人工智能可以加速科学家的职业发展——但也会限制其研究范围

下一个时代的自然。

Nature in the next era.

还不知道。

Know yet.

为什么

Why

生命到目前为止是怎样的？

is life so far?

听起来很简单。

Like, it sounds so simple.

他们完全不知道。

They had no idea.

但现在

But now

我找到的数据不仅令人耳目一新，而且在某种程度上令人震惊。

the data's I find this not only refreshing, but but at some level astounding.

自然。

Nature.

欢迎回到《自然》播客。

Welcome back to the Nature Podcast.

本周，探讨人工智能如何可能提高研究人员的生产力，但同时缩小他们的研究视野。

This week, how AI might increase researchers' productivity but narrow their focus.

还有宇宙中那些微小红点的谜团。

And the mystery of the universe's little red dots.

我是本杰明·汤普森。

I'm Benjamin Thompson.

我是尼克·彼得鲁奇奥。

And I'm Nick Petrucciow.

人工智能工具已成为许多科学领域的组成部分。

Artificial intelligence tools are part and parcel of many areas of science.

我指的不仅仅是像ChatGPT这样的生成式AI助手。

And I don't just mean generative AI assistants like ChatGPT.

深度学习支撑了像AlphaFold这样的工具，而机器学习几十年来一直被用于发现数据中的关联。

Deep learning has underpinned tools like AlphaFold, and machine learning has been used to find associations in data for decades.

尽管关于这些工具对研究的影响已有大量讨论，但人们对它们对整个科学的影响实际上知之甚少。

And while a lot can and has been said about the effects of these tools on research, not much is actually known about their impact on science as a whole.

我们aware了这些工具的炒作以及科学家使用它们的自然动机，但我们只是好奇这是否也有另一面。

Well, we were aware of the hype and the associated kind of natural motivation for scientists to use these tools, but we just were wondering if there was a flip side to that.

这对整个科学意味着什么？

What is that doing to science as a whole?

这是数据科学家詹姆斯·埃文斯，他一直在研究这一问题，相关论文本周发表在《自然》上。

This is James Evans, a data scientist who's been looking at this question for a paper that was published in Nature this week.

他的研究结果表明，这些工具可能会让科学家更具生产力，但需要付出代价。

And his results imply that these tools may well make scientists more productive, but at a cost.

詹姆斯和一个研究团队分析了1980年至2025年间发表的超过4100万篇论文，这些论文在某种程度上都得到了AI工具的辅助，他们称之为AI增强研究。

James and a team of researchers looked at more than 41,000,000 papers published between 1980 to 2025 that were in some way assisted by AI tools, which they refer to as AI augmented research.

他们还选择专注于自然科学，排除了计算机科学和数学。

They also chose to look at the natural sciences, ignoring computer science and mathematics.

我们感兴趣的是AI如何被应用于科学领域，而不仅仅是AI本身的核心开发。

We're interested in how AI is being applied to the sciences rather than just the core development of AI itself.

他们所研究的自然科学并不是唯一受到AI增强的领域。

And the natural science research they were looking at wasn't the only thing that was AI augmented.

为了阅读这4100万篇论文，研究团队使用了一种名为双向编码器表示变换器（BERT）的AI语言模型，朋友们都叫它BERT。

To read through these 41,000,000 papers, the team used an AI language model called bidirectional encoder representations from transformers or BERT to its friends.

这使他们能够扫描论文的标题和摘要，以识别可能的AI使用情况。

This allowed them to scan through the titles and abstracts to identify possible AI use.

这可能包括使用机器学习或深度学习来分析数据，甚至使用生成式AI。

This could be things like machine learning or deep learning to scour through data or even generative AI use.

这一结果的准确性随后由人类专家进行了审查，BERT与专家的判断一致率达到87.5%。

The accuracy of this was then scrutinized by human experts, and BERT agreed with the experts 87.5% of the time.

这一准确率甚至高于专家之间的相互一致性。

Which is higher than experts agreed with each other.

在最初的数百万篇论文中，团队最终筛选出超过31万篇，这些论文的分析表明其研究在某种程度上受到了AI的增强。

From the initial millions of papers, the team ended up with just over 310,000 that their analysis suggested presented research that was AI augmented in some way.

