人工智能如何为人类节省数十亿年的研究时间 | 马克斯·杰德伯格

在医生严重短缺的地方，你们如何提供医疗服务？

How do you provide health care in a place with extreme physician shortages?

2011年，南苏丹成为了一个独立国家。

In 2011, South Sudan became an independent country.

我们是当今世界上最新的国家。

We are the youngest nation in the world right now.

我们感到被忽视了。

We feel neglected.

那是约翰内斯·赖希医生，他是他所在社区第一位成为医生的人。

That's doctor Johannes Reich, who's the first in his community to become a doctor.

作为一名医生，我对未来的期望是建立一个健康的社会，这正是我们正在努力的目标。

My hope for the future first as a doctor is a healthy society, and that is what we are working for.

当目前的U。

When the current U.

S。

S.

政府上台后，我们收到了突然的通知。

Government came into power, we got an abrupt notification.

明天，我们将暂停所有活动。

Tomorrow, we suspend all activities.

这对我们产生了直接影响。

So that had immediate effect on us.

成千上万的社区无法获得基本的医疗服务。

Thousands of communities are not able to access basic health care services.

他通过自己的非营利性社区医疗组织YoCare，在重重困难下取得了非凡的成就，帮助南苏丹人民获得必要的医疗照护，并掌握自身健康需求的主动权。

With his nonprofit community health care organization, YoCare, he's made heroic strides against great odds to empower the people of South Sudan to get access to integral medical care and take charge over their own health needs.

了解更多关于约翰内斯及其令人惊叹的故事，请观看我们最新一期的‘TED演讲每日秀’中的‘同伴星期五’访谈。

Learn more about Johannes and his breathtaking story in our most recent Fellow Fridays interview only on TED Talks Daily.

大家好。

Hi, everyone.

我是索珊娜。

Shoshana here.

今天，我们要分享一期我们认为你们会喜欢的播客。

Today, we're sharing an episode of a podcast we think that you'll love.

这期节目由TED团队精心挑选，我们相信作为TED Health的听众，你们一定能获得全新的想法和全新的视角。

It's been handpicked by the TED staff, we think that as a TED Health listener, you'll come away with a fresh idea and a totally new perspective.

请尽情收听，并在简介中点击链接获取更多信息。

Enjoy and head to the link in the description for more.

人工智能常常因它能做或不能做的各种神奇事情而备受关注。

AI gets a ton of attention about all of the fantastical things it can or can't do.

但你有没有想过，这项技术实际上如何影响着新闻头条之外的世界？

But do you ever wonder how this tech is actually impacting the world beyond the headlines?

想象一下，在数字领域加速药物研发、设计革命性材料，并揭开宇宙的奥秘。

Imagine accelerating drug discovery, designing revolutionary materials, and unlocking the secrets of the universe all within the digital realm.

这是来自TED音频合集的TED Tech播客。

This is TED Tech, a podcast from the TED Audio Collective.

我是主持人谢瑞尔·多西。

I'm your host, Sherelle Dorsey.

今天，我们将带您幕后探访伊斯莫尔菲克实验室的首席人工智能官马克斯·亚德伯格，他曾是DeepMind的研究科学家，一起探索人工智能如何彻底改变科学发现本身。

Today, we're going behind the scenes with Max Yaderberg, Chief AI Officer at Isomorphic Labs and previous research scientists at DeepMind to explore how AI is revolutionizing scientific discovery itself.

马克斯认为，我们正进入一个新时代，人工智能能够创建我们世界的虚拟模拟，让我们以前所未有的规模进行实验和创新。

Max believes we're entering a new era where AI can create virtual analogs of our world, allowing us to experiment and innovate at an unprecedented scale.

他的演讲将带我们踏上人工智能驱动科学的前沿之旅。

His talk is a journey to the cutting edge of AI powered science.

在开始之前，先插播一段来自我们的赞助商的简短信息。

Before we dive in, a quick break to hear from our sponsors.

现在，让我们欢迎马克斯·亚德伯格登上TED讲台。

And now, Max Yederberg takes the TED stage.

