科学巨匠：大卫·贝克

BBC之声，音乐、广播、播客。

BBC Sounds, music, radio, podcasts.

大家好，欢迎收听《五维生命科学》。我是《裸体科学家》节目的克里斯·史密斯。接下来我们将展望2025年，邀请健康、人工智能、天文学、海洋科学和考古学领域的专家，谈谈未来十二个月值得期待的重要进展。

Hello. Welcome to five Life Science. I'm Chris Smith from the Naked Scientists. Coming up, a look ahead to the year ahead 2025. We'll be asking experts in the fields of health, AI, astronomy, marine science, archaeology what we should look forward to and expect to hear about in the next twelve months.

这里是五台直播的《裸体科学家》节目。

The Naked Scientists on five Live.

2025年，英国将出台严格新法规限制电子烟使用。目标是打造所谓'无烟一代'，其中重要举措包括禁止在英格兰、威尔士和苏格兰销售一次性电子烟。爱丁堡大学公共卫生教授琳达·鲍尔德将为我们解读。

In 2025, The UK is set to introduce strict new laws to restrict and control vaping. The aim is to create what's been called a smoke free generation, and a large part of that will include a ban on the sale of single use disposable vapes in England, Wales and Scotland. Here's Linda Bald, who's a professor of public health at the University of Edinburgh.

这项法案延续了上届英国政府的提案框架，保留了原有权限并新增修正条款。简而言之，核心措施是逐步禁止向2009年后出生者销售烟草，但法案还包含多项已通过初审的条款，正进入立法程序下一阶段，其中就包括电子烟监管。正如我们之前讨论的，电子烟是重大公共卫生问题，大量青少年正在使用。

So what the bill is going to do, it was previously proposed by the last UK government and has been brought back with the same powers, but also some additional amendments. So it's quite a long list, I'll be brief. I think the headline one is phasing out the sale of tobacco to anyone born after 2009, but there's also quite a range of powers in this bill which has passed its first readings, is starting that stage into the other parts of the legislative process, to also look at vaping. So vaping, as you and I have discussed before, is an important public health issue. Large numbers of young people are vaping.

因此法案旨在遏制青少年吸电子烟现象，措施包括：禁止吸引儿童的电子烟包装设计、叫停免费派发、严格规范烟油成分与口味、限制店铺陈列宣传方式。最后这部分与我研究密切相关——将未获医药许可的尼古丁产品（如俗称'瞌睡丸'的尼古丁含片）纳入监管范围，这些产品目前游离于现行电子烟法规之外。

So the bill is aiming to have powers to curb youth vaping, and that means doing things like promoting branding on vapes that appeal to children, some of the names and the colors, and stopping handing out free vapes, importantly, regulating vape contents and flavors, and also looking at how they're displayed and promoted in shops. And then the last bit of this, which is actually of particular interest to me in my research, is a whole bunch of nicotine products that are not medicinally licensed, that are not covered by our existing vaping legislation, like nicotine pouches, snoozes they're often called, and they'll fall within the bill as well.

这些替代性尼古丁产品是否被认为与电子烟和传统香烟具有同等健康危害？我们是否应该对它们网开一面，而集中解决更迫切的电子烟问题——特别是电子烟已悄然渗透青少年市场，培养了大量新的尼古丁成瘾者？

Are they thought to attract the same kind of health disbenefit as vaping and smoking? These other forms of nicotine use. Or should we be turning more of a blind eye to those and bearing down on the elephant in the room, which is the fact that vapes seem to have crept in, especially in the youth market, and seduced and basically hooked a whole legion of new nicotine addicts.

是的，他们确实如此。你说得完全正确。我们面临的是一个风险连续体。我是这样描述的。实际上，其他一些尼古丁产品，特别是尼古丁袋（俗称口含烟，虽然口含烟本指其他产品），我认为风险相当低，因为里面只有尼古丁，而且产品的包装方式也有所不同。

Yes, they have. And you're absolutely right. We have a continuum of risk. That's how I describe it. Actually, some of the other nicotine products, particularly nicotine pouches, so called snus, although snus is something else, but that's what people call them colloquially, I actually think are really quite low risk because all you've got in there is the nicotine and some of the other ways that the product is packaged.

这些都是我们已知无害的产品。它们也不像电子烟那样需要吸入——这正是我们对电子烟担忧较少的原因，虽然它比吸烟危害小得多，但仍存在我们尚未了解的风险。因此尼古丁袋可能不是我们需要过分担心的，但我觉得将其纳入立法（至少是部分措施）很有必要，因为它们极易上瘾。另一个问题是目前尼古丁袋中的尼古丁含量不受监管，甚至没有销售年龄限制。所以我们必须对尼古丁袋采取行动。

These are things that we know are not harmful. It's also not breathed in in the way you do with vaping, which is one of the reasons why we're concerned about vaping far less harmful than smoking, but still risks we don't understand. So nicotine pouches are probably not something we should be terribly worried about, but the reason I think it's good to bring them into the legislation, or at least some of the measures in the legislation, is because they're very addictive. And the other thing about the pouches is that the level of nicotine in them isn't regulated, at the moment, there's actually no age of sale. So we needed to do something on the nicotine pouches.

你说得对，如果追溯危害的源头——尼古丁替代疗法作为许可药物非常安全，但有些人可能会从它开始，逐渐转向口含烟，再到电子烟，最后发展到最致命的传统香烟。这些产品都伴随着不同程度的风险。

But you're right, if you look at where harm begins, nicotine replacement therapy, licensed medicine, very safe, but, you know, some people can get a bit hooked on it through to things like pouches, then through to vapes, and then through to the smoked cigarette, which is the most deadly. There's different risks associated with all of these products.

但我们在电子烟问题上是不是已经亡羊补牢为时过晚了？在我看来我们介入得太迟。它们已经像触手般深入年轻人群体。我们实际看到的使用率正在飙升，而我们现在甚至还没完全禁止这些产品。

Are we not slamming the door after the horse has bolted on the vapes though? Because it strikes me we're coming to this party so late. They've already got their tentacles into so many young people. And we've actually seen rates of up take go up, go flying up actually. And we're not even at the point where we've banned these things properly yet.

中国，就连中国都早在多年前就采取了行动。

China, even China did it ages ago.

是的。事实上我们已经有很多关于电子烟的法规，但显然还不够。问题在于产品形态在不断演变。我们已经禁止了大多数形式的电子烟广告，设置了销售年龄限制，有执法措施，限制了尼古丁含量，并要求电子烟标注健康警示。

Yeah. We do actually have a lot of legislation around vaping already, but clearly it hasn't been enough. And the problem has been that the products have evolved. So we've banned most forms of vaping advertising. We have an age of sale, we have enforcement, we have limits on the nicotine content, we have a health warning on the vape.

我们在2016年就完成了这些工作，但后来一次性电子烟进入市场——它们非常便宜、吸引人，正如其名可随意丢弃，对环境有害，并成为青少年使用率激增的主要推手。可以说我们早期确实做得不够，但现在采取额外措施仍为时不晚。我认为完全禁止可能效果有限。政府这次法案的目标是禁止向2009年后出生的儿童销售最致命的传统香烟。但像澳大利亚这样规定电子烟仅限处方购买的国家，青少年电子烟使用率几乎和我们一样高。

You know, we did all of that stuff in 2016, but then disposable vapes came on the market, which are very cheap, very attractive, disposable, as the name says, environmentally harmful, and they actually have been a major driver of youth uptake. So the horse has definitely bolted to the extent that we didn't do enough early enough, but I think taking additional action now will be okay. I would say banning them probably wouldn't be that successful. What the government aims to do with this bill is ban smoking in terms of selling it to children born after 2,009, that's the deadliest product. But countries like Australia, which have made vapes only available on prescription, have youth vaping rates almost as high as ours.

因此我认为关键在于采取相称的策略，努力领先于行业发展和产品迭代。我希望我们能成功，但必须保持现实态度。

So I think it's about proportional approaches and trying to keep ahead of industry evolution and the evolution of the products. I hope we can do it, but we have to be realistic.

完全不同的主题，但与健康相关。过去三十年里，各国政府陆续推出了约170项政策来试图控制肥胖问题，包括糖税在内。这必须是未来任何年份的健康优先事项，不是吗？毫无疑问。

Totally different topic, but health related. We've had various measures that have been brought in, I think something like 170 policies in the last about thirty years by successive governments of all colors to try to control obesity, sugar tax included in them. That has got to be one of the health priorities for any year in the years ahead, hasn't it? Surely.

确实如此。实际上，我认为2025年会出现一些变化，但对于我们公共卫生领域的大多数人来说还远远不够。以苏格兰为例，我们承诺将对垃圾食品的多件购买和临时促销采取立法行动。英国政府最终也将对网络和电视广告实施限制，特别是针对黄金时段的电视广告。问题在于我们的食品供应已经发生了变化。

Absolutely. And actually, think you're going to see some changes in 2025, but they're not going to be enough for most of us in the public health community. What you're going to see in 2025, if I just start with Scotland, we're committed to taking action and legislation on multi buys and temporary price promotions of junk food. The UK government is finally going to be bringing in its restrictions on advertising online and on television, particularly on TV, in relation to the watershed. The problem is our food supply has changed.

