新闻室背后的AI：《华盛顿邮报》的维内特·科斯拉

人工智能如何帮助公司紧跟客户的需求，尤其是在客户行为和需求快速变化的情况下？

How can AI help companies meet customers where they are, especially when their behaviors and needs evolve quickly?

在本期节目中，了解一下一家新闻媒体如何将这一挑战转化为机遇。

Find out how one news outlet turns this challenge into an opportunity on today's episode.

我是《华盛顿邮报》的维尼特·科斯拉，您正在收听《我、我自己和人工智能》。

I'm Vineet Khosla from The Washington Post, and you're listening to Me, Myself, and AI.

欢迎收听《我、我自己和人工智能》，这是麻省理工学院斯隆管理评论推出的播客，探讨人工智能的未来。

Welcome to Me, Myself, and AI, a podcast from MIT Sloan Management Review, exploring the future of artificial intelligence.

我是萨姆·兰斯博姆，波士顿学院的分析学教授。

I'm Sam Ransbotham, professor of analytics at Boston College.

自2014年以来，我一直在麻省理工学院斯隆管理评论研究数据、分析和人工智能，发表过研究文章、年度行业报告、案例分析，以及如今这13期播客。

I've been researching data, analytics, and AI at MIT SMR since 2014, with research articles, annual industry reports, case studies, and now 13 of podcast episodes.

每期节目，我们都会邀请企业高管、前沿研究人员和人工智能政策制定者，一起剖析什么是真正推动人工智能成功的要素，而不仅仅是炒作。

In each episode, corporate leaders, cutting edge researchers, and AI policymakers join us to break down what separates AI hype from AI success.

大家好，各位听众。

Hi, listeners.

今天，我们邀请到了《华盛顿邮报》的首席技术官维尼特·科斯拉。

Today, we're joined by Vineet Khosla, chief technology officer at The Washington Post.

《华盛顿邮报》不仅仅是个新闻编辑部。

The Post isn't just a newsroom.

它其实是一个庞大的技术系统，每天向全球数百万读者推送新闻内容。

It's a giant technology machine that delivers journalism to millions of people around the world every day.

维尼特负责带领团队打造支撑突发新闻、读者体验、安全防护和人工智能的后台系统，今天我们希望能通过对话深入了解这些部分。

Vineet leads the teams that build those systems behind the breaking news and audience experience and security and AI, we're hoping based on the discussion today.

我们会聊聊技术如何重塑新闻业，也可能揭开一些观众看不到的幕后故事，再展望一下新闻的未来会是什么样子。

So we'll talk about how technology is shaping journalism and maybe a little bit about what audiences don't see behind the scenes and what the future of news might look like.

维尼特，感谢你今天来做客。

Vineet, thanks for being here.

谢谢你邀请我，萨姆。

Thanks for having me, Sam.

我早就开始听你的播客了，今天终于能坐到镜头这边，真的很开心。

Been listening to your podcast for a while, so it's a pleasure to be finally on the other side of it.

我们不妨聊聊播客背后都发生了些什么。

Maybe we can talk a little bit about what happens behind the scenes with the podcast.

先从很多听众都有同感的一点说起吧。

Let's start with something that many listeners feel.

我觉得在当今这个年代，获取新闻往往让人感到不堪重负、支离破碎，还很难理清头绪。

I think consuming news in our modern world can be pretty overwhelming and fragmented and tough to understand.

尤其是对年轻一代来说，他们成长的数字环境和我当年完全不同。

And that may be especially true for younger audience who are more raised in a different digital world than I was.

从你们的角度看，现在新闻的呈现方式哪里出了问题？又该怎样改进？

So from your side, what's maybe currently broken about how we're experiencing news, and what needs to change?

在我看来，新闻本身并没有出问题。

The way I view it is there is not something broken about news.

如果我们把视野拉远一点，应该把新闻看作一种专业实践，而不是一种形式。

And if we zoom out, we should think about journalism as a discipline, not a format.

一旦只把它当成一种形式，年轻人就容易觉得它已经失效了。

When you start to think about it solely as a format, it may seem broken to the younger audience.

差别在于，他们获取新闻的方式跟你我完全不同。

The difference is they're just consuming it very differently than you and me.

我举个例子：以前我们只是读新闻，后来有了广播，我们听新闻，再后来有了电视，我们看新闻，现在有了人工智能，我们开始跟新闻对话、提问。

And I use this example of we used to just read the news, then came radio, we heard the news, then came TV, we watched the news, then came AI, we started talking and asking to the news.

但在这每一次变化中，人们对新闻的接触其实更多了，新闻在社会中的价值也反而提升了。

And in all of these changes, the consumption of news actually increased, the value of news in our society actually increased.

我们只是在一天中的不同时间，用不同的方式接触新闻而已。

We are just consuming it very differently at different times of the day.

是的，这种获取方式的转变确实很重要。

Yeah, so that consumption is a big deal.

我只想了解我关心的新闻。

I want to know only the news that I care about.

我不想听那些我不在乎的事，但我也希望知道那些我不在乎的事正在发生。

I don't want to hear stuff I don't care about, but I wanna be aware that the stuff I don't care about is happening.

我不想活在信息茧房里。

I don't wanna be in a bubble.

如果你想想流媒体、零售和音乐行业，它们在这上面都挺挣扎的。

So other industries have really struggled with this if you think about the streaming industry and retail and and music.

个性化新闻对《华盛顿邮报》来说会是什么样子？

What is personalized news going to be for The Washington Post?

这个问题我过去两年半一直在琢磨。

That's a question I've grappled with for the last two and a half years.