很久以前，我完成了我的博士学业，当时我以为做研究会很容易。

So a while ago now, I did a PhD, and I actually thought it'd be quite easy to do research.

结果发现，这真的很难。

Turns out it was really hard.

我的博士生涯主要在编写神经网络层和CUDA内核，完全是计算机科学方向的研究。

My PhD was spent coding up neural network layers and writing CUDA kernels, very much computer based science.

当时，我有一个朋友在实验室做真正的实验科学。

And at that time, I had a friend who worked in a lab doing real messy science.

他正在尝试通过实验确定蛋白质的结构，这是一件非常困难的事情。

He was trying to work out the structure of proteins experimentally, And this is a really difficult thing to do.

仅仅确定一个全新蛋白质系统的结构，就可能需要整整一个博士的研究工作。

It can take a whole PhD's worth of work just to work out the structure of a single new protein system.

十年后，我所在的领域——机器学习——彻底改变了他所从事的蛋白质结构研究。

And then ten years later, the field that I was in, machine learning, revolutionized his world of protein structure.

DeepMind开发了一种名为AlphaFold的神经网络，能够非常准确地预测蛋白质结构，解决了长达五十年的蛋白质折叠难题。

A neural network called AlphaFold was created by DeepMind that can very accurately predict the structure of proteins and solved this fifty year challenge of trying to do protein folding.

就在两周前，这项成果荣获了诺贝尔化学奖。

And just two weeks ago, this won the Nobel Prize in chemistry.

据估计，自该模型发布以来，我们已经节省了超过十亿年的人类研究时间。

And it's estimated that since the release of this model, we've saved over a billion years of research time.

十亿年。

A billion years.

原本需要整个博士学位的研究工作，现在只需几秒钟的神经网络计算就能估算出来。

A whole PhD's worth of work is now approximated by a couple of seconds of neural network time.

对我朋友来说，这听起来可能有点令人沮丧，对此我深感抱歉，但对我来说，这简直太了不起了。

And to my friend, this might sound a bit depressing, and I'm sorry about that, but to me, this is just really an incredible thing.

由于AI模型能够替代现实世界中的实验工作，我们现在得以接触到海量的关于蛋白质宇宙的新知识，这释放了我们宝贵的人类时间，使我们能够开始探索科学的下一个前沿。

The sheer scale of new knowledge about our protein universe that we now have access to due to an AI model that's able to replace the need for real world experimental lab work, and that frees up our precious human time to begin probing the next frontiers of science.

有些人说，这是一次性的事件，我们不能指望AI在科学领域再次带来这样的突破。

Now some people say that this is a one time only event, and that we can't expect to see these sort of breakthroughs in science with AI to be repeated.

我不同意。

And I disagree.

我们将继续看到AI在理解我们这个复杂真实世界方面带来突破。

We will continue to see breakthroughs in understanding our real messy world with AI.

为什么？

Why?

因为我们现在拥有了能够处理任何类型数据的神经网络架构。

Because we now have the neural network architectures that can eat up any data modality that you throw at them.

我们已经拥有了一套成熟的方法，能够将世界上任何可能的信号融入这些学习算法中。

And we have tried and tested recipes of incorporating any possible signal in the world into these learning algorithms.

同时，我们还具备工程和基础设施，能够将这些模型扩展到任何所需的规模，以充分利用我们所能创造的巨大计算能力。

And then we have the engineering and infrastructure to scale these models to whatever size is needed to take advantage of the massive amount of compute power that we can create.

最后，我们不断创造新的方法来记录和测量我们真实而复杂世界的每一个细节，从而生成更大的数据集，帮助我们训练出更丰富的模型。

And finally, we're always creating new ways to record and measure every detail of our real messy world that then creates even bigger data sets that help us train even richer models.

因此，摆在我们面前的是一个全新的范式——创建我们真实复杂世界的AI模拟体。

And so this is a new paradigm in front of us, that of creating AI analogs of our real messy world.

这一新的AI范式将我们真实而自然的世界作为基础，通过神经网络学习并重建其各个组成部分。

This new AI paradigm takes our real messy natural world and learns to recreate the elements of it with neural networks.

