How to stop Al from killing your critical thinking - Advait Sarkar

You're listening to ted talks daily where we bring you new ideas to spark your curiosity every day. I'm your host, Elise Hugh. What happens when we start to let AI think for us? It's a big question right now, and is it actually making us smarter and more efficient, or is it hindering our ability to think critically?

你每天都在听ted演讲，我们每天都会给你带来新的想法，激发你的好奇心。我是你的主持人，Elise Hugh。当我们开始让AI为我们思考时会发生什么？现在这是一个大问题，它实际上是让我们更聪明、更有效率，还是阻碍了我们批判性思考的能力？

In this talk, Advait Sarkar, a researcher at Microsoft, examines the cognitive trade offs of AI at work and sketches a different kind of tool, one that promotes critical thinking and nudges reflection to help you get smarter and not just faster.

在这次演讲中，微软的研究员Advait Sarkar研究了人工智能在工作中的认知权衡，并绘制了一种不同的工具，一种促进批判性思维和推动反思的工具，帮助你变得更聪明，而不仅仅是更快。

I'm here today to talk about thinking for yourself.

我今天在这里谈论的是为自己思考。

And I must admit, I did use AI to help me think about it. The irony not lost on me, but the way I did so is not by using AI as an assistant to help me prepare this talk faster. Rather, I use AI as a tool for thought.

我必须承认，我确实使用了人工智能来帮助我思考这个问题。讽刺的是，我并没有忘记，但我这样做的方式并不是使用人工智能作为助手来帮助我更快地准备这次演讲。相反，我将AI用作思考的工具。

And by the end of this talk, I will have explained what I mean by that, why it's important and given you a glimpse of how it might work.

在这次演讲结束时，我将解释我的意思，为什么它很重要，并让你一瞥它是如何工作的。

But first, I need to set the scene. Let's look at a day in the life of a twenty first century knowledge worker. I arrive at my office and look at my inbox full of emails.

但首先，我需要设定场景。让我们来看看21世纪知识工作者的一天。我来到办公室，看着收件箱里塞满了电子邮件。

Let's summarize it. OK? I'm struggling to figure out how to respond here, so let's get AI to write a response.

让我们总结一下，好吗？我正在努力弄清楚如何在这里做出回应，所以让人工智能写一个回应。

Next, I need to write a report that I'm struck by the blank page problem.

接下来，我需要写一份报告，说明我对空白页问题感到震惊。

I know I'll drop in some resources and get an AI draft. Looks good to me. By the way, the writer's block used to be staring at a blank page. Now it's staring at a page that AI filled out for me and wondering if I agree with it. I've become a professional validator of a robot's opinions.

我知道我会投入一些资源，得到一份人工智能草稿。在我看来不错。顺便说一句，作家的障碍曾经是盯着一张白纸。现在，它正盯着AI为我填写的页面，想知道我是否同意。我已经成为机器人意见的专业验证器。

I've got some data to analyze. Maybe AI can analyze this data for me.

我有一些数据要分析。也许AI可以为我分析这些数据。

Probably correct.

也许是对的。

OK, I've got to make a deck as well. You know the drill.

好吧，我也得做一个甲板。你知道规矩。

All right. Oh, I was supposed to prototype something as well. OK, let me vibe code something. All right, all this looks good. Let's go.

满意哦，我也应该做一些原型。好的，让我来感受一下编码这些东西。好吧，这一切看起来都很好。走吧。

This isn't a vision of the future. This is a completely plausible, if slightly exaggerated, picture of the world of knowledge work today.

这不是对未来的展望。这是当今知识工作世界的一幅完全合理的图景，尽管有点夸张。

Welcome to the age of outsourced reason.

欢迎来到外包理性的时代。

Where the knowledge worker no longer engages with the materials of their craft, we've become intellectual tourists in our own work. We visit ideas. We don't inhabit them.

当知识工作者不再与他们的工艺材料接触时，我们就成了自己工作中的知识游客。我们访问想法。我们不住在那里。

Our relationship to our work is entirely intermediated by AI, some might say alienated. We've heard that story before.