我们面对着实力雄厚的大企业，它们不仅改变了我们的饮食结构，还导致心血管疾病发病率等问题的变化。我们在控烟方面取得的进步正因超重和肥胖问题而大幅倒退。因此我希望2025年能看到更多行动。这是公共卫生的当务之急，我们必须开始应对。

We've got big, powerful companies and it's changed both our diet, but also things like rates of cardiovascular disease, which we know some of improvements we saw with productions in smoking are largely being reversed because of overweight and obesity. So I hope in 2025 we see more action. It's a public health priority and we need to begin to tackle it.

爱丁堡大学的琳达·鲍尔德为我们分享了她的2025年公共卫生愿望清单。现在转向科技领域，我们将展望未来十二个月人工智能方面可能出现的进展。今年我们预计将迎来OpenAI的ChatGPT五代的发布，这是驱动ChatGPT的AI语言模型的下一代产品。但它会如期而至吗？它又能实现哪些功能？

Linda Bald at the University of Edinburgh with her public health wish list for 2025. We're now to tech and we're going to look ahead at what we might expect to hear about from an artificial intelligence perspective over the next twelve months. This year we anticipate the release of OpenAI's ChatGPT five, the next generation of the AI language model that powers ChatGPT. But will it come to pass? And what might it be able to do?

接下来有请牛津大学机器学习专家迈克·沃德里奇为我们解读。

Here's Mike Waldridge, who's an expert on machine learning at the University of Oxford.

让我们回顾一下时间线。GPT二代是该系列首个重要版本，大约在2018、2019年发布。GPT三代是突破性产品，这是首个真正令人印象深刻、面向广大用户的大规模语言模型，这项技术最终应用于2022年11月发布的ChatGPT。

So just recall the timeline. GPT two was the first notable release in the in in the GPT series. That was around about about 2018, 2019. GPT three was the breakthrough. GPT three was the first really, really impressive, large scale, large language model that that met a large audience, and that was the technology basically that goes into ChatGPT, and ChatGPT is November 2022.

随后我们看到GPT-4迅速推出，所有人都在等待GPT-5。最近我与同事朋友进行了许多讨论——有时可能借着几杯酒劲——关于GPT-5的确切进展。我听到了截然相反却又都充满信心的观点：有人认为技术已遇瓶颈，GPT-5迟迟未现只因表现平庸。这种观点部分源于OpenAI CEO山姆·奥特曼的言论，他称'规模竞赛'带来的优势已不如GPT-3时代显著，这更让人怀疑GPT-5是因令人失望而雪藏。

Then we see GPT four rolled out fairly quickly after that, and everybody has been waiting for GPT five. Now I've had lots of conversations with colleagues and friends recently, possibly fueled by a few glasses of wine in some case, about where exactly GPT five is, and I've heard flatly contradictory, very confident, but flatly contradictory statements from from people about where they where they think it is. So one view is that the technology has flatlined and that GPT five, we haven't seen it yet because it just isn't that impressive. And the people who believe that, their their beliefs are fuelled somewhat by Sam Altman, the CEO of OpenAI, the company behind this technology, his statements that, you know, the race to scale, just making things bigger, was not delivering the same level of advantages that it did with GPT-three. And so that's fuelling this idea that actually GPT-five hasn't been released because it's a disappointment.

但同时也有同事斩钉截铁地表示，GPT-5必将震撼世界，OpenAI团队正在疯狂测试以确保其完全符合市场预期，待发布时定能惊艳全场。说实话我难以判断哪种说法属实，但毋庸置疑众人对其寄予厚望。

So at the same time, I've had very confident statements from colleagues that no, no, GPT-five really is going to be mind blowing, and it's just that OpenAI and Co are are busy frantically testing it to make sure that it's it's really, really, really fit for the market so that when it's released, it really does dazzle. So I honestly don't know which of those two statements is true, but a lot, I think, is riding on it because the expectations are so high.

你认为我们2025年能见到它吗？事实上你预见2025年会发生什么？考虑到当前投资规模和对多种技术的撒网式投入，若找不到'黄金应用场景'有些公司将难以为继——这场重大调整是否会在2025年到来？或是其他激动人心的突破？

Do you think that we will see it in 2025? And what do you in fact see in 2025? Is that big correction that we're considering must be on the cards with this level of investment, this sort of scattergun approach to to investing in so many of these technologies, which is going to bring some companies financially to their knees if they don't discover the golden nugget of this is the use case that everyone wants. Is is the reset coming in 2025 or or something else exciting?

我认为GPT-5必定会出现，未来一年内问世毋庸置疑。但其具体功能和能力范围仍是未知数。就目前而言，我猜测其语言能力不会有巨大突破，幻觉问题可能依然存在。

Well, I think we we will see GPT-five. I think it's unimaginable that we wouldn't see GPT-five in the next year. What it will do and what the scope of its capabilities will be is anybody's guess. My guess would be right now that its language capabilities will not be hugely advanced. I think it will still hallucinate.

它仍会犯错。发布几天后社交媒体上肯定会出现'看看GPT-5干的蠢事'这类言论。但同时它很可能会融合多模态能力——未来几年生成式AI的主战场将在多模态领域，不仅是图文结合。

I think it will still get things wrong. We will still start to see, you know, a few days after release, you will start to see on social media people say, look at this stupid thing that GPT-five has done. That's my guess is that will all be there. But, yeah, at the same time, I think what it will probably embrace is different modalities. So where I think that one of the big battlegrounds for generative AI is over the next over the next couple of years is around multimodal AI, and that means not just text, but text and images.

GPT-4已实现图文生成，但声音、音乐、视频等模态还有发展空间。比如上传视频后不仅能生成字幕，还能解释画面内容：'这个视频是男子打高尔夫时摔倒的场景'——这类功能现在虽不完美但已初步实现。

And of course, in GPT four, we've already seen text and images come together. But things like sound, music, video, being able to upload a video and actually having an explanation of what's going on in the video, not just a transcript of the voices in the video. We can do that now, not perfectly, but we can do that now. But actually, what's happening in the video. This is a video about a man taking a golf shot and falling over, you know, this kind of explanation.

多模态将是未来方向。或许明年就会出现能根据提示生成抖音时长视频的AI。抖音是当下最大社交平台之一，当具备这种能力时，我们将进入一个全新的娱乐纪元——音乐生成等所有模态都将被重新定义。

So multimodal, I think, is where it's really going to be. At some point in the next few years and maybe even next year, we're gonna have generative AI that will be able to take a prompt and generate a TikTok length video to order. You know, TikTok is one of the biggest social media platforms out there right now. And when we have that kind of capability, that we're in a different world, I think, of a new world, a completely new world of entertainment. So multimodal, all of those different modalities, you know, being able to produce music.

再次强调，我们已经见过可按需实现这一功能的原型系统。比如，我想要一首快乐分队与艾德·希兰的混搭曲目——类似这样的需求，系统就能生成快乐分队与艾德·希兰的混搭歌曲，或是快乐分队风格的艾德·希兰单曲。所有这些不同模式的融合将带来非常怪异又荒诞的创作，但这将引领我们进入一个全新的娱乐与媒体消费时代。

And again, we've seen prototype systems that can do this to order. You know, I would like a mashup of Joy Division and Ed Sheeran, you know, something like that, and produce a song that's a mashup of Joy Division and Ed Sheeran or an Ed Sheeran song in the style of Joy Division, something like that. All of those different modalities coming together. And what people are gonna do with that is gonna be very weird and very, very wacky, but it's gonna take us into a completely a completely new era of entertainment and media consumption.

牛津大学的迈克·威尔德里奇可能对快乐分队/艾德·希兰混搭不感兴趣，但这项技术本身依然令人印象深刻。2025年的太空旅行会有什么进展？答案很大程度上取决于特朗普新政府愿意投入多少资金。我们为此联系了太空科学家兼作家大卫·怀特豪斯。

Mike Wildridge from the University of Oxford and a Joy Division slash Ed Sheeran mashup might not be our cup of tea, but the technology is all the same, pretty impressive. What might be in store for space travel in 2025? Well, the answer very much hinges on how much Donald Trump's incoming administration wants to spend. We put in a call to space scientist and author David White house.

