，究其核心都是让搜索问题‘可解’。面对一个极其复杂的问题，可能的解决方案有很多，而我们的任务是找到最优解，这就像是在干草堆里找针一样。而你无法通过蛮力来完成这一搜索，因此必须去了解神经网络模型，使其能够高效地引导搜索过程，并找到最佳解决方案。”

 “If I look at all the work we've done in the last 15 years, first of all our games work, and then now with the scientific work that we’ve been working on, it’s all about making this search ‘tractable’. You have this incredibly complex problem, and there’s many possible solutions to the problem, and you've got to find the optimal solution –  kind of like a needle and a haystack. And you can't do it by brute force, so you have to learn this neural network model, so that you can efficiently guide the search and find the optimal solution.

“我认为人工智能几乎可以应用到每个领域，并且我认为在接下来五到十年里，通过人工智能，我们可以推动很多领域的发展。”

 “I think AI will be applicable to pretty much every field, and I think there are many, many advances to be made over the next 5-10 years by doing that.”

在讨论通用人工智能（AGI）的发展路径时，戴密斯表示，Google DeepMind 正在多个领域推动人工智能对现实世界物理规律的理解，并提到了其最新的 Veo 2——一款最先进的视频生成工具，可以根据文本描述生成视频；以及 Genie 2——能够基于单一提示生成计算机游戏。

Discussing the path to Artificial General Intelligence (AGI) Demis said that Google DeepMind was making advances in all areas of AI’s understanding of the physics of the real world, and pointed to its new Veo 2 state-of-the-art video generation tool, which generates videos from a text description, and Genie 2, which can generate a computer game based on a single prompt.

他还强调了人工智能安全性的重要性，以及构建这些变革性系统和技术所需承担的责任。他解释了 Google DeepMind 的 SynthID 工具如何在 AI 生成的内容中嵌入隐形水印，使其可以被检测为人工合成的图像、音频、文本或视频。

And stressing the importance of AI safety, and the responsibility that came with building these kinds of transformative systems and technologies, he explained how Google DeepMind’s SynthID tool invisibly watermarked AI-generated content, which can then be detected as synthetically generated image, audio, text or video.

“人工智能在解决我们面临的重大挑战（从气候变化到医疗健康）方面具有巨大的潜力。但它势必会影响所有人。因此我认为，我们必须与社会各界的利益相关者展开广泛合作，这一点非常重要。而且，考虑到这些技术正以指数级的速度进步，这种合作在未来只会变得更加关键。”

 “AI has this incredible potential to help with our greatest challenges, from climate to health. But it is going to affect everyone, so I think it’s really important that we engage with a wide range of stakeholders from society. And I think that’s going to become increasingly important given the exponential improvement that we’re seeing with these technologies.”

展望未来，戴密斯表示他对下一代虚拟助手技术感到“非常兴奋”，他在介绍Google DeepMind开发的一款可以理解我们周围世界的研究原型助手时将其称为“通用助手”。

Looking to the future, Demis said he was “very excited” about the next generation of virtual assistant technology, or ‘universal assistants’ as he described Google DeepMind’s work on a research prototype assistant that can understand the world around us.

“我们把它叫做‘阿斯特拉计划’（Project Astra），你可以把它放在手机或者其他设备上，可能是眼镜上。它是一个你可以带着它在现实世界中四处走动的助手，帮助你处理日常生活中的事务。”

 “We call it ‘Project Astra’, where you have it on your phone or some other devices, maybe glasses. It’s an assistant you can take around with you in the real world and it helps you in everyday life.”

他说，人工智能的下一步是构建像我们在 AlphaGo 中看到的那种规划系统，这些系统能够搜索并找到问题的最佳解决方案，通过‘建立在’像 Google Gemini 这样的世界模型之上，这些模型能理解现实世界是如何运作的。他表示，结合这两者，它们能够在现实世界中进行规划并实现目标。

He said the next step in AI was building planning systems like we saw with AlphaGo, which can search and find good solutions to problems, ‘on top of’ world models like Google Gemini, which understand how the real world works. Combined, he said, they can plan and achieve things in the real world.

“这对于机器人技术等领域的应用至关重要，我认为在未来两到三年里，这将是一个会迎来巨大进展的广阔领域。”

 “That’s key to things like robotics working, which I think in the next two or three years is going to be a huge area that’s going to have massive advances.”

在结束他的演讲时，戴密斯形容自己是“图灵的冠军”，并提出了一个问题：“这些图灵机和传统计算的概念能够走多远？”

Bringing his talk to an end, Demis described himself as “Turing’s champion” – and posed the question “how far can these Turing machines and the idea of classical computing go?”

“人们认为有很多问题需要量子计算来解决。我的猜想是，实际上，这些人工智能系统构建基础的经典图灵机能做的事情远远超出我们之前对它们的预期。”

 “There are a lot of things that are thought to require quantum computing to solve. My conjecture is that actually classical Turing machines that these types of AI systems are built on can do a lot more than we previously gave them credit for.

“如果你想一想AlphaFold 和蛋白质折叠——蛋白质是量子系统，它们在原子尺度上运作，人们可能会认为你需要量子模拟才能找到蛋白质的结构。然而，我们却能够通过神经网络得到近似的解决方案。”

 “If you think about AlphaFold and protein folding – proteins are quantum systems, they operate at the atomic scale and one might think you need quantum simulations to actually be able to find the structures of proteins. And yet we were able to approximate those solutions with our neural networks.

“因此，很有潜力的一个的观点是，任何可以在自然界中生成或发现的模式，都可以通过这些经典学习算法被高效地发现和建模。如果这一点得到验证，它将对量子力学甚至基础物理学产生深远影响，这是我希望进一步探索的领域。也许这些经典系统能够帮助我们揭示现实的真正本质。”

 “And so one potential idea is that any pattern that can be generated or found in nature can be efficiently discovered and modelled by one of these classical learning algorithms. And if that turns out to be true, it has all sorts of implications for quantum mechanics and actually fundamental physics, which is something that I hope to explore. Maybe these classical systems will help us uncover what the true nature of reality might be.

“这也让我回到我多年前走上人工智能研究道路的初衷上来。我一直相信，以这种方式构建的通用人工智能（AGI）可以成为理解我们周围宇宙及我们在其中位置的终极通用工具。

 “And that leads me back to the whole reason I started my path on AI many, many years ago. I always believed that AGI built in this way could be the ultimate general purpose tool to understand the universe around us and our place in it.”