Why A.I. Isn’t Going to Make Art
为什么人工智能不会创造艺术

In 1953, Roald Dahl published “The Great Automatic Grammatizator,” a short story about an electrical engineer who secretly desires to be a writer. One day, after completing construction of the world’s fastest calculating machine, the engineer realizes that “English grammar is governed by rules that are almost mathematical in their strictness.” He constructs a fiction-writing machine that can produce a five-thousand-word short story in thirty seconds; a novel takes fifteen minutes and requires the operator to manipulate handles and foot pedals, as if he were driving a car or playing an organ, to regulate the levels of humor and pathos. The resulting novels are so popular that, within a year, half the fiction published in English is a product of the engineer’s invention.
1953 年，罗尔德・达尔发表了 "The Great Automatic Grammatizator"，一个关于一位热衷于写作的电气工程师的短篇小说。有一天，在建造出世界最快的计算机之后，这位工程师意识到" 英语语法受制于几乎数学般严格的规则 "。他建造了一台小说写作机器，可以在 30 秒内生成一篇 5000 字的短篇小说；写一部小说需要 15 分钟，操作者需要操作手柄和脚踏，就像驾驶汽车或演奏管风琴一样来调节幽默和悲怆的水平。这些小说如此受欢迎，以至于在一年内，半数以上英语出版物都是出自这位工程师的发明。

Is there anything about art that makes us think it can’t be created by pushing a button, as in Dahl’s imagination? Right now, the fiction generated by large language models like ChatGPT is terrible, but one can imagine that such programs might improve in the future. How good could they get? Could they get better than humans at writing fiction—or making paintings or movies—in the same way that calculators are better at addition and subtraction?
艺术中是否有什么让我们认为它不能像达尔所想象的那样通过按下按钮就能创造出来？目前由大型语言模型如 ChatGPT 生成的虚构内容还很糟糕，但我们可以想象这些程序未来会有所改进。它们会提高到什么程度？它们会不会像计算器在加减方面比人类更擅长一样，在写作小说或创作绘画和电影方面也超过人类？

Art is notoriously hard to define, and so are the differences between good art and bad art. But let me offer a generalization: art is something that results from making a lot of choices. This might be easiest to explain if we use fiction writing as an example. When you are writing fiction, you are—consciously or unconsciously—making a choice about almost every word you type; to oversimplify, we can imagine that a ten-thousand-word short story requires something on the order of ten thousand choices. When you give a generative-A.I. program a prompt, you are making very few choices; if you supply a hundred-word prompt, you have made on the order of a hundred choices.
艺术很难定义，优秀艺术和糟糕艺术的区别也很难界定。但我想提出一个概括性的说法：艺术是通过大量选择而产生的结果。如果以小说写作为例，这种解释会更加简单明了。在写小说时，你会有意识或无意识地对几乎每个词语做出选择；为了简化，我们可以设想一篇一万字的短篇小说需要进行大约一万次选择。当你给一个生成式人工智能输入提示时，你只需做很少的选择；如果你提供了一百字的提示，你大约只做了一百次选择。

If an A.I. generates a ten-thousand-word story based on your prompt, it has to fill in for all of the choices that you are not making. There are various ways it can do this. One is to take an average of the choices that other writers have made, as represented by text found on the Internet; that average is equivalent to the least interesting choices possible, which is why A.I.-generated text is often really bland. Another is to instruct the program to engage in style mimicry, emulating the choices made by a specific writer, which produces a highly derivative story. In neither case is it creating interesting art.
如果一个人工智能根据您的提示生成了一篇一万字的故事，它就必须填补您没有做出的所有选择。它可以通过多种方式来实现。一种方法是取网上文本中其他作家所做选择的平均值，但这相当于采用最无趣的选择，这就是为什么人工智能生成的文本往往如此平淡无奇。另一种方法是指示程序进行风格模仿，模仿某位特定作家的选择，但这会产生一个高度衍生的故事。在这两种情况下，它都不是在创造有趣的艺术作品。

I think the same underlying principle applies to visual art, although it’s harder to quantify the choices that a painter might make. Real paintings bear the mark of an enormous number of decisions. By comparison, a person using a text-to-image program like DALL-E enters a prompt such as “A knight in a suit of armor fights a fire-breathing dragon,” and lets the program do the rest. (The newest version of DALL-E accepts prompts of up to four thousand characters—hundreds of words, but not enough to describe every detail of a scene.) Most of the choices in the resulting image have to be borrowed from similar paintings found online; the image might be exquisitely rendered, but the person entering the prompt can’t claim credit for that.
我认为同样的基本原理适用于视觉艺术，尽管很难量化画家可能做出的选择。真实的绘画体现了大量决策的标记。相比之下，一个使用文本到图像程序如 DALL-E 的人输入一个提示，如 "一个身穿盔甲的骑士与一条喷火的龙战斗", 然后让程序完成其余部分。（DALL-E 的最新版本可以接受长达四千个字符的提示 —— 数百个词，但还不足以描述场景的每一个细节。）生成图像中的大部分选择都必须从在线找到的类似绘画中借用；图像可能被描绘得非常精美，但输入提示的人无法为此获得信用。