在特朗普政府眼中——这个不尊重传统、更看重成功而非传承的政府——埃隆·马斯克很可能会推翻NASA制定的许多方向和计划，尤其是在载人航天项目上。因为最新发布的研究展示了美国重返月球的计划：宇航员将先乘坐猎户座飞船抵达月球轨道，然后转乘星际飞船登陆月表，返程时再换乘猎户座飞船返回地球。当这个计划被可视化呈现时，我们看到了相关图像。

In the Trump administration, which is no respecter of tradition and for which success and achievement counts more than heritage, it may well be that Elon Musk is able to overrule many of the directions and programs that NASA has, particularly in its its human space program. Because there was, a study just released which showed how America plans to get to the moon. The astronauts at the moment is gonna gonna go to the moon on the Orion capsule. They're gonna transfer to the Starship, go down to the surface, and then come back back up again and transfer back to the Orion capsule to come back to Earth. When that plan was actually put in in visual terms, we saw pictures of it.

人们开始质疑：猎户座飞船如此小巧，星际飞船却如此庞大，这看起来很不协调，非常怪异。

People have started saying, but Orion is tiny. Starship is enormous. This doesn't look right. This doesn't look compatible. This looks very strange.

这要么是登月的正确方式，要么就是在暗示我们：如何将政府耗资巨大、周期漫长的登月计划与埃隆·马斯克更敏捷、快速且高风险的方式相结合。马斯克的太空发射次数已超过所有机构总和，他的星际飞船每年可发射五到十次，而负责运送猎户座飞船的太空发射系统可能每两年才能发射一次。人们逐渐意识到这两个系统的严重不兼容——尽管有人会说马斯克权力过大，但他可能是赶在中国之前重返月球的关键。

Now either that's the way to go to the moon, or it's telling us something about meshing the government expensive long term space effort to go to the moon with Elon Musk's nimbler, faster, more risky approach. Elon Musk has launched at more than anybody else put together into space. His Starship could be launched five, six, seven, eight, nine, ten times a year as you get going. The space launch system that launches the Orion capsule, that's to take astronauts to the moon, probably cannot launch more than once every two years. So these are two very incompatible systems that people are beginning to realize that perhaps even though power is too much with Elon Musk, one would argue, he might be the way to get back to the moon before the Chinese.

你的措辞非常有力，你说2025年将是自1969年以来人类航天史上最重要的年份。这个预言非常非常大胆。你认为2025年会发生什么来印证这个预言？

You've written some really powerful words because you said 2025 will be the most important year in human spaceflight since 1969. Very, very powerful. What do you think is gonna happen then in 2025 to justify those words?

我认为这是决策之年，关乎我们以何种方式重返月球。自登月计划后，我们主要依赖航天飞机和空间站，而众所周知航天飞机存在缺陷。埃隆·马斯克将面临与航天飞机相同的重大挑战——如何让宇航员安全乘坐星际飞船。因为星际飞船本质上是太空舱，而他似乎打算快速推进载人飞行设计。

Well, I think this is the year of decisions. This is about how to go back to the moon. Because since since we've been to the moon, we've had basically the space shuttle and the space station, and the space shuttle we know was a flawed system. And we're gonna have to face Elon Musk's one of his big tasks is he's gonna have to face is the same problem as the space shuttle had with with putting people on board his Starship. Because the Starship is a is a capsule, and when he does put people on board, and it seems that he wants to put you know, design it for a crewed flight relatively quickly.

他的主要问题在于，目前似乎缺乏一种紧急中止系统——宇航员位于舱内，而火箭与舱体并排相连，就像航天飞机那样。要知道，当挑战者号爆炸时，机组人员根本无路可逃。因此他必须解决载人星际飞船的安全问题，这关乎重返月球的计划。按当前方案，宇航员需转乘星际飞船登陆月球表面并返回。无论他具备将星际飞船送往太阳系何处的能力，都必须解决这些安全隐患，这将是极其困难的。我们预计未来几年会向火星发射无人飞船。

His main problem is that at the moment, there doesn't seem to be an abort system in the sense that the crew are inside the capsule, and the rocket and the capsule are next to each other as was the space shuttle. And that, you know, when Challenger blew up, the crew had no way to get out. So he has got to address when he puts people on board the Starship. And that's part of going back to the moon because at the current plan, the crew will transfer to the Starship to go down to the moon's surface and come back again. He's got to work out the safety issues of that, and that is going to be very difficult no matter what his ability to send the Starship around the solar system, and we expect to send him sea send him send an uncrewed one to Mars in the next couple of years.

这是他必须面对的根本性人身安全问题。但在特朗普政府时期，成功就是一切。关于重返月球的决策——包括美国使用何种舱体及如何探索太阳系——很可能在今年敲定。目前有正在研发的星际飞船，这种多功能强力飞船或将载人进入太空、登陆月球甚至火星；还有SpaceX的龙飞船负责空间站人员往返；波音的星际客机飞船则因推进器问题，今年早些时候未能从空间站接回宇航员，这些问题至今仍未解决。

That is a fundamental human safety problem that, he is going to face. But success is everything at the moment with the Trump administration, and the decisions for going back to the moon for what capsules America uses and how it explores the solar system will probably be made this year. Because at the moment, you have you have the Starship, the up and coming, what could be a very versatile, very powerful spaceship to take people into space, to the moon, and possibly onto Mars. You have got, the Dragon capsule, which is also SpaceX that takes people up to and from the space station. You've got Boeing's Starliner capsule, which, had all these problems and wasn't able to bring the the crew back from the space station earlier this year because they had problems with the thrusters, and those are still not resolved.

此外还有昂贵但性能强大的猎户座飞船，它将运送宇航员登月，但目前其隔热罩存在复杂问题。因此美国航天项目的监管者和特朗普会对马斯克说：我们有星际飞船、星际客机、龙飞船和猎户座四种载人航天方案，真的需要这么多吗？SpaceX已证明其高效节约的能力，某些项目必须被淘汰。

And you've got the expensive and capable Orion capsule that is going to take astronauts to the moon and, is complicated and has problems with its heat shield at the moment. So the those overlooking the American space program and and Trump will say this to Musk. Musk will say, how come we have got Starship, Starliner, Dragon, and Orion? Do we need four ways to put American astronauts into space? Surely, with wanting to save money and be more efficient and achieve more, which is what SpaceX has shown us it can do, something's got to go.

因此我认为未来三年将做出重大决策，这将设定重返月球的技术路线和目标。但实际登月时间可能比现在预期的要晚得多，火星计划亦是如此。

So I would argue big decisions this year, next year, and the year after. And that will set the agenda and the goal, the technological direction to go back to the moon, which I think is gonna be much later than we thought now, and possibly onto Mars.

大卫·怀特为我们展望了2025年的太空前景。而即将到来的这一年预计将打破高温纪录——这已是近年来的趋势。但这对海洋领域会产生什么影响？人们是否正在采取行动保护我们和海洋生物免受气候变化的最严酷影响？

David White house watching this space for us and what lies ahead and overhead in 2025. Now the year ahead is predicted to be the hottest on record, which has been something of a trend in recent years. Let's face it. But what effect do we anticipate it having on the marine realm? And what, if anything, are people doing to protect us and marine life from the harshest of the effects of climate change?

威尔·廷格尔采访了海洋科学传播者、《Out of Our Bumble》播客主持人莉伯蒂·登曼。

Will Tingle's been speaking to marine science communicator and host of the Out of Our Bumble podcast, Liberty Denman.

我认为全球目光都将聚焦在联合国气候变化大会（COP）上——不仅是气候大会，还包括生物多样性大会。过去几年无论是会议成果还是举办地都遭受了很多批评。我们最终期待看到的是实际行动，在生物多样性和气候两方面，看到各国拿出政治勇气推进变革。我认为保护工作在政治领域遭遇了太多阻力。

I think it's going to be all eyes on COP. And that's not just the COP for climate, but also for biodiversity. This is something that I think they've suffered a lot of backlash, both in the contents of what comes out of COP, also the locations that they've been hosted in the last couple of years. I think ultimately what we're waiting to see is that action, both the biodiversity and the climate side of it and seeing the countries having the political courage to make the moves to do that. I think conservation suffers so much in the political spaces.

政治任期短暂，因此政客们往往只考虑短期利益，但遗憾的是自然界和环境保护并不遵循四年或两年的任期周期——这取决于你关注的具体领域。我们需要的是长远视角。不幸的是，我认为这正是当前症结所在——当政策不断变动时，我们很难看到长期影响。因此我认为2025年海洋保护的重点应该在于建立长期保护机制，无论是通过海洋保护区框架对特定海域进行保护，还是针对不同区域、不同物种制定专项保护措施，都应根据实际需求将我们的注意力转向这些领域。

Political terms are short and so politicians think short term, but unfortunately the natural world and conservation doesn't work on four year terms or two year terms, depending on where you're looking. It's something that needs to have that long term view. And unfortunately, I think that's something that it really suffers from because when you're constantly changing things, it's very difficult to see that long term impact. So I think that's really where we want to be looking for the ocean is seeing that commitment to long term protection, whether it's for specific areas of ocean under that kind of MPA framework or targeted areas for specific species protections for different areas, different species, depending on what's necessary, I think is really where we want to be turning our attention to in 2025.