我不是新闻行业出身的。

I'm not from the news industry.

我是从外面进来的。

I come from outside.

所以我刚来的时候就意识到，新闻其实有两件特别重要的事。

So when I landed here, I realized there are two things news does that is very important.

一是告诉我们世界上哪些事重要，二是解释为什么重要。

One is it tells us what is important in the world and then it tells us why it is important.

这其实就是帮我们理解世界，对吧？

That's the sense making, right?

而个性化的内容已经被社交媒体接管了，它们早就告诉你什么重要了，等用户来到我们这儿时，能提供的新信息其实寥寥无几。

And the personalized aspect is taken over by social media, they already tell you what's important, so by the time they come to us, you know, there are very few things we are telling them different than they already know.

但‘为什么重要’这部分，才是我们真正的核心价值。我认为这里必须找到平衡：要靠数据驱动，但得把数据当指南针，而不是导航仪。

But the why, that is the core value that we provide And that's where I think we have to have a balance of personalized, you need to be data driven, but you need to use your data almost like a compass, not a GPS.

最终责任还在新闻团队身上——一个有责任感、有伦理标准的新闻机构，必须确保我们提供的新闻不会过度个性化，沦为信息茧房，只是不断强化用户的既有观点。

It is still the onus of the newsroom, a responsible ethical newsroom with journalistic standards to make sure the news we give out to the people is not so personalized that it becomes an echo chamber and a reinforcement of their beliefs.

这确实很难把握，因为我们很清楚，外面的大科技公司只要走极端个性化路线，就能吸引用户、获得点击、赚到钱、提升收入。

And it's a hard thing to balance because we understand looking at big tech outside, if you go deeply personalized, you will have audience, you will have clicks, you will have money, you will have revenue.

对我们这个行业来说，要在两者之间找到平衡：既要贴近用户当前的需求，提供他们真正需要的新闻，又不能在他们还没准备好时强行灌输太多内容，同时还得确保我们的立场和观点保持公正、清晰，这一点至关重要。

And for our industry to balance both of these, meet the consumer where they are, give them the news they actually need, don't give them too much when they're not ready for it, but at the same time, make sure we are being very even and our perspective and our opinion is coming through is very important.

我觉得你描述的这种状况，其实是个特别难的‘金发姑娘问题’——做得够多，但又不能过头。

I think what you're describing is a really difficult Goldilocks problem, which is you want to do enough but not too much.

就是不能太热，也不能太软，得刚刚好。

You know, it's the not too hot, not too soft, just right.

我们既想了解这个广阔世界正在发生的一切，又希望看到那些跟自己原有观点更接近的见解。

We want to know about the whole wide world that's going on, but we also care about opinions that are closer to what my prior opinion was.

而且我经常刻意保持一些我非常不喜欢的新闻来源在生活里。

And often, you know, I try to be pretty active about keeping news sources in my life that I dislike intensely.

当你在选择关注什么、不关注什么的时候，怎么在过程中保持新闻的专业操守呢？

How do you maintain a journalistic integrity in that process then when you're choosing a lot about the kinds of things that you focus on and don't?

这种状况已经持续好多年了。

And this has been going on for years.

所以这根本不是什么新问题。

So this is not a new problem.

我觉得这是一个多层面的问题。

So I think it's a multifaceted problem.

对吧？

Right?

首先，问题其实从新闻编辑部就开始了。

Like, first, it actually starts with the newsroom.

我相信我们的编辑部，凭着他们的标准和报道方式，一直在努力呈现一个公正的视角。

I do believe our newsroom, you know, with its standards and the way they do reporting, they're trying to put a very fair perspective out.

如果你来用我们的应用，会发现其实有好多不同的使用方式。

What you will see if you come to our application is there are actually many different ways to consume.

你可以读，也可以听。

You can read it, you can listen to it.

我们刚推出了一个AI播客，AI会挑一些你可能感兴趣的文章，帮你做成播客。

We just started AI podcast where the AI chooses some articles that you might be interested in and turns it into a podcast.

你也可以去首页，那里是我们编辑团队精心挑选的内容。

You have the option of going to the homepage, which is edited by our editors, right?

这是编辑们对当下世界大事的专业解读。

So this is the expert perspective on what the world is happening.

你还可以点开‘为你推荐’标签，看专属于你的个性化新闻。

You can go to the For You tab and just read personalized news.

对我们来说，关键是要给你多种选择，同时通过好的产品设计，让你明白这些选项为什么存在。

So from our side, what we ensure is we give you many options and we educate you with good product and design why these options exist.

希望在这些选择之间，你能慢慢跳出自己的信息茧房。

So hopefully somewhere between that, you get out of your echo chamber.

不过呢，我们还想做得更多，对吧？

Now, we wanna go beyond that too, right?

如果你打开我们的首页，底部几乎会看到一个老式的滚动条，像《华盛顿邮报》网站那样，把其他新闻机构首页上的内容免费展示出来，告诉你：这些也是正在发生的事。

If you go to our homepage, you will almost see like a old style ticker at the bottom of our washingtonpost.com, where we are letting other news organizations, what they're putting on their homepage, almost run for free on our site to say, hey, these are other things that are happening.

毕竟我们不可能事无巨细地覆盖每一个角度，但得让这项服务持续扩展到全国。

Because it's quite possible, we are not gonna cover everything in every perspective and to keep extending the service to the nation.

对吧？

Right?

我觉得，作为一家新闻机构，我们真该尽力为每个人的生活带来价值。

Like, I really think we need to, as a news company, try and give value to everyone's life as much as possible.

我们最近推出了一项叫‘Ripple’的新服务。

We recently started something called Ripple.