Some commentators imagine that image generators will affect visual culture as much as the advent of photography once did. Although this might seem superficially plausible, the idea that photography is similar to generative A.I. deserves closer examination. When photography was first developed, I suspect it didn’t seem like an artistic medium because it wasn’t apparent that there were a lot of choices to be made; you just set up the camera and start the exposure. But over time people realized that there were a vast number of things you could do with cameras, and the artistry lies in the many choices that a photographer makes. It might not always be easy to articulate what the choices are, but when you compare an amateur’s photos to a professional’s, you can see the difference. So then the question becomes: Is there a similar opportunity to make a vast number of choices using a text-to-image generator? I think the answer is no. An artist—whether working digitally or with paint—implicitly makes far more decisions during the process of making a painting than would fit into a text prompt of a few hundred words.
一些评论者想象，图像生成器将像摄影术的兴起一样影响视觉文化。尽管这看似表面上合理，但摄影和生成式 AI 是相似的想法值得更近一步的考察。当摄影术首次被开发时，我怀疑它并不像是一种艺术媒介，因为并不明显有许多选择可做；你只需设置相机并开始曝光。但随着时间的推移，人们意识到相机可以做很多事情，艺术性在于摄影师做出的众多选择。并不总是容易解释选择是什么，但当你将业余照片与专业照片进行比较时，你就可以看到差异。那么问题就变成了：是否有类似的机会使用文本到图像生成器做出大量选择？我认为答案是否定的。无论是数字还是油画，艺术家在创作过程中做出的决定远远超过几百个字词提示所能包含的。

We can imagine a text-to-image generator that, over the course of many sessions, lets you enter tens of thousands of words into its text box to enable extremely fine-grained control over the image you’re producing; this would be something analogous to Photoshop with a purely textual interface. I’d say that a person could use such a program and still deserve to be called an artist. The film director Bennett Miller has used DALL-E 2 to generate some very striking images that have been exhibited at the Gagosian gallery; to create them, he crafted detailed text prompts and then instructed DALL-E to revise and manipulate the generated images again and again. He generated more than a hundred thousand images to arrive at the twenty images in the exhibit. But he has said that he hasn’t been able to obtain comparable results on later releases of DALL-E. I suspect this might be because Miller was using DALL-E for something it’s not intended to do; it’s as if he hacked Microsoft Paint to make it behave like Photoshop, but as soon as a new version of Paint was released, his hacks stopped working. OpenAI probably isn’t trying to build a product to serve users like Miller, because a product that requires a user to work for months to create an image isn’t appealing to a wide audience. The company wants to offer a product that generates images with little effort.
我们可以想象一个文本到图像生成器，在许多会话过程中，允许您输入成千上万的词语到文本框中，以实现对图像的极其细致的控制；这将类似于具有纯文本界面的 Photoshop。我认为一个人使用这样的程序仍然可以被称为艺术家。导演贝内特・米勒已经使用 DALL-E 2 生成了一些非常引人注目的图像，这些图像已在高古轩画廊展出；为了创造它们，他制作了详细的文本提示，然后指示 DALL-E 再次修改和操作生成的图像。他生成了超过十万张图像才得到展览中的二十张图像。但是他说他无法在后续版本的 DALL-E 上获得相同的结果。我怀疑这可能是因为米勒在使用 DALL-E 时把它用于一些不应该用于的事情；这就像他黑进了 Microsoft Paint 使其像 Photoshop 一样，但一旦新版本的 Paint 发布，他的黑客技术就失效了。OpenAI 可能并不打算开发一款为像米勒这样的用户服务的产品，因为需要用户花费数月时间来创造一张图像的产品对于广大用户来说并没有吸引力。该公司希望提供一种只需很少努力就能生成图像的产品。

It’s harder to imagine a program that, over many sessions, helps you write a good novel. This hypothetical writing program might require you to enter a hundred thousand words of prompts in order for it to generate an entirely different hundred thousand words that make up the novel you’re envisioning. It’s not clear to me what such a program would look like. Theoretically, if such a program existed, the user could perhaps deserve to be called the author. But, again, I don’t think companies like OpenAI want to create versions of ChatGPT that require just as much effort from users as writing a novel from scratch. The selling point of generative A.I. is that these programs generate vastly more than you put into them, and that is precisely what prevents them from being effective tools for artists.
很难想象一个程序能在多次会话中帮助您写出一部优秀的小说。这种假设的写作程序可能需要您输入十万字的提示，才能生成另外十万字组成您想象中的小说。我不清楚这样的程序究竟会是什么样子。从理论上讲，如果这样的程序真的存在，用户或许可以被称为作者。但是，我认为像 OpenAI 这样的公司并不想创造出需要用户付出与从头写一部小说一样多精力的版本的 ChatGPT。生成式 AI 的卖点在于，这些程序生成的内容远远超出用户投入的，这正是它们无法成为艺术家有效工具的原因。