每次讨论环境保护时，房间里的大象永远是气候变化。有人预测2025年将再次成为有记录以来最热的一年——在这个阶段这种预测几乎显得毫无意义。

And the elephant in the room, whenever you talk about conservation is, of course, climate change. 2025, some people predicting it is going to be, again, the hottest year on record. That kind of feels meaningless at the stage.

我们年复一年地打破自己的纪录。我们太喜欢这种自我竞赛了。

Consistently beating ourselves every year. We love the competition with ourselves.

虽然答案显而易见，但气候变化究竟会对海洋产生什么影响？

I mean, I feel like this is an obvious yes, but how will that potentially have impacts on the ocean?

想象你被困在滚烫的浴缸里会怎样？你会大汗淋漓、感到恶心、脱水？可能还有我没想到的其他反应。海洋生物面临的正是同样的处境。

I mean, let's just imagine you're stuck in a bath where the water is really, really hot. How's it gonna impact you? Are gonna be sweating, feeling a bit sick, dehydrated? Probably also a load of other things that I'm not thinking about. But ultimately it's that same concept.

海洋中的动物和生态系统无处可逃，而海水正在变暖。具体表现会因海域和物种不同而差异显著——总体而言，海水升温会导致海平面上升，同时引发海水化学变化，使甲壳类动物难以维持外壳。

The animals and ecosystems within the ocean can't go anywhere, and that ocean is warming. And that can look very different depending on what areas you're looking at or what species you're looking at. I mean, generally speaking, as the ocean warms, that will cause a sea level rise, but it will also cause a change in the chemistry, making it hard for things like crustaceans and crabs to maintain their shells.

请允许我暂时戴上悲观主义者的帽子——显然，我

And to put my pessimist hat on just for a moment, obviously, I'm

阳光，只为一瞬。

sunshine Just for a moment.

我大多时候都如阳光彩虹般灿烂。但当我们谈论海洋保护区，并在回顾2024年时对其赞不绝口，面对气候变化这头巨兽即将造成的破坏，它们又如何能指望弥补哪怕一小部分呢？

Just I'm sunshine and rainbows for most of the day. But when we talk about marine protected areas and we wax lyrical about them in the look back at 2024, how can they even hope to reconcile a fraction of the damage that we know is going to be caused by this behemoth of climate change?

我的意思是，如何定义需要保护的脆弱区域，这又完全取决于那个经典的科学推诿答案——视情况而定。取决于你想保护什么。但概括来说，我们真正讨论的是基于自然的解决方案，这确实是我们开始关注的方向。特别是在疫情期间我们意识到，当我们放手不管时，大自然反而开始复苏。

I mean, how you define, like, a vulnerable area to protect does, again, completely depend on the classic science cop out answer. It depends. Depends on what you want to be protecting. But to cover the broad strokes, what we're actually talking about is the fact that nature based solutions are actually really where we're starting to look. As we kind of realised, especially over COVID, as we kind of left things alone, nature kind of started coming back.

当时有各种头条新闻，比如我们在这些河流中看到了海豚，各种生物开始重新出现，因为我们减少了对这些区域的影响——每天外出时间不超过一小时。这确实向我们展示了：基于自然的解决方案，只要我们以这种方式不去打扰自然，它就能恢复。问题在于如何在一个不受疫情限制的社会中实现这一点。显然海洋保护区是其中至关重要的一环，只需划定区域任其恢复，就能产生溢出效应惠及未受保护区域，这意味着我们将提升渔业能力、发展生态旅游、或许还能获得更多资源。因此归根结底，我们需要做的是推动跨部门协作，展示保护这些区域的共同利益及其对所有人的价值。

There was all those headlines like we're seeing dolphins in these rivers and all of these different things were starting to reappear as we kind of receded our impact on those areas because we weren't allowed outside for any more than an hour a day. And so I think what that really did show us is that nature based solutions, and if we can leave nature alone in that way, it will recover. It's just how do we actually make that work in a society that isn't functioning under the power of COVID. So Marine Protected Areas are obviously a super important part of that, just designating an area to leave alone to recover, because then that allows the spillover effect into those areas that aren't protected, which means that we will have increased fishing abilities, increased eco tourism, perhaps more resources to use all of these different things. And so ultimately that's what we need to be doing is for that collaboration across different sectors, showcasing the mutual benefit of protecting these regions and how it's going be useful to everyone.

同时要明白，并非只有从事海洋保护工作才能为此做出改变。蓝色金融正日益成为我们讨论的话题——如何向长期资金短缺的保护领域注入资金？正因为缺乏同等经济效益，保护海藻林和海草床就比拆除红树林建造酒店困难得多。

And also understanding that you don't just have to be in Marine conservation to be able to make a difference in this. You know, blue finance is becoming increasingly something we speak about. How can we actually inject money into the conservation sector that is so chronically underfunded and therefore difficult to protect because it doesn't have the same financial benefit. Protecting kelp forests and seagrass doesn't have the same financial benefits. It does tearing mangroves down for a hotel.

因此我们需要找到那个平衡点，既能持续从自然中获取日常所需利益，或许还能为此赋予金融价值，以便用西方世界的价值观体系来理解这种保护。

And so that's we what need to understand is where that middle ground is so we can ultimately be benefiting from nature in the way that we do every day and perhaps even putting a financial value on that so that we can kind of understand that in the Western world of how we hold value.

所以这将需要大量的合作与凝聚力。

So a great deal of cooperation and cohesion is going to be required.

确实如此。我认为我们最需要的正是未来的协作。毫无疑问。

Definitely. I think that is gonna be the biggest thing that we all need is collaboration moving forwards. Absolutely.

刚才听到的是利伯蒂·登曼与威尔·廷格尔的对话。最后，我们将展望2025年的考古学。正如之前提到的，人工智能将在多个科学领域再次迎来重大突破。那么它在考古学中究竟能带来多大变革？我前往剑桥大学采访了艾玛·波默罗伊，了解这一情况并听听她在2025年还将开展哪些工作。

Liberty Denman in conversation with Will Tingle there. Finally, we're gonna look forward to archaeology in 2025. As we heard earlier, AI is set to have another huge year across many scientific fields. So how much of a game changer is it in archaeology? I went to see Emma Pomeroy at the University of Cambridge to find out that and to hear what else she's getting up to in 2025.

我认为这是非常激动人心的进展，相信未来几年我们会看到更多此类成果。本质上是通过使用激光雷达等技术——即利用光线从地面反射的原理，当你飞越某区域或派遣无人机时，它几乎能让我们看到以往无法观测到的事物。这种通过气球搭载相机进行航拍的技术在考古学中已有很长历史，可以让我们看清地面结构。但在某些地区实施起来确实困难，比如植被茂密的热带雨林，或是难以抵达的高海拔地区。

I think this is a really exciting development, and I'm sure we'll see more of this in the years to come. But essentially by using things like LIDAR, so where you're using kind of light, how it kind of bounces back from the ground when you're sort of flying over it or sending a drone over a particular area. It can almost let us see things that we couldn't see before. So using this sort of technology where you send up a balloon with a camera or something, I mean, that's been done for a very long time in archeology, and then you can see structures on the ground. But some places that's really hard to do, for example, in tropical forests where you've got very heavy plant coverage, or for example, at high altitude where it's not so easy to get there.

已有许多引人入胜的研究运用这项技术穿透热带雨林或探索人迹罕至的区域，向我们揭示了规模超乎预期的遗址，这些发现远超我们以往的预测和研究能力。

And there's been a number of really interesting studies that have used this technology to kind of see through those tropical forests or to look at places that are much more inaccessible and show us really exciting things about sites of kind of scale that we wouldn't necessarily have predicted or or been able to study before.

你2025年的工作计划是什么？通常你都会去些异域之地挖掘古物。今年也是类似安排吗？

And what's in your diary for 2025? You've normally got somewhere exotic to go and something ancient to dig up. More of the same?

是的。我们希望能重返伊拉克库尔德斯坦的沙尼达尔洞穴进行更多发掘工作。过去十年间我们在此开展了大量工作，不仅发现了尼安德特人遗骸，更致力于研究尼安德特人与更晚近的现代人类行为。我们计划重返该地，重点研究尼安德特人使用洞穴与现代人类首次出现之间的过渡期，试图厘清这种交替的具体形态——他们是完全独立存在的吗？

Yes. So hopefully, we will be going back to Shanidar Cave in Iraqi Kurdistan for more excavations there. We've been working extensively over the past kind of ten years now, finding Neanderthal remains, but also trying to understand the behavior of Neanderthals and more recent modern humans. And we're hoping to go back there to focus on this period where we have a transition between Neanderthals using the cave, and the first modern humans turning up, to really try and understand what that sort of transition looks like. Are they there completely separately from one another?

他们是否存在时间上的重叠？或是交替使用洞穴？这就是我们新一年的重要研究目标。

Are they perhaps overlapping in time, alternating in their use of the cave? So that's one of our big targets for the coming year.