他们随后可以观察这些论文的引用次数与未使用任何AI工具的论文相比如何，以及从事AI增强研究的科学家后续发表了多少成果，以及这如何影响了他们的职业生涯。

They could then look at how many citations these papers got versus the ones that hadn't used any AI tools and how much the scientists behind the AI augmented research went on to publish and how it impacted their careers.

我的意思是，这真是个绝佳的职业选择。

I mean, it's a great career move.

无论在他们科学发展的哪个阶段，只要他们选择使用AI工具而其他同行没有使用，这都是一个明智的决定。

It was a great move at any point when they kind of split from their peers in using AI tools versus comparable scientists that chose not to use those tools at any point of their scientific development.

他们获得了更多的引用关注。

They got more citation attention.

他们更不可能退出科学界，而且更迅速地建立实验室，成为在论文中主导研究的资深科学家。

They were less likely to drop out of science, and they were more rapidly likely to establish labs and become senior scientists that direct research on paper.

进行AI增强研究的科学家发表的论文数量约为同行的三倍，获得的引用次数接近五倍，并且比非AI增强的同行提前一年多成为研究项目负责人。

Scientists who did AI augmented research published about three times as many papers, got nearly five times as many citations, and became research project leaders more than a year earlier than their non AI augmented peers.

但詹姆斯和团队也对AI对科学整体的影响感兴趣。

But James and the team were also interested in the effects of AI on science writ large.

因此，他们研究了这些论文与其他论文之间的关联。

So they looked at how these papers related to others.

例如，它们是否相互引用了对方？

Did they cite one another, for example?

尽管他们发现这对个人有益，但这种AI使用似乎正在缩小科学本身的研究范围。

And while they found a benefit to individuals, this AI use seemed to be narrowing the focus of science itself.

因此，很明显，AI实际上是在压缩或自动化现有的科学领域，而不是生成能够引发热烈讨论的新问题。

So it seemed clear that it was really compressing or kind of automating existing scientific fields rather than generating new questions that, you know, lead to fermentive discussion.

分析表明，使用AI辅助的研究人员虽然进行了更多的研究，但研究领域却更少，他们更专注于那些数据丰富、适合应用AI工具的领域，这一趋势让詹姆斯感到担忧。

The analysis suggests that AI augmented researchers were doing more research but in fewer areas, instead focusing on fields that had lots of data and that they could apply their AI tools to, a trend which concerned James.

如果每个人都爬同一棵树，低处的果实已经被摘光了，我们在解决重要问题时只能为几个百分点的竞争而挣扎。

If everyone's kinda climbing up the same tree, the low hanging fruits are gone, and we're kind of battling over a couple of percentage points as we solve important problems.

所以，你知道，AI被用来解决重要、已确立且被广泛认可的问题，这很重要。

So, you know, AI is being used to solve important, established, agreed upon problems, and that's important.

问题是，如果你解决了所有这些问题，或者比生成新问题的速度快得多，那么新颖问题的提出和讨论的速率就会放缓，而这正是我所说的释放人类创造力和远见的关键。

The problem is if you solve all those problems or if you solve them much faster than you generate new problems, then it slows down the rate of novel questioning and discourse and the kinds of things that unleashes, I would say, human creativity and vision.

负责撰写关于这项新研究的新闻与观点文章的计算机信息系统研究者维达·斯托里，对这些结果并不那么担忧。

Veda Storey, a researcher of computer information systems who's been writing a news and views article about the new research, was less concerned about these results.

我觉得他们太悲观了。

I thought they were too pessimistic.

我会更加乐观。

I would be much more optimistic.

在某些方面，维达认为这些结果是使用AI工具的自然结论。

In some ways, Veda saw these results as a natural conclusion of using AI tools.

我们现有的工具在处理和筛选大量数据方面非常有效。

The tools we have are largely good at working with and sorting through lots of data.

因此，使用这些工具的人专注于数据丰富的领域也就不足为奇了。

So it's unsurprising that people who use them may focus on areas that are data rich.

她认为这有可能推动科学进步。

And that's something that she thinks could allow science to advance.