这些AI模拟体之所以如此强大，不仅在于理解、近似或模拟世界本身，更在于它为我们提供了一个可大规模实验的虚拟世界，从而最终创造新知识。

And why these AI analogs are so powerful is that it's not just about understanding, approximating, or simulating the world for the sake of understanding, but this actually gives us a little virtual world that we can experiment in at scale to ultimately create new knowledge.

你可以想象，这种针对AI模拟体的实验也可以在计算机中进行，与其他智能体形成闭环的计算内开放式发现，最终产生我们能够带回现实世界并改变周围环境的新知识。

And you can imagine that this experimentation against our AI analogs, this can also happen in silico, in a computer, with other agents in a loop of in silico open ended discovery, ultimately to create new knowledge that we can take back out and change the world around us.

这并不是科幻小说。

And this isn't science fiction.

现在，我们有数千张显卡正在运行，训练我们自身微生物世界的基础模型，同时还有代理在探查这些AI模拟物，以设计可能成为新药的分子。

Right now, we have thousands of graphics cards burning, training foundational models of our own microbiological world, and then agents that are probing these AI analogs to design new molecules that could be potential new drugs.

我想向你们展示这个过程对我们而言具体是如何运作的，因为我相信它可以作为一个蓝图，推动AI驱动的科学与技术进步迎来全新浪潮。

And I want to show you exactly how this process works for us, because I believe it can serve as a blueprint to bring about a whole new wave of the future of AI driven scientific and technological progress.

现在，药物设计是一个非常重要的关注领域，因为实际上设计新药正变得越来越困难。

Now, drug design is such an important area to focus on because it's actually becoming harder and harder to design new drugs.

这是一张图表，显示了随着时间推移，每十亿美元研发投入所产生新药的数量。

This is a graph of the number of new drugs created per billion dollars of r and d spend over time.

你可以看到，新药的数量呈指数级下降。

And what you can see is that the number of new drugs is exponentially decreasing.

制造一种新药的成本正变得越来越高。

It's becoming more and more expensive to create a new drug.

在同一时期，我们见证了AI能力的巨大进步，这得益于一系列算法上的突破。

Now during this same time period, we've had a huge amount of advancement in the capabilities of AI, driven by a whole host of algorithmic breakthroughs.

但AI进步的一个隐秘来源也是摩尔定律——计算能力一直在随时间呈指数增长。

But one of the secret sources of this advancement in AI has also been that of Moore's Law, that the amount of computing power has just been exponentially increasing over time.

而如今，我们或许更应该关注的不是摩尔定律，而是詹森定律。

And these days, it perhaps isn't Moore's Law that we should care about, but Jensen's Law.

詹森·黄是英伟达的首席执行官，他推动了GPU算力的指数级增长，这些算力正为我们的神经网络提供动力。

Jensen Huang being the CEO of NVIDIA for the exponential increase in GPU flops that are now powering our neural networks.

那么，我们该如何将人工智能与机器学习的世界引入药物设计领域呢？

So, really, the question is how do we bring this world of AI and machine learning to that of drug design?

我们能否利用我们的AI模拟手段，逆转厄勒姆定律的诅咒，搭乘GPU算力指数增长的浪潮？

Can we think about using our AI analogs to reverse this curse of Erum's law and jump on this exponential wave of GPU flops powering our neural networks?

实际上，将这两个领域融合并推动这一变革，正是我每天所肩负的责任。

Actually, bringing these worlds together and driving this change is the day to day responsibility that I feel.

那么，我们该如何对生物学进行建模呢？

So how can we go about modeling biology?

如果我们身处物理学领域，比如宇宙，我们实际上可以亲手用数学写下大量理论，并非常精确地预测，例如数百万光年外宇宙的演化过程。

Well, if we were in the world of physics, for example, the universe, then we can actually write down a lot of the theory by hand with maths and very accurately predict, for example, the unfolding of the universe even millions of light years away.

但我们无法对自身内部极其复杂的动态过程做到这一点。

But we can't do that for the incredibly complex dynamics within ourselves.

我们不能只是为自己写下一些方程式。

We can't just write down some equations for ourselves.