这里是剑桥大学的Emma Pomeroy。现在是新闻和体育时间，之后《科学巨人》将继续为您带来帮助破解化学与生物学中最棘手问题之一的人物——他成功预测了蛋白质的形状，包括那些决定我们身体形态与功能的蛋白质。这位2024年诺贝尔化学奖得主、生物化学家David Baker，欢迎回到《生命科学五分钟》，我是Chris Smith。今天与我对话的是诺贝尔奖得主、生物化学家兼蛋白质专家David Baker。

Emma Pomeroy at the University of Cambridge there. Well now it's time for the news and sport, but after that, titans of science continues with the man who helped to crack one of the hardest problems in chemistry and biology, and that's predicting the shapes of proteins, including the ones that make our bodies look and work the way that they do. He's the twenty twenty four chemistry Nobel laureate and biochemist David Baker. Welcome back to five Life Science with me, Chris Smith. Today I'm in conversation with Nobel Prize winning biochemist and protein guru, David Baker.

David Baker于1962年10月6日出生于西雅图，父母都是科学家。他就读于该市的加菲尔德高中，随后在哈佛大学攻读生物学。毕业后，他开始研究蛋白质在细胞内的运输机制。后来，他开创了设计蛋白质及预测其三维结构的方法，这为他赢得了2024年诺贝尔化学奖的殊荣。David联合创办了多家生物技术公司，并入选《时代》杂志首届'全球健康领域最具影响力百人榜'。

David Baker was born to two parents who were themselves both scientists in Seattle on the 10/06/1962. He attended Garfield High School in the city before he read biology at Harvard University. On graduation, he then began working on how proteins are transported around cells. Later, he would go on to pioneer methods to design proteins and predict their three-dimensional structures, and that helped to earn him a share of the 2024 Nobel Prize in chemistry. David's co founded several biotechnology companies, and he was included in Time Magazine's inaugural list of the 100 most influential people in health.

他现在是华盛顿大学蛋白质设计研究所的所长。欢迎来到节目，David，祝贺你获得诺贝尔奖。你是如何对科学产生兴趣的？

He's now the director of the University of Washington's Institute for Protein Design. Welcome to the show, David, and congratulations on your Nobel Prize. How did your interest in science get started?

说来可能有些意外，我对科学产生兴趣相对较晚。我并非那种从小就对科学着迷的人。事实上，刚进大学时我最初申报的是社会研究专业，后来对哲学产生了兴趣。直到大学最后一年，我才决定转向科学并专注于生物学。

Well, perhaps surprisingly, I really became interested in science relatively late. I'm not one of those people who's fascinated by science from an early age. In fact, when I got to university, I initially declared my major to be social studies, and later I got interested in philosophy. And it was really not till my last year of college that I decided to switch to science and focus on biology.

但说到蛋白质——我记得学生物时老师对蛋白质的痴迷，当时我还不太明白其中的奥妙。为什么蛋白质对你如此重要？是什么吸引你投身这个领域？

But proteins. I mean, I remember my biology teacher at school, I wasn't very old, obsessing about proteins. I really didn't get what all the fuss was about. Why did they matter to you? What drew you into that?

而且为什么蛋白质对所有人都很重要？

And why do they matter to anybody?

其实我上大学时也完全不了解蛋白质，直到选修了生物课。虽然觉得有趣，但真正着迷是多年后的事。让我告诉你原因：自然界中，生物体、动物和人类能完成各种惊人的事情。若仔细观察这些过程，你会发现核心都是蛋白质在发挥作用。

Well, in fact, at the time I was in university, I had no idea what proteins were either until I took a biology class. It seemed interesting to me, but it wasn't until quite a few years later that I really started becoming obsessed. And I'll tell you why. In nature, biological organisms, animals, humans do all kinds of really amazing things. And if you look in detail how those things are accomplished, at the heart of everything are proteins.

这些是专门的蛋白质，在我们思考和说话时调节大脑中的电流。它们让我们能够活动，使植物能够捕获太阳能并用于制造分子。基本上，生物学中的所有活动都由蛋白质完成。生物体的运作方式就是由成千上万种不同的工作组成的。

So they're specialized proteins that mediate the electric currents through our brains while we're thinking and talking. They're proteins that allow us to move around. They're proteins that enable plants to capture solar energy from the sun and use it to make molecules. Basically everything that goes on in biology is done by proteins. So the way that biology works is there are all these different jobs, thousands and thousands of jobs in any organism.

每一项工作都有特定的蛋白质负责。你可以把蛋白质想象成微型机器，它们完成了生命中所有重要的事情。

And for each job, there's a specific protein. So you can kind of think of proteins as the miniature machines, which do all the important things in life.

它们的结构是怎样的？我该如何识别它们？

And what's their structure? How would I recognize one?

即使你看到蛋白质也认不出来，因为它们极其微小。直径仅有一纳米，这意味着需要一万亿个蛋白质才能凑成一米。但每个蛋白质都有非常明确的形状，这是蛋白质最神奇的特性之一。就像我们现实生活中遇到的机器，每台机器都有明确的形状，这对它们的功能至关重要。

Well, you wouldn't recognize one if you saw it, because they're extremely small. They're just one nanometer across, which means that you need a trillion of them to get to meter. But each protein has a very well defined shape. That's one of the really kind of miraculous things about proteins. If you think about the machines that we're used to encountering in real life, each machine has a very defined shape, which is really important for it to do its job.

就像汽车有轮子才能滚动，有内部空间才能载人。同样地，每个蛋白质都有明确的形状，正是这些形状让蛋白质能够发挥它们的功能。

Like a car has wheels so it can roll and it's got an interior compartment you can get into. And in the same way, every protein has a very defined shape. And those shapes are really what lets the proteins do what they do.

但它们是如何形成这种形状的呢？

How do they come by that shape though?

正如我所说，蛋白质负责我们身体和所有生物体的全部工作。制造蛋白质的指令存在于我们的基因组DNA中。基因组DNA规定了每种蛋白质的化学结构，即氨基酸的序列。蛋白质由氨基酸构成，共有20种不同类型的氨基酸，一个蛋白质是由大约100到500个氨基酸组成的线性链。

Proteins carry out all the work in our bodies and all living things, like I said. The instructions for making proteins are in our genomes, in the DNA. And so the DNA in our genomes specifies what the chemical structure of each protein is, that is what the sequence of amino acids is going to be. Proteins are made out of amino acids. There are 20 different types of amino acids, and a protein is a linear chain of about 100 to 500 amino acids.

蛋白质的氨基酸序列完全决定了其形状，正如我所说，这个序列是由我们基因组中的基因所指定的。

And the sequence of amino acids of a protein completely determine what its shape is, and that sequence, as I said, is specified in the genes in our genomes.

这些氨基酸在化学性质上各不相同，因此它们可以形成不同的形状、结构、尺寸和电学行为。这意味着根据你插入的氨基酸类型不同，就像用不同形状的砖块砌墙一样，你会得到不同形状、颜色或具有特殊功能的蛋白质结构。

And those amino acids are all different chemically, so they can have different shapes, different structures, different sizes, different electrical behaviors, and so that means depending upon which ones you slot in. It's a bit like building a wall with different shaped bricks. You're gonna get a different shaped wall or a different colored wall or a wall with interesting properties if you put different amino acids into it.

是的，这个比喻非常贴切。就像拥有一个通用积木套装，通过氨基酸的正确组合，你基本上可以构建任何形状。而且不仅仅是形状，蛋白质机器还需要以正确方式与其作用对象互动。通过在蛋白质表面布置特定类型的氨基酸，就能使其与其他蛋白质或DNA相互作用，完成生物学使命。

Yes. That's a very good analogy. It's kind of like having a universal building block kit, kind of like a child's construction toy, where you can basically make any shape by having the right combination of amino acids. And it's not just the shape, a machine also has to interact with whatever it's operating on in the right way. So by having certain types of amino acids on the surface of a protein, that will enable it to interact with other proteins or with DNA and carry out the job it's meant to do in biology.

当你开始研究这些问题时，距离沃森和克里克发现DNA功能已过去近半个世纪——DNA存储着指导细胞制造这些神奇蛋白质的密码，而这些蛋白质将主导生命活动。当时是什么吸引了你？有哪些未解之谜让你想要深入探索？

By the time you were obviously doing your studies and getting into these sorts of questions, it had been nearly half a century since Watson and Crick worked out what DNA did, and that it was storing the code that told cells how to make these magical things, proteins, that were going to go on to dominate your life. So what was it at the time that intrigued you? What was the unknown or the unanswered that you wanted to explore further?