就是 washingtonpost.com/ripple，我们走访全美各地的评论版块，把他们的内容和合作资源带到我们的用户面前。

So it's washingtonpost.com/ripple, where we are going to opinion sections across America and trying to bring their content and partnerships with them to our consumers, to our users.

这确实是个难题，但总得有人去解决，也得有人愿意推动，比如你这样的人。

And it's just a hard problem, but you do need people who are solving it and you also need people on the other side who want it to be solved, people like you.

人工智能的炒作声势很大，但实际影响却更安静、更难察觉，也更重要。

AI hype is loud, but impact is quieter, harder, and more important.

真正的AI成功，不只靠模型变得更先进。

Real AI success isn't about better models alone.

关键在于思维方式、领导力，以及技术如何真正融入日常工作。

It's about mindset, leadership, and how technology actually shows up in work.

麻省理工斯隆管理学院推出了专注于AI的课程，帮助领导者超越试水阶段，把AI真正融入决策、流程、文化和治理中。

MIT Sloan Executive Education offers AI focused courses designed to help leaders move beyond experimentation and embed AI into decisions, workflows, culture, and governance.

了解更多线上和线下课程，请访问 executive.mit.edu/aismr。

View their online and in person course offerings at executive.mit.edu/aismr.

这个想法真的很棒，就是试着从各个角落捕捉那些涟漪。

That's a really fascinating idea, the idea of trying to surface those ripples from lots of different places.

说白了，

And, you know, I mean, let's be frank.

你不可能在这方面做到完美。

You're not gonna be perfect at doing that.

但我认为这不可避免地是过程的一部分，因为不这么做带来的代价，可能远比在系统里出现一些算法问题更严重。

But I think that's inevitably part of the process, that the cost of not doing it is probably more extreme than the cost of making some algorithmic problems in there.

我知道你在做播客时也遇到过类似的问题，尤其是在个性化推荐上，想做到极致的个性化很难。

And I know you've had trouble with that with the podcast in terms of personalization and trying to get that extreme personalization.

你能跟我们分享一下这个项目进展得怎么样吗？

Can you share with us a bit about how that project has gone?

是的。

Yeah.

我们发现，在高度精选的编辑型播客之外，其实存在一个市场缺口。

We realized there is a market need in the middle of heavy curated editorial podcast.

我几乎把它们看作是专家观点。

I almost view them as expert opinions.

对吧？

Right?

这些是我们公司的专家，他们认为哪些事情是你必须知道的，而有些事虽然对世界没那么重要，但对我个人来说很关键。

These are the experts of our company who are saying these are the important things you need to know versus sometimes things that are not important to the world, but they're important to me.

我给你举个特别典型的例子，这个例子让我彻底成了这个产品的粉丝。

And I'll give you one very good example, which like really made me a fan of this product.

你还记得得克萨斯州重新划分选区那会儿吗？当时打了好多场官司。

You remember like when the Texas redistricting fight was happening and there was a lot of court cases going on.

与此同时，印度的比哈尔邦也在举行选举。

And at the same time in India, there were elections happening in the state of Bihar.

我们报道了这两件事，而根据我的兴趣，这个播客专门讲了得克萨斯的选区划分、相关法律，以及执政党如何试图保住选票。

So we covered these two stories and somehow the podcast, given my interest, talked about the redistricting, the laws and how the party in power over there is trying to hold on to the votes.

接着它又对比了比哈尔邦的选举——那里过去可能早就发生过类似操作，所以现在赢的政党，靠的就是这种选区划分带来的优势。

And then it contrasted with the elections of Bihar where some of this might have already happened in the past and therefore the party that's winning is banking on the wins coming from those type of redistricting efforts.

那一刻，我脑子里突然灵光一现，萨姆。

And it just like neurons fired in my brain, Sam.

我当时就想，天啊，这太有意思了。

And I'm like, woah, this is so interesting.

我在印度见过这种操作，现在又看到得克萨斯正在发生同样的事。

I have seen this site in India and I see what's happening in Texas.

我其实不太喜欢，不过还是谢谢你给我看了这两个。

I kinda don't like it, but thank you for showing me these two.

现在，如果你想象一下，对美国99%的人来说，专家视角下的第二个故事根本无关紧要，对吧？

Now, if you imagine an expert's view to 99% of population of America, that second story is not relevant, right?

就算他们感兴趣，也很难像这个播客让我那样，脑子里突然亮起火花。

And even if they're interested in it, it's not really gonna fire the neurons in their brains the way this podcast did for me.

我觉得，这正是我们做个性化播客想填补的空白，因为所有内容都基于我们的报道，对吧？

And I think that is the gap we are trying to really hit with personalized podcast is because this is all based on our reporting, right?

这些都是我们在《华盛顿邮报》做的真实报道，因为我们觉得这些事对世界很重要。

Like this is all factual stuff we did at Washington Post, we did it because we think this is important for the world to know.

我们和新闻团队紧密合作，反复测试过很多遍。

We worked very closely with our newsroom, we tested it very well.

当然，它不可能完美，也出过一些小错。

And yes, it's not going to be perfect, it made a few mistakes.

上线之后，我们通过设计、免责声明和提示，明确告诉用户：这是一款正在测试的实验性产品。

And once we launched it, and we made sure when we presented to our consumers with our design, with the disclaimers, with the warning, they understand that this is a beta experimental product, right?

他们明白可能会出错，所以我们整个团队都在密切监控。

They understood that there would be mistakes that happened and we were all as a team watching it very closely.