我们或许可以写下原子如何相互作用的理论——那是物理学，但要在细胞内万亿级原子的尺度上模拟这些相互作用，完全是不可行的。

We can perhaps write down the theory of how atoms interact, that's physics, but then simulating these interactions on the scale of trillions of atoms within our cells is just completely unfeasible.

而且，我们还没有找到用更粗略、更简单的术语来描述这些复杂动态的方法，以便用数学写下来。

And then we haven't worked out how to describe these complex dynamics in coarser and simpler terms that we could write down with maths.

很难想象，我们能如此精确地模拟遥远的宇宙，却无法理解指尖下细胞的运作。

It's just crazy to think that we can model the universe so far away, but not the cells at our fingertips.

但人工智能和机器学习可以成为生物世界的完美抽象工具。

But AI and machine learning can be the perfect abstraction for a biological world.

通过我们从自身获取的零散数据，我们可以从神经网络的激活中隐式地学习到方程、理论和抽象模型。

Using the snippets of data that we can record from ourselves, we can then learn the equations and theories and abstractions implicitly within the activations of our neural networks.

事实上，我们的公司名为Isomorphic Labs。

In fact, our company is called Isomorphic Labs.

我们称之为Isomorphic，是因为我们相信，在生物世界与信息科学、机器学习和人工智能的世界之间，存在着一种同构性，一种根本性的对称关系。

Isomorphic because we believe there is an isomorphism, a fundamental symmetry that we can create between the biological world and the world of information science, machine learning, and AI.

所以，为了了解我们今天如何使用这些AI类比，我想深入人体，观察细胞并思考蛋白质。

So, to see how we're using these AI analogs today, I want to dive into the body and have a look into cells and think about proteins.

蛋白质是生命的基本构建单元之一，这些蛋白质在体内承担着不同的功能。

Now, proteins are one of the fundamental building blocks of life, and these proteins carry different functions in the body.

如果我们能够调节蛋白质的功能，那么我们就离开发新药不远了。

And if we can modulate the function of a protein, then we are well on our way to creating a new drug.

蛋白质由氨基酸序列组成，大约有20种不同的氨基酸，每种在这里用一个不同的字母表示。

Proteins are made up of a sequence of amino acids, and there are about 20 different amino acids, each one here depicted by a different letter.

氨基酸是一组原子组成的分子，这些分子连接成一条线性序列。

An amino acid is a collection of atoms, a molecule, and these molecules are joined together into a linear sequence.

蛋白质的功能不仅取决于这些氨基酸的序列，还取决于蛋白质折叠形成的三维结构。

And the function of a protein is not just due to the sequence of these proteins, but also due to the three-dimensional shape that these proteins fold up into.

我们体内有数千种蛋白质，每种都有其独特的序列和独特的三维结构。

And there are thousands of proteins inside of us, each with their own unique sequences and their own unique three d shape.

请记住，通过实验确定这种三维结构可能需要数月甚至数年的实验室工作。

And remember, trying to work out experimentally that three d shape can can take months or even years of lab work.

但随着2020年AlphaFold和AlphaFold2的突破，我们现在有了一个模型，它可以将氨基酸序列作为输入，非常准确地预测蛋白质的三维结构作为输出。

But with the breakthrough of AlphaFold and AlphaFold two in 2020, we now have a model that can take the sequence of amino acids as input and then very accurately predict the three d structure of a protein as the output.

这使我们能够填补已知蛋白质宇宙中的空白。

And this allows us to actually fill in the gaps of our known protein universe.

这就是我们的蛋白质AI模拟模型。

It's our AI analog of proteins.

蛋白质承载着它们的功能，但这些蛋白质并不会孤立地发挥作用。

So proteins carry their function, but these proteins, they don't actually act in isolation.

它们是更大的分子机器的一部分，这些蛋白质与其他蛋白质以及DNA、RNA和小分子等其他生物分子相互作用。

They're part of bigger molecular machines, with these proteins interacting with other proteins as well as other biomolecules like DNA, RNA, and small molecules.

例如，让我们放大来看这个蛋白质。

For example, let's zoom in and have a look at this protein.