上世纪60年代末或70年代有个重要发现：如果你将蛋白质拆解，它会重新折叠回原来的形状。这确实证明了蛋白质形状由其氨基酸序列决定。但没人知道这个过程如何运作——从氨基酸序列到三维结构的编码机制是什么？这个折叠过程的本质是什么？多年前我加入华盛顿大学任教时，就被这个问题深深吸引。

There was a very important observation made in the 1970s or late 60s, which was that if you took a protein and you pulled it apart, it would fold back up to the same shape. And that was what really proved that the shape of a protein was determined by its sequence of amino acids. But nobody knew how that worked, how that code for going from amino acid sequence to three-dimensional structure worked. No one really understood what this folding up process was. And I became fascinated about that when I joined the University of Washington as a professor many years ago.

我认为它迷人的原因在于这是生物学中最简单的自组织案例。想想我们周围的无生命物质，它们没有组织性，完全是随机的。但当你观察宠物、兄弟姐妹，动植物都具有高度组织性——这正是它们与无机世界的区别所在。

I think I found it fascinating because it's kind of the simplest case of self organization in biology. So if you think about all the non living things around us, they're not really organized, they're kind of all random. But then you look at your pet, a dog or a cat, or your brother or sister. Animals and plants are really highly organized. That's how they're different from the rest of the world.

蛋白质是最典型的例子：由数千个原子组成，按理说应该呈现完全随机的混乱形状，但实际上它们只形成一种特定结构。从这个角度让我深感着迷。正如我所说，蛋白质执行着生命体的所有重要功能。我想如果我们能理解蛋白质折叠机制，未来或许能创造新型蛋白质，这将开启无限可能。

Proteins are really the simplest case, because proteins are made out of thousands of atoms, and you would expect them just to be completely random jumbles of different shapes, but yet they just have one shape. And so I was really fascinated about it from that point of view. And also, like I said, proteins carry out all the important jobs in living things. And so I thought if we could understand how proteins fold up, we might be able to actually make new proteins at some point, which could then have huge number of possibilities.

所以我们当时会想，好吧，我需要一个原子尺度的微型机器来完成X任务，我认为它必须具备Y形状才能实现，因此我会设计一种定制蛋白质来实现这个目标。这大致就是我们当时心中的终极目标。

So we would think, well, I want to have a miniature machine at the scale of atoms capable of doing job X, and I think it would have to have shape Y to do that, and therefore I would design a bespoke protein that would do that. That was sort of the end goal we had in mind at the time.

是的，正是如此。

Yes, exactly.

那么，是什么阻碍了你们实现这个目标呢？

Well, what was stopping you doing that?

这个过程非常复杂——从氨基酸序列到三维结构，或者从三维形状反向推导出编码它的氨基酸序列。正如我所说，蛋白质含有成千上万个原子。我们开始研究这个问题时，尝试模拟所有这些原子之间的相互作用。为了理解蛋白质折叠的过程，我们开发了模拟这种超长蛋白质链折叠的方法，并受到这数千个原子间相互作用的引导。我们在这个问题上取得了一些进展。

Well, it's very complicated, the process of going from amino acid sequence to three-dimensional structure, or three-dimensional shape, or going backwards from a three-dimensional shape to a sequence of amino acids that will encode it. As I said, proteins have many thousands of atoms. And so the way we started working on this problem was to try and model all of the interactions between all of those atoms. So to think about the process of protein folding up, we developed methods for actually modeling that folding up of the very long protein chain and guided by all these thousands of interactions between atoms. And we were able to make some progress on that problem.

后来我们意识到可以逆向操作：先确定一个全新形状，然后反向推算出能折叠成该形状的原子组合和氨基酸序列。大约二十年前，我们成功设计出了能折叠成新形状的氨基酸序列。这真正打开了无限可能的大门——既然我们能从零开始创造新形状，理论上就应该能设计出新功能。对于任何你希望蛋白质完成的工作，我们都应该能设计出相应的蛋白质来实现。

And then we realized we could go backwards and take a brand new shape and go backwards to figure out what combination of atoms and what amino acids would have the property that they would actually fold up to that shape. We were actually able to do that about twenty years ago and design a new amino acid sequence that folded up to a new shape. And that really opened the door to a lot of possibilities, because once we could make new shapes completely from scratch, it seemed like we should be able to design new functions. Given any job that you might want a protein to do, we should be able to design a protein to do it.

这类研究之所以困难，是否因为所有原子都各不相同——不同尺寸、不同质量、不同电荷？这意味着即使只考虑两个原子a和b，你很容易理解它们会如何粘附、吸引或排斥。但当涉及数千个各自独立行动的原子时，就需要考虑成千上万种可能性。这就是问题难以解决的原因吗？

Is it tricky to do this sort of thing because all these atoms are different. They're different sizes, different masses, different electrical charges. And that's gonna mean that if you just had two atoms to consider, a and b, and you could quite easily understand how they would probably stick to each other or want to attract or repel each other. Once you've got thousands of them, and they're all doing their own thing independently, you've got thousands and thousands of possibilities to consider. Is that why it's a difficult problem to solve?

没错，这是原因之一——原子数量庞大且组合方式繁多。另一个挑战在于，我们并不完全清楚这些原子或氨基酸相互作用的精确细节。因此在进行这些计算时会出现误差——由于原子数量太多，必须借助计算机完成。每个原子都可能处于无数不同位置，我们不断改进对原子间相互作用的描述，尝试更多不同的原子组合。这就是我们能够设计更复杂蛋白质的原因。

Yes, that's one of the reasons, because there's so many of them, and there's so many different possible combinations. The other thing that makes it challenging is we don't know exactly the details of how those atoms or amino acids interact with each other. And so for that reason, there are errors when we do these calculations, which have to be done on a computer because there so many atoms. And so there's so many different possible places each atom could be, and the interactions between the atoms we kept improving our description of those interactions on our ability to try out more and more different combinations of atoms. And that's why we were able to make progress in designing more complicated proteins.

这听起来确实像是需要高性能计算才能更好处理的事情，因为你可以让计算机考虑所有不同的可能性，虽然可能需要一些时间，但这不正是我们建造超级计算机的目的吗？就是为了能进行大量计算并模拟这类事情。

It does sound though like something that would be more tractable with heavy duty computing, because you can ask a computer to consider all of those different possibilities, and it may take a while, but wasn't that what we built supercomputers to do, to be able to do loads and loads of calculations and model this sort of thing?

这正是我们的思路。实际上，我们启动了一个名为'罗塞塔@家'的分布式计算项目，招募了全球各地拥有家用电脑并运行屏保程序的用户，让他们运行一个能进行这些计算的屏保程序。它既能预测蛋白质结构，也能设计全新的结构。我们成功招募了不少志愿者，实际上'罗塞塔@家'的算力已经相当于一台中型超级计算机。我认为，社区参与对我们的科学研究一直非常重要。

That is exactly what we reasoned. So in fact, we started a distributed computing project called Rosetta at Home, where we enlisted people all over the world who had computers at home and who were running screensavers to run a screensaver that would do these calculations. It would both predict protein structures and it would design brand new structures. And we were able to enlist quite a few volunteers, and actually Rosetta Home became equivalent to a medium sized supercomputer. And so that, I think community involvement in our science has always been really important.

后来'罗塞塔@家'发展成了——我们有个屏保程序，你可以在电脑屏幕上看到蛋白质被设计或折叠的过程，人们会观看这个屏保，然后给我们写信说：'这很酷，但我觉得我能做得更好。'这促使我们开发了一款名为《折叠它》的互动游戏，参与者不仅能让计算机折叠蛋白质，还能亲自参与指导折叠过程。

Rosetta at Home then led to, we had a screensaver, you could see the protein getting designed or folding up on the computer screen, and people would watch the screensaver, and they would write to us and say, It's really cool, but I think I could do better. And that led us to develop an interactive game called Fold It, where the participant could not only have their computer fold the protein, but they could get in and actually guide it.

所以这有点像生物化学版的'SETI@家'概念，对吧？人们将数据片段下载到家用电脑上，用自家电脑和电费进行处理，非常巧妙，大卫。这种方式最终促成了对某段数据的解析或分析，当把所有计算结果汇总时，你就通过整合全球数千台电脑获得了巨大的计算能力。

So this is sort of like the biochemical equivalent of what went on to become the SETI at home concept, wasn't it? People downloading bits of data to their home computer, which could then be crunched on their computer, their electricity bill, very crafty, David. And that led to a resolution of a, or an analysis of a piece of data, that when brought back together, you had that enormous wealth of computing power by harnessing thousands of computers around the world.

没错，正是如此。实际上我们不需要为此搭建基础设施，因为SETI@home团队和开发者们非常慷慨，他们帮助我们使用他们的整个平台和基础设施，将无数个人电脑连接起来进行这些计算。不同之处在于，我们的项目参与者不是在处理无线电信号，而是在折叠和设计蛋白质。

Yes, that's exactly right. In fact, we didn't have to build up the infrastructure for this, because the SETIhome group and developers were incredibly generous, and they actually helped us to use their entire platform and infrastructure for connecting many, many, many personal computers together to do these calculations, except that the difference was rather than processing radio signals, the participants in our project were folding and designing proteins.