我们与工作的关系是完全由人工智能中介，有些人可能会说是异化的。我们之前听过这个故事。

What I want to focus on today is that using AI in this way can have profound implications on human thought.

今天我想重点谈谈，这样使用AI的方式可能对人类思维产生深远的影响。

Consider creativity. On an individual level we might think that AI is a creativity boost, giving us rapid access to new ideas. But numerous studies have shown that, on a collective level, knowledge workers using AI assistants produce a smaller range of ideas than a group working manually.

考虑创造力。在个人层面上我们可能会认为人工智能是一种创造力的提升，让我们能够快速获得新想法。但许多研究表明，在集体层面上，使用人工智能助手的知识工作者产生的想法范围比手动工作的群体要小。

We've created a hive mind so the hive is really boring and keep suggesting the same five ideas.

我们创造了一个蜂巢思维，所以蜂巢真的很无聊，一直在提出同样的五个想法。

Consider critical thinking.

考虑批判性思维。

We surveyed knowledge workers about their use of AI.

我们调查了知识工作者对人工智能的使用情况。

They reported that they put less effort into critical thinking when working with AI than when working manually, and this fact was greater when they had greater confidence in AI and less confidence in themselves.

他们报告说，与手动工作相比，他们在使用人工智能时对批判性思维的投入更少，当他们对人工智能更有信心而对自己没有信心时，这一事实就更大了。

Consider memory. When people rely on AI to write for them, they remember less of what they wrote. And when they read AI generated summaries, it's hardly surprising that they remember less than if they'd read the document. 考虑一下记忆。当人们依赖人工智能为他们写作时，他们对所写内容的记忆会减少。当他们阅读人工智能生成的摘要时，他们比阅读文档时记得更少，这并不奇怪。

And finally consider meta cognition, which is the ability to think about your own thinking process. Working with AI requires significant metro-cognitive reasoning about your task goals, decomposing the task, the applicability of Gen AI, your ability to evaluate the output.

最终考虑元认知，即思考自己思维过程的能力。使用人工智能需要对任务目标进行大量的计量认知推理——分解任务、Gen AI的适用性、评估输出的能力。

These are things which are built into the process of working directly with the material, and which become problematic when that material engagement becomes intermediated. Basically, we've become middle managers for our own thoughts.

这些都是直接使用材料的过程中固有的东西，当这种材料参与成为中介时，就会出现问题。基本上，我们已经成为了自己思想的中层管理者。

So what's the score?

那么，比分是多少？

We have fewer ideas. We think about them less critically. We remember them less well, and we have a harder time doing it.

我们的想法更少。我们不那么批判性地思考它们。我们对他们的记忆不太好，而且我们很难做到这一点。

Taken together, we can see that AI assisted workflows can have profound effects on human thinking, and this extends even to seemingly trivial mundane tasks, because these everyday opportunities for exercising our creativity, our critical thinking and our memory are essential for protecting our cognitive musculature and allow us to rise to the occasion when an exceptionally complex task comes our way.

总的来说，我们可以看到，人工智能辅助的工作流程可以对人类思维产生深远的影响，这甚至延伸到看似微不足道的日常任务，因为这些锻炼我们创造力、批判性思维和记忆力的日常机会对于保护我们的认知肌肉组织至关重要，并使我们能够应对异常复杂的任务。

Studies show that when we don't use our brains, they get worse at brain things. Nobel prize committee, please hold your applause.

研究表明，当我们不使用大脑时，大脑会变得更糟。诺贝尔奖委员会，请不要鼓掌。

Is this the cost of progress?

这就是进步的代价吗？

We solved the problem of having to think.

我们解决了不得不思考的问题。

Unfortunately, thinking wasn't actually a problem. It's like, it's like we invented a cure for exercise and then wondered why we're out of breath all the time.

不幸的是，思考实际上并不是问题。这就像，就像我们发明了一种治疗运动的方法，然后想知道为什么我们总是上气不接下气。

Doesn't have to be this way. Beyond AI as an assistant, I believe that AI should be a tool for thought. AI should challenge, not obey. And I believe that right at this moment we are at a critical juncture, where the world of work is poised to be transformed by generative AI.