如果你仔细想想，他们已经发现许多科学家正在使用AI工具。

If you think about it, they have identified the fact that many scientists are using AI tools.

如果允许他们深入某些领域，我们就能从中受益。

And if they allow them to go deeper into certain areas, we can benefit from that.

我们可以从对它的深入理解中获益。

We can benefit from an in-depth understanding of it.

因此，我认为作为科学家，我们有无数机会继续沿着使用AI工具的道路前进，而这些AI工具只会变得越来越好。

So I just see so many opportunities for us as scientists to continue on this road of using the AI tools, and these AI tools are only going to get better.

Veda还指出，由于这项研究范围有限，尤其未涵盖数学和计算机科学等领域，因此很难对整个科学界做出普遍性结论。

Vader also pointed out that it may be hard to generalize about all of science from the limited study here, especially as it doesn't include some areas like mathematics and computer science.

尽管这篇论文研究了生成式AI工具，但她提醒说，现在判断这些工具对科学可能产生的影响还为时过早。

And while the paper did look at generative AI tools, she cautions that it's a bit soon to tell how they might impact science.

对于这些工具，她的态度也更为谨慎。

On these tools as well, she was a bit more cautious.

她认为这些工具在科学写作中尚未准备好投入使用，并担心它们可能影响研究的可重复性。

She didn't think they were quite ready for use in scientific writing, and she had concerns that they may impact replicability.

过去，AI工具具有确定性。

In the past, AI tools have been deterministic.

你输入一个内容，就会一次又一次地得到相同的输出。

You put in an input and you get the same output time after time.

生成式人工智能是概率性的，意味着相同的输入可能会产生不同的输出。

Generative AI is probabilistic, meaning that you can get different outputs from the same input.

在科学领域，我们总是需要重复验证结果。

Now in science, we always need to replicate a result.

这是科学中非常重要的事情。

That is a very important thing in science.

我们能重复这些研究吗？

Can we replicate these studies?

我们能否确保所报告的结果是可靠的，并经得起科学标准的时间考验？

Can we ensure that the results that we are reporting are good and hold the test of time for scientific standards?

如果你使用一个相同输入会给出不同输出的工具，当你试图重复所有这些研究时会发生什么？

If you're using a tool that will give you different outputs given the same input, what happens when you go to try and replicate all these studies?

在她看来，詹姆斯的研究为这一主题的进一步研究奠定了基础。

In her opinion, James's study has laid the foundation for further work on this topic.

她希望看到几年后这一情况会如何发展。

And she would like to see how it would play out in a few years' time.

詹姆斯虽然对科学研究范围的缩小表示担忧，但他认为人工智能工具具有很大的潜力。

James, despite his concerns about the narrowing of scientific focus, believes that AI tools have a lot of possibilities.

对他而言，我们需要关注的是科学中的激励机制。

For him, we need to instead look at the incentives in science.

我认为，这里我们看到的是个人与整个科学体系之间的激励冲突。

I think what we see here is kind of a conflicting incentive for individuals and for science as a whole.

对吧？

Right?

个人试图在科学宇宙中生存下去。

Individuals are trying to survive in the scientific universe.

他们想要晋升。

They want promotion.

他们想要资源来开展更多的科学研究。

They want resources to do more science.

而最快、最有效的方式是什么？尤其是在有了人工智能工具的情况下，这些工具能够有效压缩你拥有的数据，并生成答案和预测。

And what's the fastest, most efficient way to to do that, especially with, you know, AI tools that effectively compress, you know, data that you have and produce answers and predictions.

但我认为，科学整体的激励机制是不同的，它的目标是尽可能了解一切。

But I think science as a whole has a different incentive, which is to kinda know everything.

詹姆斯希望看到更多激励措施，以推动新领域的开拓和提出新问题。

James would like to see more incentives to forge new fields and ask new questions.

他甚至认为，人工智能可以通过从数据更稀疏的领域收集数据来协助实现这一点。

He even thinks AI could assist with that by collecting data from more data sparse fields.

这也需要相应的激励机制。

That too would need incentives.