你们最初向用户发送了什么内容？在开发你刚才提到的游戏之前，当你们只是向他们展示正在处理的数据时，他们从服务器获取了什么？他们的电脑在做什么？展示给用户的内容中，是什么让某些人觉得'我知道如何改进这个'？

What were you sending to the users, and what were they when they were first doing this, before you got onto the game that you developed that you just mentioned, but when you were just showing them data that was being crunched, what were they picking up from your server? What was their computer doing? What was it showing them that some people were then latching onto and saying, I think I know how to improve on this?

当我们试图弄清蛋白质如何折叠时，正如我们所说，任何蛋白质都可能有许多不同的形状。所以我们只发送蛋白质的氨基酸序列，然后参与者的电脑会将其折叠，并将折叠后的结构发回给我们。通过这种方式，我们会收到数十万种蛋白质可能折叠的方式，从中我们可以识别出最可能是正确解决方案的结构。我们用于评估的原理类似于预期——就像球在凹凸不平的表面上滚动，最终会停在最低点。同样地，正如我所说，我们通过这些计算来观察所有原子间的相互作用，并以此计算每个蛋白质的能量。

Well, when we were trying to figure out how a protein folded up, as we said, there are many, many different shapes that any protein could have. So we would send out just the amino acid sequence of the protein, and then the participant's computer would fold it up and it would send us back the folded structure. And what we will get back from this is hundreds of thousands of different possibilities for how the protein could fold up, and from which we could identify those that were the most likely correct solution. And the principle we use to evaluate that is similar to expectation when you have a ball rolling on a bumpy surface, that it will eventually end up in its lowest elevation point. Well, similarly, as I said, we work in these calculations where we look at the interactions between all the atoms and we calculate the energy of each protein in that way.

因此我们筛选出那些氨基酸序列能量最低的蛋白质构型。

And so we select out those shapes for which the amino acid sequence had the lowest energy.

这大约是在世纪之交、2000年代初吧？当时你们正在做这项研究。虽然取得了一些进展，但显然还存在差距，因为这项技术并未立即革新我们的蛋白质预测能力，否则你们应该早就获得诺贝尔奖了。那么当时的主要障碍是什么呢？

This was about the turn of the millennium, early noughties, wasn't it, that you were doing this? So that got you a bit further along, but there was still clearly a gap, because this didn't immediately revolutionize our ability to predict proteins, or you would have won the Nobel Prize a lot longer ago than you have. So what was still the stumbling block at that stage then?

无论是结构预测还是设计，障碍都如我所述——蛋白质由成千上万个原子组成，结构极其复杂。比如要获得精确的结构预测就非常困难。在设计方面，我们虽然能设计出功能越来越强大的蛋白质，但必须尝试大量设计方案才能找到真正有效的解决方案。真正的转折点是深度学习的出现，DeepMind团队（我的获奖同事约翰·詹珀和德米斯·哈萨比斯）精彩地证明了这一点——他们利用足够庞大的蛋白质结构数据库，从中学习折叠规则，实现了从氨基酸序列直接预测三维结构。不过我需要补充些背景信息。

Well, the stumbling block for both structure prediction and design were just the ones that I described, that proteins are very complicated and they're made out of many thousands of atoms. Really doing accurate calculations, it was really hard to get really accurate structure predictions, for example. On the design side, we were able to design more and more powerful proteins doing a wider and wider range of jobs, but we had to try a lot of different designs to find one that really worked well and solved the problem that we intended it to solve. So the real game changer was the advent of deep learning, and that was really demonstrated in a spectacular fashion by the DeepMind team, my co laureates John Jumper and Demis Hassabis, who showed that database of protein structures was sufficiently large that one could learn from it the rules of protein folding and go from an amino acid sequence directly to a three-dimensional structure. So I have to tell you one thing though, just to put this in context.

在能够通过氨基酸序列预测蛋白质结构之前，全球科学家花费了数十年时间（至今仍在继续）通过实验测定蛋白质结构。这意味着要确定蛋白质中每个原子的空间位置，他们采用多种方法，比如最有效的手段之一是用X射线照射蛋白质晶体并分析散射模式，从而直接获取原子位置信息。过去50多年里，数万名科学家投入了数百亿美元的研究经费来测定蛋白质结构，许多杰出科学家仍在致力于解析更复杂蛋白质的结构。这最终建立了一个包含约20万种蛋白质结构的数据库，每种结构都精确标明了各原子的相对位置。

Before it was possible to predict the structure of a protein from its amino acid sequence, Scientists around the world spent many, many years, and actually still do, determining the structures of proteins experimentally. That means figuring out where in space each atom of a protein is, and they do this in a number of ways. For example, one of the most powerful is shining x rays at a crystal of the protein and figuring out how those x rays scatter, and that gives you direct information on the position of atoms. Now, tens of thousands of scientists over 50 at an expense of tens of billions of dollars or more spent their careers determining the structures of proteins, and many scientists, great scientists, are continuing to solve the structures of more and more complex proteins. What this led to was a database of about 200,000 different protein structures, and each protein structure specifies exactly where each atom in that protein is relative to the others.

这个数据库堪称极其丰富的信息宝库。DeepMind团队证明，这个信息库足够详尽丰富，足以从中学习规律并根据序列预测蛋白质结构。

So it's this incredibly rich storehouse of information. And what the DeepMind group showed is that this information store was sufficiently detailed and rich that you could really learn the rules and predict structures of proteins from their sequence.

你们将所有宝贵信息——即人们辛苦测定的各种蛋白质三维原子坐标——输入人工智能系统供其学习。这意味着当输入一个未知蛋白质的氨基酸序列（即从未见过的蛋白质构建单元）时，AI就能运用相同规律推演出其可能的结构形态。

You feed into the artificial intelligence all of that wealth of information, where people have painstakingly worked out where the atoms are in three-dimensional space in each of those proteins, so it can then learn. And that presumably means you can then feed it an unknown protein, an amino acid sequence. These are the building blocks of a protein you've never seen before, and it can apply the same rules to then work out what it would look like.

完全正确。DeepMind团队开发的程序名为AlphaFold，它通过已知结构的蛋白质氨基酸序列进行训练，学习结构预测能力。现在只需向AlphaFold输入新的氨基酸序列，它就能生成预测结构。

That's exactly right. So the program that the DeepMind group developed is called AlphaFold. AlphaFold was trained on all the amino acid sequence of proteins of known structure. It was trained to predict the structure, and so now you can give a new amino acid sequence to AlphaFold, and it will generate the predicted structure for it.

评奖委员会提到的一点是，你们实现了制造新型蛋白质这一近乎不可能的壮举。这本质上是我们刚才讨论内容的前置基础。你们证明了可以从零开始创造全新蛋白质，能够提出概念并完成设计。而我认为DeepMind团队随后所做的，就是为你们提供了一种大幅加速这一过程的方法。

One of the things that the award committee said was that you achieved the almost impossible feat of making new proteins. So this was essentially upstream of what we've just said. You proved that you could make a new protein from scratch. You could come up with a concept and design it. And I suppose what the DeepMind team then did was to equip you with a way of doing that far faster.

确实如此。正如我所述，早在蛋白质设计成为成熟领域之前，我们就已开始相关研究。我们采用了我早先描述的原子级建模方法，需要模拟所有原子间的相互作用。正是用这种方法，我们设计出了完全新颖的蛋白质。诺贝尔委员会特别引用了我们2003年的这项突破性工作。

Well, yes. So as I described, when we started designing proteins long before was deep even a well established field. And we used this atomic description that I described earlier, where we had to model all the interactions between pairs of atoms. And we used that approach to design completely new proteins. And that was what was cited by the Nobel Committee, that was back in 2003.

当DeepMind证明深度学习能极大提升蛋白质结构预测后，我们自然迅速转向将其应用于蛋白质设计。我们发现基于深度学习的方法能设计出远超传统原子云建模技术的新型蛋白质。这些新方法让我们能设计具有广泛功能的蛋白质，且已向全球研究者免费开放。现在最令人振奋的是，看到众多科研团队正用我们开发的深度学习方法设计新型蛋白质。

After DeepMind showed that protein structure prediction could be greatly enhanced using deep learning, we naturally were very quickly moved to apply deep learning to protein design. What we found is that we were able to develop very powerful methods for designing brand new proteins that were much better than the previous methods based on this cloud of atoms I described earlier. And using these new design methods, we can design proteins that have a very wide range of different functions. And we have made these methods freely available to anyone in the world. And so it's very exciting now because we're seeing many different research groups designing new proteins using the deep learning methods we've developed.

回想十到十五年前，试图用设计蛋白质来解决生物技术或可持续发展问题，这种想法会被视为疯狂至极。但现在人们已真正认识到设计新型蛋白质在医疗、可持续发展和技术领域的巨大潜力。这确实是个激动人心的时代。

So there's going to be maybe ten, fifteen years ago, the idea of trying to solve a problem in biotechnology or in sustainability with a design protein just sound totally crazy on the lunatic fringe. But now there's really great interest in designing new proteins to solve problems in medicine and sustainability and technology. So it's a very exciting time.