不必这样。人工智能不应作为一名助手，我认为人工智能应该是一种思考工具。AI应该挑战，而不是服从。我相信，此刻我们正处于一个关键时刻，在工作世界即将被生成性人工智能改变的地方。

And we must act now to shape and drive that transformation towards humanistic values. Of these two diverging roads, we must take the one less troubled.

我们现在必须采取行动，塑造和推动人文价值观的转变。这两条不同的道路，我们必须选择一个不那么麻烦的。

Beyond getting the job done, a tool for thought helps us better understand the job. 除了完成工作，思考工具帮助我们更好地理解工作。

Beyond getting it done faster, it helps us get it done better.

不仅仅是更快地完成工作，它能帮我们做得更好。

Beyond getting us to the right answers, a tool for thought helps us ask the right questions.

除了让我们得到正确的答案，思考工具还可以帮助我们提出正确的问题。

Beyond automating known processes, it helps us explore the unknown.

除了自动化已知流程外，它还帮助我们探索未知领域。

What does this look like? What I'm about to show you is a prototype developed by my colleagues and me at the tools for thought team at Microsoft research in Cambridge. 这看起来像什么？我要向大家展示的是我和我在剑桥微软研究院思想工具团队开发的一个原型。

Now please bear in mind that this is a live research prototype. It's not a product, and it's just one of a series of explorations that our team is conducting to study how different modes of working with AI can enhance human thought. So let's look at a fictitious example.

现在请记住，这是一个实时研究原型。这不是一个产品，它只是我们团队正在进行的一系列探索之一，以研究不同的人工智能工作模式如何增强人类思维。让我们来看一个虚构的例子。

Clara and her colleagues run a company that sells bottled beverages. They've just had a meeting to discuss a new industry report that seems to have some pretty important findings about consumer preferences for sustainable packaging.

克拉拉和她的同事们经营着一家卖瓶装饮料公司。他们刚刚开会讨论了一份新的行业报告，该报告似乎对消费者对可持续包装的偏好有一些非常重要的发现。

Clara's colleagues have asked her to write a proposal arguing for how the company ought to respond, so she really needs to get to grips with this report, understand its findings and its data, and how it fits into her business context. She starts by loading some documents into her workspace.

Clara的同事要求她写一份提案，说明公司应该如何回应，所以她真的需要掌握这份报告，了解其调查结果和数据，以及它如何适应她的业务环境。她首先将一些文档加载到她的工作区中。

There's the meeting transcript to remind her what was discussed. There's a recent internal report from her own business, and of course, there's the industry report, which she opens.

这是会议记录，提醒她讨论了什么。这是她自己公司最近的一份内部报告，当然还有她打开的行业报告。

She sees an overview of the document along with section by section summaries. Except these aren't really just summaries. We think of them more as lenses. They're customizable micro representations of the text that can emphasize what is most relevant to the task at hand.

她看到了该文档的概述以及逐节摘要。但这些并不仅仅是总结。我们更多地将它们视为透镜。它们是文本的可定制微观表示，可以强调与手头任务最相关的内容。

So in this case, Clara selects the consumer lense. She can select a section for deeper reading. As she reads she makes notes about her thoughts and highlights excerpts from the document.

因此，在这种情况下，Clara选择了消费者透视。她可以选择一个部分进行更深入的阅读。在阅读时她记录了自己的想法，并强调了文件的摘录。

As she reads, she also sees AI generated commentary and critiques. We call these provocations. Note how this process is a hybrid of completely manual reading and completely relying on AI to read for you.

在阅读时，她还看到了人工智能生成的评论和批评。我们称之为挑衅。请注意，这个过程是完全手动阅读和完全依赖人工智能为您阅读的混合体。

Clara still reads, but intentionally and strategically. Now as Clara is working, she's building up an outline of her argument manually. This outline is lightly structured and allows her to sketch out the flow of her argument at a high level what's still retaining deep connections and being grounded in the source documents, as a result of which we can already generate a draft of the proposal.