如果没有改变，詹姆斯预测人们将继续以现有方式使用人工智能，可能会缩小科学探究的范围。

Without change, James predicts that people will just carry on using AI how they have, potentially shrinking the focus of scientific inquiry.

因此，为了让科学得以发展，詹姆斯认为我们需要改变使用人工智能的方式。

So to allow science to grow, James thinks we need to change how we use AI.

人工智能的应用范围广泛，正在科学领域不断增长和加速，而我们在利用它拓展集体探究空间、开拓新领域和生成新数据方面，存在巨大的被忽视的机会。

AI use is vast, and it's growing, and it's accelerating in science, and there's some massive missed opportunities in how we use it to expand the space of collective inquiry to new fields and the generation of new data.

如果我们不这样做，就可能面临模型崩溃的风险，即人工智能模型基本上消耗了自身数据产生的结果。

And that if we don't do that, then we risk the kind of model collapse that we see when AI models basically consume the results of their own data.

你知道，它们会捕捉分布的尾部，然后就不再有效了。

You know, they pull in the tails of the distribution, and they they just stop working.

我们需要科学不断发展。

Like, we need science to grow.

这才是科学的本质。

That's its character.

因此，我们需要重新引导人工智能的使用方式。

And so that means we need to redirect the use of AI.

这是来自美国芝加哥大学的詹姆斯·埃文斯。

That was James Evans from the University of Chicago in The US.

你还听到了来自美国佐治亚州立大学的韦德·斯托里。

You also heard from Vader Story from Georgia State University, also in The US.

如需了解更多，请查看节目说明中的相关链接。

For more on that, check out the show notes for some links.

接下来，是可能解开宇宙小红点之谜的研究。

Coming up, the research that may have solved the riddle of the universe's little red dots.

不过现在，让我们进入由卡特琳娜·克拉克朗读的研究亮点部分。

Right now, though, it's time for the research highlights read by Katrina Clark.

根据对当时岩石的分析，雪球地球时期的海洋不仅非常寒冷，而且盐度极高。

Snowball Earth's oceans were not only very cold, but also extremely salty according to analysis of rocks from the time.

大约七亿年前，有人提出地球就像一个雪球，冰川一直延伸到赤道，平均气温可能低于零下12摄氏度。

Around seven hundred million years ago, it's been proposed that the Earth resembled a snowball with glaciers reaching all the way to the Equator and average temperatures potentially 12 degrees Celsius below freezing.

换句话说，那将会非常寒冷。

To put it mildly, it would have been pretty chilly.

而海洋被冰层覆盖，会切断陆地与海洋之间的常规物质交换。

And this covering of the oceans with ice would cut off the usual exchanges between land and sea.

例如，本应被光合生物氧化的铁，最终沉积在了海底。

For example, iron that would usually have been oxidized by photosynthetic organisms would have ended up deposited on the ocean floor.

研究人员分析了雪球地球时期岩石中的此类沉积物。

Researchers analyzed such deposits in rocks dating from the snowball Earth period.

通过模拟铁的不同同位素在水中的分离方式，他们得出结论：这些铁是在零下22至零下8摄氏度的温度下沉积的。

By modeling how different isotopes of iron would have been separated in water, they concluded that the iron was deposited in temperatures between minus 22 and minus eight degrees Celsius.

在这些温度下，水要保持液态，就必须含有极高的盐分。

At these temperatures, to stay liquid, the water would have had to have been extremely salty.

这些极寒时期可能是地球历史上最冷的海洋温度。

These frigid times may actually have been Earth's coldest ocean temperatures.

放松一下，阅读一下发表在《自然·通讯》上的这项研究。

Chill out and give that study a read over nature communications.

将免疫细胞转入夜间模式可能减轻心脏病发作造成的损伤。

Putting immune cells into night mode may reduce the damage from heart attacks.

中性粒细胞是一种对抗微生物的免疫细胞，但它们也可能杀死周围组织，从而加剧心脏病发作的损伤。

Neutrophils are a type of immune cell that protect against microorganisms, but they can also kill surrounding tissue, which can increase the damage from heart attacks.