这是一种修复DNA的蛋白质，它与DNA结合，夹住DNA，帮助促进修复，然后将修复好的DNA释放回细胞中。

This is a protein that repairs DNA and it interacts with DNA, clamping down on it, helping facilitate repair, and then the repaired DNA is released back out to the cell.

在药物设计中，我们的目标是让分子机器更好地工作，或者干脆阻止它们发挥作用。

Now, in drug design, what we want to do is either make molecular machines work better or actually stop them from working.

在这种情况下，对于癌症，我们实际上希望阻止这种特定的DNA修复蛋白发挥作用，因为癌细胞中没有备用的DNA修复机制。

And in this case, for cancer, we actually want to stop this particular DNA repair protein from working because in cancerous cells, there is no backup DNA repair mechanism.

所以如果我们阻止它发挥作用，癌细胞就会死亡，只留下健康的细胞。

So if we stop this from working, then cancerous cells will die, leaving just healthy cells remaining.

那么，针对这种蛋白的药物会是什么样子呢？

So what would a drug actually look like for this protein?

药物是一种能够调节分子机器的物质，它可能是一种进入人体、进入细胞后附着在该蛋白上的小分子药物。

Well, a drug is something that comes in and modulates a molecular machine, This could be a drug molecule that goes into the body, goes into the cell, and then sticks to this protein just over here.

这种药物分子实际上将DNA修复蛋白的夹子粘合关闭，使其无法有效修复DNA，从而导致癌细胞死亡，只留下健康的细胞。

This drug molecule actually glues the DNA repair protein's clamp shut, so it can't do effective DNA repair, causing cancerous cells to die and leaving just healthy cells remaining.

要完全理性地设计如此神奇的药物分子，我们必须理解所有这些生物分子元件是如何协同作用的。

Now, to design such an amazing drug molecule completely rationally, we'd have to understand how all of these biomolecular elements come together.

我们需要一个能够模拟所有生物分子系统的AI模型。

We would need an AI analog of all and any biomolecular systems.

今年年初，我们取得了突破性进展。

Early this year, we had a breakthrough.

我们开发了一个名为AlphaFold 3的新版本，能够以前所未有的精度模拟几乎所有生物分子的结合结构。

We developed a new version of AlphaFold called AlphaFold three that can model the structure of almost all biomolecules coming together with unprecedented accuracy.

该模型的输入包括蛋白质序列、DNA序列和分子原子。

This model takes as input the protein sequence, the DNA sequence, and the molecule atoms.

这些输入被送入一个基于Transformer的大型处理模块的神经网络。

And these inputs are fed to a neural network that has a large processing trunk based on transformers.

与处理一维序列的大语言模型不同，我们的模型使用一种称为‘配对形成器’的结构，作用于输入序列的二维相互作用网格上。

Now, unlike a large language model that operates on one dimensional sequences, instead our model uses what's called a pair former and operates on a two d interaction grid of the input sequence.

这使得我们的模型能够明确推理该生物分子系统中可能发生的每一对相互作用。

And this allows our model to explicitly reason about every pairwise interaction that could occur in this biomolecular system.

因此，我们可以利用这个处理模块的特征来条件化一个扩散模型。

And so we can use the features of this processing trunk to condition a diffusion model.

你可能知道扩散模型是这些出色的图像生成模型。

Now, you might know diffusion models as these amazing image generative models.

就像对图像的像素进行扩散一样，我们的扩散模型则是对生物分子系统的三维原子坐标进行扩散。

Now, just like diffusing the pixels in an image, instead our diffusion model diffuses the three d atom coordinates of our biomolecular system.

因此，这为我们创造了一个完全可塑的虚拟生物分子世界。

So now, this gives us a completely malleable virtual biomolecular world.

这是我们的人工智能模拟环境，我们可以像对待真实世界一样对其进行探索。

It's our AI analog that we can probe as if it's the real world.

我们可以更改输入内容、分子设计，并观察这些变化如何影响输出结构。

We can make changes to the inputs, changes to the molecule designs, and see how that changes the output structure.

让我们使用这个模型来为我们的DNA修复蛋白设计一种新药。

So let's use this model to design a new drug for our DNA repair protein.