从技术上讲，我们发现当文章里大量使用第三人称指代时，系统就容易出问题。

In terms of technical, know, thing we realized was it has a lot of trouble when you have a lot of third person references in an article.

比如说，文中写‘张三这么说，李四那么讲’，接下来的句子用‘他’和‘她’来指代。

So let's just say it says, Vineet said this and Jennifer said that and the following sentences are he and then she.

对我们来说，一眼就能看出‘他’和‘她’分别指谁，但对AI来说可能就不清楚了。

To us, it's immediately clear who the he is and who the she is, to AI it might not be.

一旦我们摸清了这些问题，就回头调整了脚本和提示词，但文章原文一字没改，完全保留了新闻团队的原始写法。

And once we started figuring out those type of problems, we really went back, changed our scripts, changed our prompts, made sure that we didn't change the writing of the article, it is exactly what the newsroom wrote, so none of that is edited.

我们只是在AI处理的环节想办法解决这个问题。

We just made sure on the AI side way of solving this problem.

证明这一点的是，到目前为止，我们已经发布了超过十万期个性化播客。

And the proof of that is we have published about over a 100,000 personalized podcasts by now.

这些播客的完播率，其实比我们普通播客的完播率还要高。

The completion rate of these podcasts, it's actually higher than the completion rate of normal podcasts that we publish.

这个例子真棒，它会串联起一些东西，也可能漏掉一些，但万一它做到了，效果可能会特别惊艳。

That's a beautiful example because it's going to connect some things, it's going to miss some things, but maybe when it does, it's going to be amazing.

我们节目里一直有个不变的主题，就是不断改进。

And one of the kind of enduring themes of our show seems to be this exact idea of improvement.

我们早期请过一位嘉宾，提到过一句话：第一天总是最糟的一天。

One of our early podcast guests mentioned the idea that the first day is the worst day.

当你把这个实验推出来，就会发现一些问题，比如你刚才说的代词难题，还有我们一眼就能看出哪个故事该连到哪个上。

So when you put this experiment out, you're going to discover some stuff like the pronoun problem you mentioned and how it's obvious to us which story connects to which one.

但这些问题都会被慢慢解决，系统也会越来越完善。

But you're gonna fix those, and it'll keep improving.

你对这个产品、这个个性化播客，接下来有什么打算？

What's your plan for this product, for this personalized podcast?

已经做了十万集了。

A 100,000 episodes.

我真是有点羡慕了。

I'm I'm already quite jealous.

我觉得我们现在刚过一百集，已经累得够呛了。

I think we're just over a 100 and it's been exhausting.

我觉得这并不能取代专家，你知道的。

Well, I don't think it replaces the experts, you know.

一百集已经够费劲了，但十万集对开发团队来说依然是巨大的工作量，毕竟我们得审阅他们遇到的各种问题。

A 100 is a lot of work, 100,000 is still a lot of work on the team who's building it because we review problems that they come in.

所以工作其实还是有的，只是换了个方向——对我们来说，主要是在质量把控这边。

So the work happens, I guess, on the different side, you know, for us it happens on the QA side.

不过，不如我们先跳出个性化播客这个话题，聊聊我们这边在AI方面做的其他事情。

But I would just zoom out of personalized podcast and maybe talk more about the AI efforts we are doing over here.

那样的话，你就能全明白了。

And then it would all make sense.

对吧？

Right?

我们公司对AI的看法是，AI无处不在。

The way we are viewing AI in our company is we call it AI everywhere.

对吧？

Right?

我们采取的是AI无处不在的策略，希望把它用在新闻制作的各个环节。

It's a AI everywhere approach where we want it in the production of the news.

生成式AI能做的事情太多了。

There's so much Gen AI can do.

我们有个叫Haystacker的工具，能快速处理数小时的视频内容，这原本得花人几周时间。

We have a tool called Haystacker, which can go through hours and hours of videos, you know, what would take people weeks.

现在记者可以直接说：我想找那个戴红帽子的人，查一下1月6号的所有视频，快速获取相关信息。

And now our journalist can go and say, I want to find that person with red cap, you know, and go through January 6, write videos and get that type of information.

你可能已经听说了，现在大数据集再也不让记者发怵了。

You have probably heard all about how big data sets are now no longer a thing journalists fear anymore.

他们不用再手动逐字阅读了。

You know, they don't have to manually read it.

他们可以直接向系统提出有深度的问题。

They can really ask it intelligent questions.

所以我们内部正在开发很多这样的工具。

So we are building a lot of tools internally for that side.

所以第一个重点是用人工智能来支持新闻报道的核心使命。

So that's one big pillar is use AI to help the core mission we have of journalism.

第二个方向是面向消费者的领域。

The second thing where we go is consumer facing.

像AI播客、Ask the Post、AI摘要这些产品都属于这一块，我们觉得在人工智能这场变革里，用户其实比我们走得更快，对吧？

That's where AI podcast, Ask the Post, AI summaries, all of these products come in where we have taken an approach that the audience, in the case of the AI revolution, I feel like the audience moved before we moved, right?

当年互联网兴起的时候，人们得自己买电脑，学着上网，还得用浏览器。

When there was an internet revolution, people had to go buy computers, they to learn it, they had to get on the web browsers.

然后新闻机构才慢慢建起了网站。

And then the newsrooms moved to a website.

但在人工智能时代，用户一夜之间就全都冲上去了。

In the world of AI, the audience went overnight.

我想稍微反驳一下你提到的这个Haystacker工具。

I wanna push back, you know, a little bit on this haystack.

这个名字我很喜欢。

I I really like that name.

你的意思是，想用人工智能在那堆干草里把所有针都找出来，对吧？

What you're saying is, hey, you want to go through that haystack and do it with artificial intelligence and find all those needles.