人们早已知道，中性粒细胞在清晨比夜间更活跃，因此研究人员尝试在小鼠体内将它们转入活性较低的夜间模式，观察会发生什么。

It's been known that neutrophils are more active in the early morning than at night, so researchers tried to see what would happen if they put them into the less active night mode in mice.

这些小鼠被给予了一种药物，该药物靶向中性粒细胞上控制活动昼夜波动的受体之一。

The mice were given a drug that targeted one of the neutrophils receptors that control daily fluctuations in activity.

研究团队发现，经过处理的中性粒细胞表现出类似夜间的特性，而接受药物的小鼠心脏组织的坏死或损伤程度更低。

The team found that the treated neutrophils displayed nighttime like behavior, and the mice given the drug had less dead or damaged heart tissue.

这种治疗似乎没有影响免疫力，因为当小鼠接受金黄色葡萄球菌或白色念珠菌处理时，其免疫反应正常，这表明将中性粒细胞转入夜间模式可能是治疗人类心脏病发作的一种有前景的方法。

This treatment didn't seem to affect immunity, as the mice responded normally when treated with staphylococcus aureus bacteria or candida albicans fungi, suggesting that this night mode for neutrophils could be a promising approach to treat heart attacks in people.

别错过这项研究。

Don't switch off from that research.

相关研究发表在《实验医学杂志》上。

It's over in Journal of Experimental Medicine.

一项天文谜题或许正因本周发表在《自然》杂志上的研究而接近破解。

An astronomical puzzle may be a step closer to being solved, thanks to research published in Nature this week.

如果你观看詹姆斯·韦伯太空望远镜（JWST）拍摄的图像，很难不被那些遥远恒星和星系的壮观景象所震撼。

Now if you look at an image taken by the James Webb Space Telescope, the JWST, it's hard not to be wowed by spectacular images of distant stars and galaxies.

但如果你仔细观察，可能会发现一些让研究人员百思不得其解的现象。

But if you look closer, you might notice something that has left researchers scratching their heads.

这些图像中常常散布着被称为‘小红点’的微小光点。

Often, these images are peppered with tiny points of light known as little red dots.

这些光点发出的光经历了极其漫长的旅程才到达我们这里，这意味着这些天体很可能非常古老。

The light from these dots has traveled a really long way to get to us, meaning these objects are likely really old.

一些估计认为，这些红点早在宇宙大爆炸后约六亿年就已经存在。

Some estimates have them down as being present in the early universe around six hundred million years after the big bang.

这些小红点被称为‘宇宙破坏者’，因为它们与关于早期宇宙特征的主流观点不符。

And these little red dots have been dubbed universe breakers because they don't fit in with standard thinking about the features of the early universe.

关于这些红点是充满年轻恒星的星系，还是巨大的黑洞，一直存在大量争论。

There's been a lot of debate about whether these dots are young star filled galaxies or outsized black holes.

然而，根据一组研究人员的说法，答案可能两者都不是。

Well, according to a team of researchers, the answer could be neither.

为了了解他们认为这些红点究竟是什么，我采访了该团队成员之一、来自英国曼彻斯特大学的瓦迪姆·鲁萨科夫。

To find out what they think it is, I spoke to one of the team, Vadim Rusakov, who is affiliated with the University of Manchester here in The UK.

瓦迪姆向我解释了为什么最初关于这些小红点的两种理论都不成立。

Vadim explained to me why neither of the initial theories about the little red dots quite worked.

当人们计算这些红点中的恒星数量时，发现恒星数量过多，不可能在宇宙的这个时期形成。

So when people counted the number of stars in those red dots, they found that there's just too many stars to be produced at that point in the universe.

它们之所以被称为‘宇宙破坏者’，是因为很难解释如何在如此小的空间内、如此早期就形成如此多的恒星。

They were universe breakers just because it's hard to explain how you can form so many stars in such a small volume so early on.

因此，一定还有其他因素在贡献这些斑点发出的光。

So there had to be something else that was contributing to that light coming from those dots.

当我们更详细地观察时，也就是收集了更多关于这些小红点的信息和数据后，我们发现它们表现出超大质量黑洞的特征，特别是气体快速围绕超大质量黑洞旋转的特征。

When we looked in more detail, so when we gathered more information and more data about these little red dots, we found that they exhibit features of supermassive black holes, and in those particular features are of a gas rotating quickly around a supermassive black hole.