我们可以取一种已被记录下来能与该蛋白结合的小分子，并对其设计进行修改。

We can take a small molecule that's been recorded to stick to this protein and make changes to its design.

我们希望改变分子设计，使这个分子与蛋白产生更多相互作用，从而增强其与蛋白的结合力。

We want to change the molecule design so that this molecule makes more interactions with the protein, and that will make it stick to this protein stronger.

因此，你可以想象，这为人类药物设计师提供了一个完美的游戏。

And so you can imagine that this gives a human drug designer a perfect game to play.

我该如何修改这个分子的设计，以产生更多的相互作用？

How do I change the design of this molecule to create more interactions?

通常情况下，药物设计师在设计过程的每一步都需要等待数月才能从真实实验室获得结果。

Now, normally, a drug designer would have to wait months to get results back from a real lab at each step of this design game.

但对我们而言，使用这个AI模拟器，只需几秒钟即可完成。

But for us, using this AI analog, this takes just seconds.

这正是我们伦敦的药物设计师目前正在实际进行的工作。

And this is the reality of what our drug designers back in London are doing right now.

因此，我们拥有一个美妙的游戏，我们的药物设计师正利用这种生物分子系统的AI模拟器，理性地设计潜在的新药物分子。

So we have this beautiful game that's being played by our drug designers who are using this AI analog of biomolecular systems to rationally design potential new drug molecules.

但你可以想象，我们不必仅仅将这个游戏限制在人类药物设计师身上。

But you can imagine that we don't have to just limit this game to human drug designers.

在我职业生涯的早期，我曾致力于训练智能体，使其在《星际争霸》游戏中击败顶尖人类职业选手，我们还为《围棋》和《夺旗》等游戏创建了游戏智能体。

Earlier in my career, I worked on training agents to beat the top human professionals at the game of StarCraft, And we created game playing agents for the games of Go and Capture the Flag.

那么，为什么我们不能创建智能体来玩人类药物设计师正在玩的这个游戏呢？

So, why can't we create agents that instead play the game that our human drug designers are playing?

因此，现在我们的AI模拟器成为了游戏环境，我们可以在此基础上训练智能体。

So, now, our AI analog becomes the game environment, and we can train agents against that.

我们目前已经拥有一些极其强大的代理，它们今天已经在做这件事了。

And we already have some incredibly powerful agents that are already doing this today.

现在，所有的药物设计都是在计算机上进行的。

Now, this setup, all of the drug design is happening on a computer.

那么，如果我们能使用大量计算机，会发生什么呢？

So what happens if we have access to many, many computers?

与其让一位人类药物设计师单独设计某种新分子，不如让成千上万个代理并行进行分子设计。

Well, instead of having one human drug designer working on some new molecule designs, Instead, we can have thousands of agents doing molecule design in parallel.

想象一下，这对患有罕见癌症的患者会产生怎样的影响。

Just imagine what impact that could have on patients suffering from a rare type of cancer.

我们能多快找到针对这种医疗需求的潜在新分子，或者同时应对多种疾病的能力。

The speed that we could get to a potential new molecule to address this medical need, or the ability to go after many diseases in parallel.

癌症通常由蛋白质突变引起，即使在同一类癌症中，每位患者的突变也可能不同。

Cancer is often caused by mutations of proteins, and even within the same type of cancer, each patient can have different mutations.

这意味着一种药物分子无法对所有患者都有效。

And that means that one drug molecule won't work for all patients.

但如果我们能够测量每位患者的蛋白质突变，然后让一群分子设计代理针对每位患者的特定蛋白质突变进行工作，会怎样呢？

But what if we could go in and measure each individual patient's protein mutations and then have a whole team of molecule design agents working on that individual's protein mutations?

那么我们就能为每位患者量身定制一种分子。

Then we could create a molecule tailored for each individual patient.

我这里只是展示这一点。

I'm showing just this.

在这里，蛋白质随机突变，每个红色标记的突变都会微妙地改变这种蛋白质的三维结构。

Here, the protein is randomly mutating, and each mutation in red subtly changes the three d shape of this protein.