确实，现在世界上要处理的内容多得惊人，数量简直让人咋舌。

And it's certainly true that we've got a lot more content in the world to go through, you know, that it's staggering the amount of things that are happening.

我们每天收到的内容越来越多。

We're getting a lot more content.

这些内容里，真正有价值的线索变多了吗？

Are there more needles in that content?

还是说，我们只是更擅长发现那些本来就存在的有价值信息？

Or is there better discovery of the existing needles?

还是说，你筛的那堆干草里，大部分都是没人要的垃圾内容？

Or is there a lot of the hay that you're sifting through just a lot of left tail junk.

这么说你能明白吗？

Does that make sense?

一想到干草堆，我就想，干脆把整个堆都扩大。

When I think about a haystack, I think about, okay, let's grow the whole pile.

堆得越大，针自然也会越多，因为干草多了，但说不定只是把那些针藏得更深了。

And when we grow the whole pile, we'll have more needles because we've got more hay, but we may just be hiding those other needles better.

对，这两种说法都对。

Yeah, both things are right.

我先说说为什么叫‘找针者’这个项目，名字是这么来的——我们之所以在找针，是因为我们手头本来就有一个干草堆。

So let me start with just because I used the Haystacker project, the name came, we are finding a needle in a haystack because we actually already had a haystack.

有人给了记者一大堆视频。

Somebody gave a reporter a lot of videos.

有人给了记者一堆数据，说：嘿，那边有点不对劲。

Somebody gave a reporter a lot of data and said, Hey, something's going on over here.

他们得花上两三周才能看完这些材料。

And it would take them two, three weeks to go through it.

所以我们只是帮他们加快了这个过程，对吧？

So we just help them, right?

我们是在帮他们找那根针，而不是让他们一帧一帧地看。

Like we are helping them find that needle instead of them watching it frame by frame.

所以这个工具的起源就是这样的。

So that's really the origination of this tool.

而这只是很多新闻机构正在开发的众多工具之一。

And this is one of the many tools that a lot of news companies are building these tools.

但回到你更大的问题——现在数据量确实大得多，而其中大部分其实并不重要，对吧？

But going back to your bigger question of like, well, there is just a whole lot more data, most of it is not interesting, Right?

我们不认为AI的职责是不经过记者介入，就自动找出所有有趣的内容并推给你。

We don't think it is the job of AI to find all those interesting things and serve them to you without a journalist involved in the middle.

对吧？

Right?

记者通常依靠直觉提出问题，试图挖掘更多信息。

So the journalist is usually with their instinct asking questions, trying to find more out of it.

我相信你完全可以进入一个数据源经过精心筛选的世界。

And I'm sure you can get to a world where you have really curated data sources.

你可以把劳工部的报告剔除出来，对吧？

You can take like Department of Labor reports out, right?

我们的记者会用这些报告，从中写出报道。

And our journalists use those reports and they create stories out of it.

当你去问《邮报》：2013年农业领域的失业率是多少？

So when you go to ask the Post and you say, hey, what was the unemployment rate in 2013 in agriculture sector, right?

我们可能没在新闻里专门写过，但要是这属于记者们信赖并常使用的数据源，我觉得用它来回答问题完全合理，对吧？

We may or may not have written about it in a news article, but if this is one of those data sources that our journalists trust and use, I think it's fair to use it and give the answer to the question, right?

但关键是，整个过程有新闻团队参与，确保数据经过核实。

But once again, there is a newsroom in the loop, you know, like that verification of data.

我觉得这让我们比那些在互联网上盲目抓取信息的通用搜索引擎要更靠谱一些。

And I think that makes us for a little bit higher quality than the general purpose Internet, you know, hoovering ask engines.

它们自有其存在的价值。

And they have their own place.

我并不是在贬低它们。

I'm not taking a dig at them.

我只是说，那事儿另有其位。

I'm just saying there's a different place for that.

我们在《华盛顿邮报》想打造的是：如果你在找值得信赖的新闻和深度报道，想要确凿的事实和安心的保障，那就从我们开始。

And what we are trying to build over here in Washington Post is if you are in the market for trusted news and journalism and you want some verified facts and have confidence, you should start with us.

对。

Right.

我觉得咱们还是回到你一开始说的那部分吧。

And I think let's tie back to how you started this process.

你一开始讲的是‘为什么’。

You started talking about why.

现在这个‘为什么’也得包含在内。

And right now that why has to be part of that.

不然就像你说的，这跟那些有用的搜索引擎形成了鲜明对比——它们只给你列清单，却不解释原因。

Otherwise, like you say, that's a sharp contrast between the useful search engines, which produce a list but do not produce the why.

不过说到这儿，我想起现在的搜索技术，好像正试图用人工智能，往‘为什么’和更多解释的方向走。

As I say that, though, I think about modern search technology, and it seems to be trying to use artificial intelligence to move towards more of a why and more explanation.

但你之前对记者在这个过程中的作用说得非常清楚。

But you were pretty clear about the role of your journalist in this process.

能不能再详细说说这一点？

So, maybe expand a little bit on that.

你们在哪些环节用了自动化？

Where are you automating?

哪些地方必须依赖人的判断？

What absolutely requires human judgment?

你们是怎么界定这些界限的？当然我们可以举具体例子，但你们的决策流程是怎样的？

How are you figuring out where those lines are about what can and we can talk about individual examples, but what's the process for figuring out how to decide?

这其实又回到了公司对AI使用的治理和政策问题上。

So it goes back to AI governance and policies around how we are using AI in the company.