由于超大质量黑洞会产生大量光，当气体被吸积时，会被极度加热并释放出大量光，有时它们的亮度甚至可与整个星系媲美。

And because supermassive black holes produce a lot of light, so the gas gets accreted, it gets really heated and produces a lot of light, sometimes they shine just as bright as a whole galaxy.

因此，由于这些斑点可能部分由恒星组成，部分由超大质量黑洞组成，恒星层面的‘宇宙破坏者’问题就迎刃而解了。

And so because you have these dots made up probably partly from stars and partly from the supermassive black holes, now that universe breaker problem in the stellar context kind of goes away.

但为了确认它们确实是包含超大质量黑洞的星系，我们仍需解决一些问题。

But there were a couple of issues that we still had to resolve in order to tell that they are galaxies containing supermassive black holes.

因此，关于它们究竟是什么，当时存在分歧，但有一个理论可能是主流观点，即超大质量黑洞被一层气体包裹，且有一些证据支持这一观点。

So there was this discrepancy then as to what they might be, but there was one theory that was perhaps a front runner, and this was the unusual situation of there being supermassive black holes enveloped in a layer of gas, and there was some evidence to back that up.

而你们的研究得出了类似的结论，并可能有助于解决围绕‘这些小红点中心是普通超大质量黑洞’这一想法的谜题之一。

And your work comes to a similar conclusion and perhaps helps to solve one of the puzzles surrounding the idea that at the heart of these little red dots was a regular supermassive black hole.

为了得出这一结论，你们研究了十几颗这类小红点，这些红点都积累了大量数据。

And to get to this conclusion, you looked at a dozen of these little red dots for which there's been a lot of data collected.

特别是，你研究了这些天体发出的光。

And particularly, you were looking at the light emitted by these objects.

这能告诉你什么？

What can that tell you?

是的，我们可以通过气体围绕超大质量黑洞旋转的速度以及它释放出的光来判断黑洞的质量。

So, yes, we can tell how massive the black holes are just from the rotation of the gas around the supermassive black holes and the light that it kicks out.

光谱中有一个非常宽的特征，表明气体正以每秒数千公里的速度运动。

There's a really broad feature in the spectrum that tells us that there has to be gas moving at thousands of kilometers per second.

人们发现了这一特征，但问题在于，你可以用它来测量黑洞的质量。

So people found this feature but the problem with that was that you can use it to measure the mass of the black hole.

但如果你用它来测量黑洞的质量，结果会发现它非常巨大。

But if you use it to measure the mass of the black hole, it turns out to be quite massive.

我们又回到了同一个问题——宇宙破坏者的问题。

Again, we're coming back to the same problem of universe breakers.

不是恒星，而是黑洞。

Not with the stars but with the black holes.

因为它们在宇宙早期就被发现，而那时它们却如此巨大，这带来了一些问题。

Because they are found so early in the universe, it was a bit problematic that they were so massive at that point in the universe.

因此，这些黑洞必须形成得非常迅速，并吸积大量物质，才能增长到如此规模。

So the black holes had to have formed really quickly and have accreted a lot of material in order to have grown so much.

所以你的意思是，这么古老的天体不可能在这么短的时间内变得如此巨大，但数据却表明实际情况正是如此。

So what you're saying is then, yeah, that something this old couldn't get that big that quickly then, but the data suggested that that's what was going on.

对。

Right.

因此，我们在研究中发现，问题并不在于黑洞本身，而在于我们对数据的解释方式。

So what we found in our work is that it was not the problem with the black holes themselves, but it was the problem with how we interpreted the data.

因此，同样一项观测表明气体必须高速旋转，但实际上，这也可以由一个完全不同的系统产生——即黑洞周围的气体只需非常致密。

So the same observation that tells us the gas has to be rotating really quickly could actually be produced by a very different system where the gas around the black hole has to be just very dense.

它不需要旋转得那么快。

It doesn't have to be rotating as quickly.

与我们在本地宇宙或其他地方发现的普通超大质量黑洞相比，这个系统特别之处在于，它不必仅由中性气体组成。

And what is special about this system compared to a normal supermassive black hole that we can find in the local universe or elsewhere is that it doesn't have to be just a neutral gas.