克拉拉仍然在读书，但是有意和有策略的。现在，克拉拉正在工作，她正在手动构建她的论点大纲。该大纲结构简单，使她能够在高层次上勾勒出她的论点流程，同时仍然保持深层次的联系，并以源文件为基础，因此我们已经可以生成提案草案。

And Clara can do things here like add heading to the outline to generate a paragraph. But what I want to draw your attention to here is that while this text is AI generated, Clara has a completely different relationship to this text than if she just dropped in some documents and said write me a report, because this text is deeply rooted in a cognitively effortful but interactionally effortless thought process.

Clara可以在这里做一些事情，比如在大纲中添加标题以生成段落。但我想在这里提醒你的是，虽然这篇文章是人工智能生成的，但Clara与这篇文章的关系与她只是在一些文件中说给我写一份报告完全不同，因为这篇文章深深植根于一种认知努力，但互动不费力的思维过程。

It reflects Clara's decisions, Clara's judgments, Clara's unique personal, professional expertise. She sees another provocation, this time in the outline. In this case, she decides that while the provocation is useful.

它反映了克拉拉的决定，克拉拉的判断，克拉拉独特的个人专业知识。她看到了另一个挑衅，这次是在大纲中。在这种情况下，她决定，虽然挑衅是有用的。

She does not need to address it. Unlike typical AI suggestions, provocations are not meant to be applicable all the time. They're instead meant to stimulate your thinking about your work.

她不需要解决这个问题。与典型的人工智能建议不同，挑衅并不意味着总是适用的。相反，它们旨在激发你对工作的思考。

Because if you understand your work well enough, deeply enough to make the confident decision not to accept a piece of feedback, then the feedback process is still working as intended.

因为如果你对工作有足够深入的了解，能够做出不接受反馈的自信决定，那么反馈过程仍然在按预期进行。

But we're not done yet. Clara has entirely new ways of interacting with this text because of generative AI.

但我们还没有结束。由于生成式人工智能，Clara有了与这篇文章互动的全新方式。

A really simple example is that she can just resize a paragraph to change its length.

一个非常简单的例子是，她可以调整段落的大小以改变其长度。

She can also rapidly test different versions of this text and at select strategic points, indeed, she writes. As she writes, she sees provocations that rather than auto completing her ideas, they raise alternatives, they identify fallacies, they offer counter argument to help her strengthen and develop her own argument.

她还可以快速测试本文的不同版本，在选定的战略要点上，她确实在写。当她写的时候，她看到了挑衅，而不是自动完成她的想法，他们提出了替代方案，他们发现了谬误，他们提供了反驳，以帮助她加强和发展自己的论点。

There's something you won't find anywhere in this interface. That’s a chat box. Clara's not having to chat with anything to do her work yet.

在这个界面中，你找不到任何地方的东西，一个聊天框。克拉拉还不需要和任何人聊天来完成她的工作。

She is silently and appropriately assisted by her computer as a computer and not as us at human. Throughout this process, Clara has been assisted, and yes, probably worked faster because of AI.

她默默地、适当地得到了她的电脑以电脑的方式的帮助而不是得到我们人类的帮助。在整个过程中，Clara得到了帮助，是的，由于人工智能，她可能工作得更快。

But she's also maintained direct material engagement at strategic points. She read the relevant portions of the document herself. She constructed her decisions and her argument herself. And ultimately it can be said she has written this document herself.

但她也在战略要点上保持了直接的物质接触。她亲自阅读了文件的相关部分。她自己制定了自己的决定和论点，最终可以说她自己写了这份文件。

Moreover, she worked better because of AI. AI provocations at every stage of the process kept her meta cognitively engaged, always looking for critiques, alternatives and lateral moves.

此外，由于人工智能，她工作得更好。在这个过程的每个阶段，人工智能的挑衅都让她保持元认知的参与，总是寻找批评、替代方案和横向移动。

We have been studying the effects of tools like this and the results are promising.