我们把它分成了三个部分。

And we broke it down into three parts.

对吧？

Right?

我先说最简单的一个，就是信息安全。

The easiest one I'll talk first is InfoSec.

我们让信息安全团队参与进来，跟他们说：你们得告诉我们怎么别把事情搞砸了。

We got our InfoSec team involved and we said, like, listen, you need to tell us how to not mess it up really bad.

你们得告诉我们目前安全方面有哪些风险，然后制定一套政策——这对我们来说比较容易，因为我们用的是部署在私有服务器上的大语言模型。

You need to tell us what's happening on the bubble in terms of security and put a policy out, which is easier for us because we are using a LLM that we are hosting on a private instance.

接下来是新闻编辑部的部分，对吧？

Then comes the newsroom aspect, right?

记者和编辑们坐在一起，自己决定了AI该怎么融入他们的工作，怎么使用它，又该怎么标注它的贡献。

Like the newsroom and the journalist sat down and they've decided for themselves how they want AI to show up in the work they do, how they will use it, How will they attribute to it?

哪些能做，哪些不能做？

What are the do's and don'ts?

对吧？

Right?

第三个方面是用户。

And then the third aspect is the consumer.

这个部分比较棘手，因为一般人会把这当成一个产品问题。我们采取的做法是依靠良好的设计，始终明确告诉用户他们正在看的内容，以及其中有多少是AI生成的。

Okay, this is the tricky aspect because this is what you typically think of as a product and the approach over here we have taken is using good design, we want to always inform our consumers, our audience, what they are consuming, how much of this is from AI.

这其实是个连续谱，对吧？

And it's a spectrum, right?

拿摘要来说吧。

Let's take the example of summaries.

我们还是会标注‘这是AI生成的摘要’。

We still label AI summaries that this is an AI summary.

但我观察到，大家使用的方式，还有真正去看免责声明或者点踩的人，比例其实越来越低了。

But the way I see people use it and the number of people who are actually looking at the disclaimer or giving us a thumbs down button on it because they didn't like it, it's moving down, right?

现在几乎没人会对AI摘要感到惊讶，用户也完全不介意。

Like it's almost to the point that nobody is shocked that we have an AI summary and none of the users are bothered about it.

但要是我们突然放出一段完全由AI生成的视频——虽然我们目前没这么做，以后也不打算做——那我们一定会加更醒目的提示。

But I'm pretty sure if we put a full AI generated video, which we haven't done so far and we don't plan on, we would put stronger disclaimers.

所以在产品层面，我们想借助设计和用户行为，确保大家在使用任何内容时，都能清楚知道它是不是AI生成的。

So at a product level, we want to lean on design and consumer behavior to make sure we are always informing them when they're using something is AI or not.

不过咱们先跳到后面看看。

Let's jump forward though.

假设十年后我们坐在这儿，你肯定得想想新闻体验未来会变成什么样。

You know, if we were sitting here together in a decade, you've got to be thinking about the direction that the news experience is going.

你提到过，从阅读新闻到听新闻、再到看新闻，这个演变过程。

And you've mentioned the read the news, listen to the news, watch the news progression that's happened.

你对这个话题想得很多。

You've thought about this a lot.

那你觉得未来十年左右会发生什么？

So tell me what you think is gonna happen in the next decade or so.

哎呀，我要真有那么聪明，萨姆。

Oh, if I was that smart, Sam.

那你就不跟我聊天了？

You wouldn't be talking to me?

我早就在纽约搞对冲基金，下注赚钱去了。

I would be somewhere in New York in the hedge fund business making my bets.

好的。

Okay.

让我回放一下。

Let me listen this back.

下个月。

Next month.

下个月我们可以做得更短一些。

We can go shorter next month.

你能不能先透露一点下个月的内容，我们再据此延伸拓展？

Maybe you can give us a little hint about next month, and we can try to expand from there.

是的。

Yeah.

我真心觉得，人们对新闻和优质新闻的需求从未如此强烈。

Do sincerely believe the need for news and quality news has never been more.

新闻业是一种专业素养，而不仅仅是一种形式。

Journalism is a discipline, not just a format.

我们必须不断调整新闻的呈现方式，善用技术来辅助工作，这正是《华盛顿邮报》打算持续做的事。

We need to keep adapting our journalism to different formats, use technology where it can help us, and that's what we intend to keep doing at Washington Post.

你接下来可能会听到一些关于‘流动内容’的想法，对吧？

You will start to also probably hear, you know, the ideas around like liquid content, right?

试着像我们这样看待内容本身。

Like, so think about the content the way we do.

通常来说，新闻的生命周期只有24小时。

Typically news last twenty four hours, right?

24小时一过，每个新闻编辑部都会说，这则新闻已经过时了。

After twenty four hours, every newsroom will tell you the story dropped off.

首页撤了，大家也不再讨论了。

Do they take it off the homepage, people stop talking it.

如果你做的是深度调查报道，可能能撑上七天。

You do a deep investigative piece, maybe you go seven days, right?

我们会把它置顶，大家会转发，它的影响力也会持续更久。

Like we will pin it somewhere, people will share it, it will have longer legs.

但不管怎样，过后就没人再关注了。

But no matter what after that, it just drops off.

我觉得未来会是这样：人们的兴趣决定新闻的价值。

I see a world going where people's curiosity drives the news.

新闻可以以无限多种形式长期存在，因为随时有人会回来提问。

News can literally live in an infinite forms for a long period of time because somebody could come back and start asking bunch of questions.

他们可能会问：你能帮我整理一份关于ICE在华盛顿和明尼苏达行动策略变化的报告吗？

They could start asking questions or they could say, can you help me write up a report in the change in ICE task tactics between DC and Minnesota?