它必须是电离气体。

It has to be an ionized gas.

必须存在大量电子，形成一个围绕这些黑洞的电子包层。

There have to be essentially a sea of electrons, a cocoon of electrons surrounding these black holes.

因此，在我们的模型中，光会从该包层中的自由电子上散射。

So in our model, the light gets scattered off the free electrons in that cocoon.

这种散射实际上产生了人们以前认为是表明气体快速绕黑洞旋转的特征。

That scattering actually produces the features that people thought before were telling us about the gas that's quickly rotating around the black holes.

由于气体旋转速度并不快，人们认为质量巨大的黑洞实际上没那么大，有时甚至小10到100倍，这帮助我们缓解了许多宇宙级难题。

Because the gas doesn't rotate as quickly, the black holes that people thought were massive are actually not as massive and sometimes 10 to a 100 times less massive, which helps us ease a lot of these universe breaker type of problem.

这层浓厚的气体云有助于解释其他一些未被观测到的特征，因为通常黑洞会喷射出各种波长的电磁辐射，而这些在那些小红点中并未被观测到，也许这个包层正起到这种作用。

And this thick cloud of gas helps to explain some of the other features that weren't seen because usually black holes kick out all sorts of different wavelengths of electromagnetic radiation, which aren't seen in the little red dots, and maybe this cocoon is helping that.

是的。

Yes.

没错。

Exactly.

这个谜题有不同的组成部分。

There are different pieces to this puzzle.

通常情况下，如果你看到一个黑洞，你会预期它会发出从X射线到无线电波的各种辐射。

So normally, if you see a black hole, you'd expect there to be all kinds of emission produced from x rays to radio waves.

此外，你还会看到各种光的变化发生。

Then you also see all kinds of light variation can happen.

因此，当你长时间观察这些系统时，光会略微闪烁。

So light sort of twinkles a little bit when you look at these systems over time.

但我们没有看到任何这些现象。

But we don't see any of those.

因此，这个谜题实际上可以通过这个‘电子茧’的想法来解决。

And so the puzzle actually can be solved with this cocoon idea.

我们未能探测到的X射线和无线电波，可能被这层电子壳层部分抵消了。

Those x rays, those radio waves that we do not detect can be partly killed off by this shell of electrons essentially.

如果这还是一层浓厚的气体茧，那就能解释为什么这些黑洞不会闪烁。

If it's also a thick cocoon of gas, that could help to explain the fact that these black holes do not twinkle.

这尚未得到证实，但有可能由于存在浓厚的气体云，光必须穿过它，因此需要更长的时间，这意味着这些黑洞的亮度可能会发生变化。

It's still not established but it can happen that because there's a thick cloud of gas that the light has to travel through, it takes a somewhat longer time, which means that these black holes can vary in light.

它们可能会闪烁，但是在更长的时间尺度上，而我们尚未在足够长的时间尺度上观测到它们。

They can twinkle, but on a longer time scale, and we just haven't observed them on that long enough time scale.

因此，您的结果表明，您所观测到的是一个相对较小的超大质量黑洞，周围环绕着浓厚的气体和电子等物质。

And so your results then suggest that what you've got here is a relatively small supermassive black hole, if I can put it like that, surrounded by this thick cocoon then of gas and electrons and what have you.

这是否暗示了这些遥远的微小红点如此古老，可能正是巨大黑洞随着时间推移形成的方式？

Does this suggest that this is how enormous black holes might develop over time as these distant little red dots are so ancient?

因此，我们预计其中一些会成长为我们在邻近宇宙中发现的超大质量黑洞。

So we expect some of them to grow to the supermassive black holes that we find in the local universe.

所以其中一些可能会演化成极为巨大的黑洞。

So some of them will probably get to very massive black holes.

而另一些则可能因为燃料耗尽而停止增长。

Some of them will probably stop growing just because they ran out of fuel.

我们认为，我们正在观测到它们处于一个前所未见的快速成长阶段。

And we think that we find them in this early stage where it's a stage of very rapid growth that we haven't seen before actually.