我们能够生成能够针对这些变化与该蛋白质结合的分子。

And we're able to generate molecules that should stick to this protein in response to these changes.

不过，这距离实际应用于患者还很遥远，药物设计中仍有巨大的复杂性有待解决，但这确实让我们一窥即将到来的未来。

Now, this is still far away from patients, and there's a huge amount of complexity in drug design left to tackle, But this really does give us a glimpse at the future that is to come.

因此，我们已经看到这种新的AI范式正在推动药物设计的进步，你也能看到这一范式在材料科学、新能源开发以及化学领域中得到体现。

So we've seen how this new AI paradigm is driving our progression in drug design, and you can also see this paradigm being played out in material science and creating new forms of energy and in chemistry.

我们能够将真实世界中混乱复杂的现实，转化为我们自己的AI模拟模型，然后在计算机上进行开放式的科学探索，创造新的知识，并将这些知识带回现实，改变我们周围的世界。

The ability to take our real messy world and then create our own AI analogs to then, on a computer, do open ended scientific discovery to create new knowledge that we can take back out and change the world around us.

这是一个极其强大的范式，将带来一波全新的科学与技术进步。

This is an incredibly powerful paradigm and one that will bring about a whole new wave of scientific and technological advancements.

我们需要尽可能多的人参与进来，尤其是那些从事机器学习、人工智能和技术领域的人，来推动这一波新的进展。

And we're going to need as many people as possible, especially those working in machine learning, AI, and technology, to help drive this new wave of progression.

谢谢。

Thank you.

以上是马克斯·加德伯格在TED AI旧金山的演讲。

That was Max Gaderberg at TED AI San Francisco.

今天的分享就到这里。

And that's it for today.

TED Tech 是 TED 音频合集的一部分。

TED Tech is part of the TED Audio Collective.

本集由尼娜·伯德·劳伦斯制作，由亚历赫andra·萨拉扎尔剪辑，由朱莉娅·迪克森核对事实。

This episode was produced by Nina Byrd Lawrence, edited by Alejandra Salazar, and fact checked by Julia Dickerson.

特别感谢玛丽亚·拉迪亚斯、坦西卡、宋马尔·尼沃恩、法拉·德格伦、达尼埃拉·贝拉雷索，我是谢丽尔·多西。

Special thanks to Maria Ladias, Tansika, Sungmar Nivong, Farah Degrunge, Daniella Bellaresso, and I'm Sheryl Dorsey.

谢谢收听。

Thanks for listening.

在医生严重短缺的地区，如何提供医疗服务？

How do you provide health care in a place with extreme physician shortages?

2011年，苏丹成为一个独立国家。

In 2011, Sudan became an independent country.

我们是世界上最新的国家。

We are the youngest nation in the world right now.

我们感到被忽视了。

We feel neglected.

那是医生。

That's Doctor.

约翰内斯·赖希，他是社区里第一个成为医生的人。

Johannes Reich, who's the first in his community to become a doctor.

我作为医生对未来的期望，首先是建立一个健康的社会，这正是我们正在努力的目标。

My hope for the future, first as a doctor, is a healthy society, and that is what we are working for.

当美国现任政府上台时，我们收到了一个突然的通知。

When the current US government came into power, we got an abrupt notification.

明天，我们将暂停所有活动。

Tomorrow, we suspend all activities.

这立即对我们造成了影响。

So that had an immediate effect on us.

成千上万的社区无法获得基本的医疗服务。

Thousands of communities are not able to access basic health care services.

他通过自己的非营利性社区医疗组织‘Yo Care’，在重重困难下取得了非凡的成就，帮助南苏丹人民获得必要的医疗服务，并掌握自身健康需求的主动权。

With his nonprofit community health care organization, Yo Care, he's made heroic strides against great odds to empower the people of South Sudan to get access to integral medical care and take charge over their own health needs.

了解更多关于约翰内斯及其令人惊叹的故事，请收听我们最新一期的‘TED演讲每日秀’中的‘同伴星期五’访谈。

Learn more about Johannes and his breathtaking story in our most recent Fellow Fridays interview only on TED Talks Daily.