我真的很想了解，当时世界上到底发生了什么，让事情比过去更暴力了。

I really wanna understand what was happening in the world at that time that it became more violent than it used to be in the past.

我觉得这其实释放了更多新闻的可能性。

So I do think this unlocks more news.

它反而让市场扩大了，而不是像大家最初担心的那样萎缩——那种担忧一直都在。

It actually grows the market more than initially the fear of shrinking and that's always the fear, right?

每当新技术出现，第一个反应总是担心会缩水。

Like when a new technology comes, first is a very genuine fear of shrinking.

我可不是想否认这一点，对吧？

Like I don't wanna deny that, right?

说实话，作为一个工程师，我看到Claude Code过去两个月的进展，真有点震惊，我那条备选职业道路怕是没戏了。

Like honestly as a engineer, you know, I see what Claude Code has done in the last two months and I'm like, woah, there goes my backup career choice.

看来我没法再当个写短小Java代码的程序员了。

I guess I'm gonna not be a super short Java programmer anymore.

但只要跨过恐惧这道坎，我觉得这反而会带来增长。

But once you get past the fear, I think this grows.

只要人类还有好奇心，还有获取真实新闻和事实信息的需求，AI就会推动我们向前发展。

AI helps us grow as long as people and their curiosity and the need to get verified news information facts exist, this is going to be good.

所以，这就是所谓的熊市。

So there's the bear.

股市里那个看涨的势头叫什么来着？牛市。

What do you call in stock market, the positive Bull

还有熊市。

and bear.

你要真打算转去对冲基金，那倒是可以这么做。

You you do that if you're gonna switch to hedge fund.

牛市代表上涨，熊市代表下跌。

Bull is positive, bear is negative.

看来我未来的职业选择真是没剩几个了。

As you're realizing my future career choices are quite limited.

你还是老老实实写Java吧。

You better stick with Java.

我还是继续写Java。

I'll stick with Java.

但我确实也看到，信任这个问题是有风险的，对吧？

But I also do see there is risk around trust, right?

当我展望未来时，最让我担心的是，过去人们对媒体品牌的信任。

And when I look at the future, the thing that worries me the most is the trust of consumers used to be with the mastheads, right?

以前人们看报纸，是因为相信它们有标准、有流程、有专业团队，对吧？

You would read a newspaper because you trusted that there were standards and procedures and professionals, right?

而在我们这一代，我发现信任逐渐转移到了内容创作者身上。

And then in our lifetime, I see the trust move to creators.

人们开始更信任创作者了。

People started trusting creators more.

他们更容易被推特上的人影响。

They were more influenced by people on Twitter.

他们更愿意相信Instagram和TikTok上那些传递新闻的人，我一直在想这个问题。

They were more influenced by Instagram and TikTok people who were telling them the news, and I thought about it.

我就在想，那边到底发生了什么？

And I'm like, you know, what's going on over here?

一方面，我们的新闻媒体没能跟上变化的速度。

One is, like, our news did not adapt fast enough.

对吧？

Right?

确实如此。

That's true.

我们没有走到消费者身边去。

We did not meet the consumer where they are.

但作为人类，我们本来就更信任其他人类。

But we as humans just generally trust other humans.

我们相信声音，相信语言，不管你身在世界哪个角落，只要有人跟你说话，哪怕听不懂，你也能感觉到对方是有智慧的。

We trust voice, we trust language, you know, no matter what part of the world you are, if somebody speaks any other language, you know that you're in company of intelligence.

其实如果让我回到苹果的那些年，我们有个小故事，就是Siri刚出来的时候，那是第一个语音交互功能，人们可以直接跟机器说话。

In fact, if I could go back to my Apple days, we had this anecdote, like when Siri came, it was the first voice, right?

那时候，Siri是人们第一次能跟设备用对话的方式互动。

Like it was the first voice interaction with your machines, people could talk to it.

结果就在同一时期，苹果地图也上线了，我们出了几次数据错误，有人被导航带到土路上，困在那儿了。

And then Apple Maps came at the same time, and we had few incidents where we had wrong data and people would go on dirt roads and get stuck.

我们当时反复听到的抱怨是：‘可是Siri告诉我往那儿走啊。’

The consistent complaint, Sam, we used to get is, well, Siri told me to go there.

那一刻我们才明白，Siri的声音和苹果的品牌声音是一样的，这其实是个问题——人们给它的信任，远远超出了它该有的程度。

And that's when we realized the Siri voice and Apple voice being the same voice was actually a problem because they were putting more trust in it than they should.

他们明明看到那条路根本不存在，却还是照着Siri的指示右转了。

Their eyes were showing this road doesn't exist but they would turn right because Siri told them so.

我觉得我们的情况也是这样，信任从品牌标志转移到了人身上，毕竟作为人类，我们自然更信任其他人类一些。

So I think this is what happened to us is the trust moved from mastheads to people because naturally as humans we trust other humans a little bit more.

让我担心的是，当这些AI变得比创作者更像真人——能跟你对话、高度个性化、比创作者更懂你的时候，人们的信任可能会进一步转向AI，甚至超过对人的信任。

What worries me is as these AIs become almost a better human than a creator because they can talk back to you, they can be deeply personalized, they can understand you more than a creator does, I fear the trust will move to the AIs even more than it was with the humans.

既然如此，我们该怎么办？

Now, given that, what do we do?

对吧？

Right?

我的推测是，人们对AI的信任会非常深，关系也会变得极其紧密。

That's my hypothesis that trust to AI that people will have, the relationship we have will be very deep.