我们一直在研究这样的工具的效果，而且结果很有希望。

You can demonstrably reintroduce critical thinking into AI assisted workflows. You can reverse the loss of creativity and enhance it instead.

你可以明显地将批判性思维重新引入人工智能辅助的工作流程中。你可以逆转创造力的丧失，并增强它。

You can build powerful tools for memory that enable knowledge workers to read and write at speed with greater intentionality and remember it too.

你可以构建强大的记忆工具，使知识工作者能够以更大的意图快速读写，并记住它。

It turns out, with the right principles of design, you can build tools that are the best of both worlds, applying the awesome speed and flexibility of this technology to protect and enhance human thought. 事实证明，通过正确的设计原则，你可以构建两全其美的工具，应用这项技术的惊人速度和灵活性来保护和增强人类思维。

These are simple, general principles, like ensuring that the tool preserves material engagement, offers productive resistance and scaffolds meta cognition.

这些是简单的一般原则，比如确保工具保持材料接触，提供生产性阻力和支架元认知。

And while we've been primarily studying professional knowledge workers, we believe that these principles can extend to all aspects of AI use, including when we use it in our daily lives, our hobbies, and even in education.

虽然我们主要研究专业知识工作者，但我们相信这些原则可以扩展到人工智能使用的各个方面，包括我们在日常生活、爱好甚至教育中使用它的时候。

I repeat, efficiency is not the aim of tools for thought. Better thinking is. But sometimes you can have both. I used to think there was no such thing as a free lunch in human thinking. This is so much better than a free lunch. This is a lunch that pays you to eat it.

我再说一遍，效率不是思考工具的目的。更好的思考才是。但有时你可以两者兼得。我曾经认为在人类思维中没有免费的午餐。这比免费午餐好多了。这是一顿花钱请你吃的午餐。

I want to close with some thoughts on the values that we have in developing AI software. What if AI gets to the point?

最后，我想谈谈我们在开发人工智能软**件方面的价值观。如果AI能够切中要害呢？

Where it can do a better job of thinking than humans, why should we care so much about protecting and augmenting human thought?

在哪里它可以比人类更好地思考，为什么我们要如此关心保护和增强人类的思维？

There's two reasons. First, there may always be ways of thinking that remain unique human strengths of which we may not even be aware. Second, perhaps more importantly, we take the position that the ability to think well is essential for human agency and empowerment and flourishing.

有两个原因。首先，可能总有一些思维方式仍然是我们甚至可能没有意识到的独特的人类优势。其次，也许更重要的是，我们的立场是，良好思考的能力对于人类能动性、赋权和繁荣至关重要。

This echoes an ancient question.

这与一个古老的问题相呼应。

People once asked if writing if books of the Internet can remember for us, does it matter that we cannot? People once asked if maps can navigate for us, does it matter that we cannot? Now we ask if machines can think for us, does it matter that we cannot?

人们曾经问过，如果互联网上的书能为我们记住写作，我们不能记住写作有什么关系吗？人们曾经问过，如果地图能为我们导航，我们不能导航有关系吗？现在我们问机器是否能为我们思考，我们不能思考有关系吗？

If machines can speak for us, grieve for us, pray for us, love for us, does it matter that we cannot?

如果机器能为我们说话，为我们悲伤，为我们祈祷，爱我们，我们不能这样做有关系吗？

To me, the answer is pretty obvious.

对我来说，答案很明显。

When I began studying human AI interaction thirteen years ago, it was inconceivable to me that we would be asking these questions in my lifetime. But we are, and we must.

13年前，当我开始研究人类与人工智能的交互时，我无法想象我们会在有生之年问这些问题。但我们是，我们必须。

I leave you with this thought. What would you rather have? A tool that thinks for you or a tool that makes you think?

我把这个想法留给你。你更喜欢什么？一个为你思考的工具，还是一个让你思考的工具？

That was Advait Sarkar at TED AI in Vienna, Austria in 2025.

那是 Advait Sarkar 在 2025 年于奥地利维也纳举行的 TED AI 大会上的发言。