能否请您设想下实际应用场景？比如海洋塑料污染这个棘手难题。我们能否设计一种自然界从未存在过的酶蛋白，专门分解海洋中的塑料？现在是否可以把这类问题交给你们的解决方案，开始构建能完成这种任务的蛋白质机器？

Could you, for example, to think about how we might deploy something like this, could you say, well, ocean and marine plastic pollution, that's a major headache. I want to design an enzyme that has never existed in nature. It's a protein that can attack plastic in the ocean and get rid of it. Could we throw that sort of problem at this sort of solution now and begin to build protein machines that would do that sort of job for us?

这正是我们当前重点攻关的方向。团队中有多位顶尖研究者专门从事塑料分解催化剂的设计。我们同时也在开发新型固碳技术，以及能精准靶向癌细胞的治疗性蛋白质，从而实现无全身副作用的癌症治疗。更令人振奋的是，我们设计的第一款医用蛋白质——由我院NeilKing研发的新冠疫苗已获批准上市。

That is exactly the type of problem that we're working on now. There are several extremely talented researchers in my group who are working specifically on that to design catalysts that will break down plastic. We're also working on new ways to fix CO2, as well as new proteins that will very specifically target cancer cells in the body, so you can treat the cancer without systemic effects. It's an exciting time also because we have our first medicines that have been approved for use in humans, and that's a vaccine, a COVID vaccine developed by my colleague, Neil King, at the Institute for Protein Design here.

人们常说当事情出错时往往最有研究价值。在你们的工作中，是否发现人工智能存在某些系统性错误？因为这类异常背后可能隐藏着重要发现。你们是否观察到类似现象？

People often say that it gets interesting when things break or don't work. So when you do this, are there any things that trip up these artificial intelligences, things that they consistently get wrong and shouldn't? Because often there might be something interesting lurking in there. Have you noticed anything like that?

事实上，在我们研究的每个问题中，我们只专注于那些处于技术前沿的难题，因为我们认为那些简单问题其他人可以用我们发布的软件解决。而当你处理一个复杂问题时，通常只能理解其中40%的机理。关键是要开始着手解决问题，比如靶向肿瘤或分解塑料。最初设计的几个方案往往效果不佳，这时就需要找出问题所在。

Well, fact, in every problem we work on, we only work on problems which are kind of at the cutting edge of what's possible, because the really easy problems we figure people in other places could do with the software we're releasing. And whenever you work on a hard problem, you only understand about 40% of what's going on. And so one of the really key thing is you start working on a problem, like targeting a tumor or breaking down plastic. And the first few designs you make don't work, or they don't work very well. And then you have to look at what's going on that's wrong.

这会启发你改进设计策略或方法以真正解决问题。科学研究的本质正是如此：先提出解决问题的假设，尝试后发现效果不如预期，再探究原因并相应改进方法和思路。

Then that gives you ideas on what do you need to improve about your design strategy or the methods to really solve those problems. And so that's really largely what science is about, is having some hypothesis about how to solve a problem, trying to solve it, and then it doesn't work as well as you thought, and then trying to figure out what the basis for that is, and improving your method and approach accordingly.

现在有些科学分支正走向合成路线。正如我们对话开始时你解释的，蛋白质是由20种氨基酸组合构成的。但如今作为聪明的化学家，我们可以创造自然界不存在的氨基酸，从而进行自然界可能不存在的化学反应。不过人工智能能利用这些新型化学物质吗？毕竟我们缺乏使用这些新化学物质的庞大蛋白质数据库来训练模型。

Some branches of science are also now going down the synthetic route, where when we began this conversation, you explained a protein is something made from one of a combination of 20 different amino acids. But we can, as clever chemists now, make amino acids that don't exist in nature. So we can therefore do chemistry that may not exist in nature. Can the artificial intelligences be brought to bear using these novel chemicals though? Because of course, we won't have that vast database of proteins that use these new chemicals we're creating to train on.

没错。这正是之前基于原子间相互作用建模的方法仍然非常有价值之处。我们正在尝试你描述的方法，设计包含非天然氨基酸和非天然辅因子的催化剂。就像给机器装备全新强大组件，使其能进行更复杂的化学反应。将你提到的新深度学习方法（主要针对天然20种氨基酸）与我描述的原子物理建模方法相结合，这种组合非常强大——因为那些基于物理原理的旧方法完全可以把非天然氨基酸或辅因子视为通过化学键连接的原子集合来建模。

Exactly. So this is where the previous methods that were based on modeling all the interactions between the atoms still are very useful. So we're trying to do exactly what you described, build catalysts now that incorporate unnatural amino acids and unnatural cofactors into our designs. It's like having our machine now has this kind of totally new powerful thing in it that will allow it to do more sophisticated chemistry. And this is where combining the new deep learning methods, which as you pointed out, are really used to just seeing the natural 20 amino acids with the previous methods that I described, where we're modeling everything as just a collection of atoms using physical principles, that combination is powerful because those older physically based methods have no problem modeling that unnatural amino acid or cofactor as just a collection of atoms connected by bonds.

这一切真令人兴奋，你能清楚看到这些成果将很快转化为突破性进展，对吧？但当你结束一天工作回家时，会不会因此感到头脑发胀？你有什么巧妙的方法来放松身心，暂时不去想蛋白质结构吗？

It's so exciting, all of this, and you can really see how this is going to translate and quickly into really groundbreaking stuff, can't you? But when you go home at the end of the day, is your head spinning because of this? Or do you have crafty ways of managing to relax, or sort of get away from it, and not think about protein structures for a while?

我住在西雅图很幸运，因为我热爱山脉。每个周末我都尽量去滑雪、徒步或登山。实际上现在西雅图有点下雨，但山里正在下雪。我已经迫不及待想这周末去滑雪了。

So I live in Seattle, which is fortunate because I love the mountains. So on the weekends, I try and get up skiing or hiking or climbing pretty much every weekend. Right now, in fact, it is a little rainy in Seattle, but that means it's snowing in the mountains. I'm excited to get out and ski this weekend.

这对激发灵感很有帮助。记得多年前因发明PCR复制DNA技术获诺贝尔奖的凯利·穆利斯告诉我，他就是在开车前往蒙蒂西诺山间小屋时想到这个概念的。或许你深入荒野的旅程也会带来很多灵感。

Good for inspiration, should think, because Carrie Mullis, who got the Nobel Prize a number of years ago for discovering and and coming up with the the idea of the PCR reaction to copy DNA, he told me he came up with that concept driving up to his mountain cabin at Montesino. So maybe maybe your trips into the into the out out into the great back of beyond are very inspirational.

确实。它们确实帮助我保持了理智，这非常好。

Yeah. They certainly helped me preserve sanity, which is very good.

非常感谢你与我们分享这一切，大卫。再次祝贺你获得诺贝尔奖。我希望诺贝尔奖能带来更大的办公室，因为听起来现在有点拥挤。

Well, thanks very much for telling us all about it, David. Congratulations once again on your Nobel Prize. And I hope that with the Nobel Prize comes a bigger office, because it sounds a bit cramped.

到目前为止，我得说诺贝尔奖在改善我们的研究条件或资源方面出奇地无用，但我仍然抱有希望。

Well, so far, I would say the Nobel Prize has been remarkably useless in trying to improve our research conditions or resources, but I'm still hopeful.

这次节目就到这里。如果你想在此期间联系我们，我们的邮箱地址依然是5lifescience@bbc.co.uk。我是克里斯·史密斯，感谢收听，下周再见。

That's it for this time. But if you'd like to get in touch in the meantime, our email address as ever, 5lifescience@bbc.co.uk. From me, Chris Smith, thanks for listening, and until next week. Goodbye.

我是马特·肖蒂，每周一至周五凌晨2点从威斯敏斯特为您带来现场报道。这是政治，但它如何影响你。这里有有趣的故事、人物和构成威斯敏斯特村的个性，但我想解释它是如何运作的。

I'm Matt Shorty coming to you live from Westminster, Monday to Friday from 02:00. It's politics, but how it affects you. It's the funny stories, the people, and the personalities that make up the Westminster village, but I wanna explain how it works.

秩序。秩序。

Order. Order.

我在那里工作了二十年。我真正了解所有人、安静的走廊，也知道如何找到那些真正影响你的政治事件的故事。新闻一爆发，我就能敲墙，BBC的记者之一就会跑进来告诉我们发生了什么。马特·肖利，每周一至周五两点

I've worked there for twenty years now. I really know all the people, the quiet corridors, and I know how to find the stories about what's really happening in politics which affect you. As soon as the news breaks, I can bang on the wall, and one of the BBC correspondents will come running in and tell us what's going on. Matt Shorley, Monday to Friday from two

这里是BBC五台直播。

On BBC Radio five live.