我觉得新闻界和媒体行业有责任去打造同等水平的体验，别让消费者只能被困在少数几个大平台里，对吧？

I think the onus is on us in the news, in the journalism world to build that equal type of experiences so the consumer doesn't just get locked in with couple of big options that exist in the world outside, right?

当我看到MCP协议这样的东西出现时，我就觉得有希望了。

And I feel hopeful when I see things like MCP protocol come out.

模型上下文协议

Model context protocol.

上下文协议

Context protocol.

我看到智能体之间开始进行对话了

I see agent to agent conversations happening.

我看到不少公司，不管是大科技企业还是初创团队，都在朝这个方向努力：如果我的智能体需要新闻，我就让它和你的智能体对接，获取真正可靠的信息

I see enough companies out there, big tech, small tech startups, who are working down this path of saying, hey, if my agent needs news, I wanna connect it with your agent so it can get like the right verified news.

所以我同样抱有希望，但也非常担心信任的问题

So I'm hopeful also, but I'm also very worried about the trust.

我只是希望信任能留在真正配得上它的人手里

I wanna just make sure it stays with people who deserve it.

实际上，这里面有四五点特别有意思

Actually, there's four or five things that are pretty fascinating there.

第一，我之前还真没想过这种信任会从一个Siri产品转移到另一个，我的直觉反应，或者说天真的想法是，嘿

One, I had not really thought about that transfer of trust between the different Siri products and how that my gut reaction, my naive approach would have been to say, hey.

这种信任能够传递，确实不错。

That's good that that trust transfers.

但你提到的这一点很关键：当两个产品的准确率基础不同时，可能就不该轻易传递这种信任。

But what you pointed out is that when you have two different products with different base levels of accuracy that you might not want to transfer that trust.

你这个角度挺有意思，我以前从来没这么想过，总觉得信任越多越好。

That's an interesting way of thinking about that that hadn't I've naturally thought, hey, more trust is better.

但其实你可以通过声音来传递信号，比如用更机械的语音，暗示用户：这件事别太当真。

But you can actually signal this is something that should not be trusted with a more robotic voice, for example.

你刚才提到了Siri。

You touched on Siri.

咱们先退一步，聊聊你并不是一直都在《华盛顿邮报》的经历。

Let's back up here and and talk about how you have not always been at The Washington Post.

跟我们说说，你是怎么走到今天这一步的？Siri在这个过程中扮演了什么角色？

Tell us a little bit about how you got to where you are there and Siri as a part of that journey.

其实，我大学时第一次接触到人工智能，就被那种机器能替我搞定一切的想法迷住了。

Well, I mean, back in my undergrad days, got introduced to AI and I kinda got seduced by the idea of machines doing all the work for me.

我当时就觉得，这太棒了。

So I was like, this is great.

于是我决定去读人工智能的硕士，这样就能躺着享福了。

Well, I'm gonna go get a master's in artificial intelligence, you know, so I can just sit back and relax.

后来我就进了抵押贷款行业，做了第一份工作。

And that led to my first job in the mortgage industry.

我们当时开发了一些用于贷款的AI模型。

We used to do these AI models for loans.

如果你还记得2007年，那会儿发生了严重的房贷危机和金融崩溃，整个行业瞬间崩盘。

And if you remember the year being 2007, when the great mortgage crisis and the financial collapse happened, my entire industry got wiped out.

结果发现，根本没人听AI的建议来处理贷款。

Turns out nobody was listening to AI when it came to loans.

但一扇门关上了，另一片天地却打开了——我那时在贡献一些开源代码。

But that one door closed and a universe opened, I was contributing some open source code.

Siri的创始团队看到了我的代码。

The founders of Siri saw my code.

他们邀请我去参加面试。

They invited me to apply for an interview.

我就去了硅谷，接下来十年都跟他们一起开发Siri。

So I went over to Silicon Valley, and then I spent, like, the next ten years working with them building Siri.

我们是当时第一个语音驱动的AI，而且在Alexa和谷歌助手出现之前，一直都是唯一的。

And we were the first voice driven AI for our time and for the longest time till Alexa came and Google Assistant came and that whole universe opened up.

干了大概十年后，我来了个大转弯，转去做了Uber地图。

Spending about ten odd years, I took like a hard right turn and I went into Uber Maps.

我带团队开发路线算法，特别有意思，全是图搜索，纯正的计算机科学，对吧？

I ran the team that was building the routing algorithms and it was a whole lot of fun, it was graph search, it was hardcore computer science, right?

图搜索，这可是计算机科学最硬核的部分了。

Like this is as computer science as you get as graph search.

那段经历我真的特别喜欢。

So I really loved that stint.

干了四年左右，大语言模型突然就火起来了。

And after doing that for about four years, LLMs came on the scene, right?

然后我就想，好吧，我要回到我老本行——自然语言处理，想在那边做点什么。

And then I just was like, okay, I'm going back to my old world of natural language processing and I wanted to do something over there.

所以我从优步请了段时间假，打算重新学习一下。

So I took some time off from Uber, I thought I'm gonna reeducate myself.

我还买了些园艺工具。

I bought some gardening tools.

我老婆可真急了。

My wife got really worried.

她问我：你打算重新学习多久啊？

She's like, how long are you going to reeducate yourself?

你这工具也太多了吧。

You have too many tools over here.

但就在这时，《华盛顿邮报》的机会来了，我脑子里的所有神经一下子全激活了。

But this Washington Post opportunity came and all the neurons in my brain fired.

我当时就想，这场革命说到底，核心就是语言。

I said like, listen, this revolution is all about language.