所以我们还没有在本地宇宙中观测到这类天体。

So we haven't seen such objects in the local universe.

我们才刚刚开始发现一些与这些小红点相似但更近的天体，数量非常少。

We're only now starting to find actually things that are analogous to these little red dots but much closer, very low numbers.

显然，您的论文已经发表，并提出了证据，表明这些小红点是被气体包围的相对较小的超大质量黑洞。

Obviously, your paper is out now and you put forward your evidence that these little red dots are relatively small supermassive black holes surrounded by gas.

当然，关于这些天体的本质，还存在其他竞争性理论。

Of course, there are competing theories about what these things are.

您认为您的研究是否已经彻底解决了这个问题？

Do you think your work puts this to bed?

您觉得其他人会对这些结果作何评价？

What do you think other people will make of these results?

目前有不同团队独立开展研究，使用与我们相似的模型得出了非常相似的结论。

So there are different groups working independently from each other, coming to very similar conclusions with similar models as we do.

因此，至少有两三个团队，包括我们自己在内。

So there's at least two or three probably including ourselves.

所以我认为，社区内形成一种共识是有帮助的，即这些黑洞必须被一层厚厚的气体包裹，这正是使这些黑洞区别于我们之前所见黑洞的独特之处。

So I think it helps to have some sort of consensus in the community that these black holes have to be cocooned in a thick shell of gas, which is what makes these black holes unique, unlike the black holes we've seen previously.

但我们在物理细节上可能还有一些尚未弄清的问题，比如电离气体的含量等等。

But there might be some things that we're still trying to figure out in terms of the physical details as to how much there is ionized gas and so on.

所以我们正在努力弄清这些细节。

So we're trying to to get out the details.

每个人都喜欢太空之谜。

And so everyone loves a space mystery.

我的意思是，我们似乎一直在不断发现新事物。

I mean, it seems like we're discovering new stuff all the time.

我的意思是，你是否因为认为自己解开了这些小红点的谜团而感到高兴，还是因为谜团被揭开而感到一丝失落？

I mean, are you happy that you think you've solved the mystery of the little red dot, or are you almost sad that now we know what they are and the mystery has gone somewhat?

我当然非常高兴。

I'm very happy, of course.

这是一段完整的旅程，我在途中学到了很多，也结识了许多人。

It's been a whole journey, so I've learned a lot on the way, and I've met a lot of people.

能参与这项工作非常愉快。

So it's been very delightful to be part of this.

我认为我们进一步拓展了已知与未知的边界。

I think we sort of pushed the boundary of what is known and what is unknown further.

因此，现在我们有了许多可以探索的新问题，比如超大质量黑洞是如何形成的？

So this opens up all kinds of questions now that we can attack, like how does supermassive black holes form?

我们正在发现它们的青年时期，但我们能否理解它们究竟是如何形成的？

We're finding them in their youth, but can we understand how they actually formed?

瓦迪姆·鲁苏科夫。

Vadim Rusukov there.

要阅读他的论文，请前往节目说明获取链接。

To read his paper, head over to the show notes for a link.

本期节目就到这里。

That's it for this week's show.

但在结束之前，有个小公告。

But before we go, a little announcement.

是的。

Yeah.

完全正确，尼克。

Absolutely right, Nick.

新的一年到了，我们将在《自然》播客中做一些调整。

It's a new year, and we're going to be changing things up a little bit here at the Nature Podcast.

目前在这个环节，我们通常会进行简报对话。

Now at this point in the show, we'd usually be doing the briefing chat.

但从本周开始，我们将把这个环节独立出来，做成一个单独的播客，每周五会出现在你的订阅列表中。

But starting this week, we're going to be spinning that segment out into its own podcast, which will be in your feeds on Fridays.

所以你仍然会在周三收到最新的科学新闻。

So you'll still be getting the latest science news on a Wednesday.

并在周五收到一份简要的研究综述。

And a quick research roundup on a Friday.

我们到时候见。

We'll see you then.

我是本杰明·汤普森。

I'm Benjamin Thompson.

我是尼克·帕特里乔。

And I'm Nick Patrichow.

感谢收听。

Thanks for listening.