The companies promoting generative-A.I. programs claim that they will unleash creativity. In essence, they are saying that art can be all inspiration and no perspiration—but these things cannot be easily separated. I’m not saying that art has to involve tedium. What I’m saying is that art requires making choices at every scale; the countless small-scale choices made during implementation are just as important to the final product as the few large-scale choices made during the conception. It is a mistake to equate “large-scale” with “important” when it comes to the choices made when creating art; the interrelationship between the large scale and the small scale is where the artistry lies.
推广生成式人工智能 (A.I.) 程序的公司声称，这些程序将释放创造力。实质上，他们在说艺术可以完全靠灵感而不需要汗水付出 —— 但这些是很难截然分开的。我并非要说艺术一定要涉及乏味。我的意思是，艺术需要在每个层面都做出选择；在实施过程中做出的无数小范围选择，与在构思阶段做出的几个大范围选择同样重要，最终产品的呈现离不开这两者。将 "大范围" 等同于 "重要" 是错误的，当谈到创作艺术时，大小范围选择之间的相互关系才是艺术所在。

Believing that inspiration outweighs everything else is, I suspect, a sign that someone is unfamiliar with the medium. I contend that this is true even if one’s goal is to create entertainment rather than high art. People often underestimate the effort required to entertain; a thriller novel may not live up to Kafka’s ideal of a book—an “axe for the frozen sea within us”—but it can still be as finely crafted as a Swiss watch. And an effective thriller is more than its premise or its plot. I doubt you could replace every sentence in a thriller with one that is semantically equivalent and have the resulting novel be as entertaining. This means that its sentences—and the small-scale choices they represent—help to determine the thriller’s effectiveness.
相信灵感胜过一切其他因素是一种迹象，表明某人不熟悉媒体。即便目标是创造娱乐而非高雅艺术，我也坚持这一观点是正确的。人们经常低估了娱乐所需的努力；一部惊悚小说可能无法达到卡夫卡所谓 "冰冻海洋中的斧头" 的理想，但它仍可能像瑞士手表一样精心打造。而一部富有影响力的惊悚小说不仅仅是其前提或情节。我怀疑，如果您用语义等价的句子替换惊悚小说中的每一个句子，所得到的小说也不会如此引人入胜。这意味着小说的句子及其所代表的细节选择有助于决定惊悚小说的成功性。

Many novelists have had the experience of being approached by someone convinced that they have a great idea for a novel, which they are willing to share in exchange for a fifty-fifty split of the proceeds. Such a person inadvertently reveals that they think formulating sentences is a nuisance rather than a fundamental part of storytelling in prose. Generative A.I. appeals to people who think they can express themselves in a medium without actually working in that medium. But the creators of traditional novels, paintings, and films are drawn to those art forms because they see the unique expressive potential that each medium affords. It is their eagerness to take full advantage of those potentialities that makes their work satisfying, whether as entertainment or as art.
很多小说家都有过这种经历，有人坚信自己有一个非常好的小说创意，愿意与他们共享，条件是二五五分分成。这样的人不经意地暴露了，他们认为构建句子只是故事情节的一种令人恼烦的附带部分，而不是其根本所在。生成式人工智能吸引那些认为可以在一个媒体中表达自己的人，但不需要在该媒体上实际工作。然而，传统小说、绘画和电影的创作者之所以被这些艺术形式所吸引，是因为他们看到了每种媒体所独有的表达潜能。正是对这些潜能的渴望和追求，使他们的作品不管是作为娱乐还是艺术，都令人满意。

Of course, most pieces of writing, whether articles or reports or e-mails, do not come with the expectation that they embody thousands of choices. In such cases, is there any harm in automating the task? Let me offer another generalization: any writing that deserves your attention as a reader is the result of effort expended by the person who wrote it. Effort during the writing process doesn’t guarantee the end product is worth reading, but worthwhile work cannot be made without it. The type of attention you pay when reading a personal e-mail is different from the type you pay when reading a business report, but in both cases it is only warranted when the writer put some thought into it.
当然，大多数写作作品，无论是文章、报告还是电子邮件，都不会期望它们体现成千上万个选择。在这种情况下，自动完成这项任务有什么危害吗？让我提供另一个概括性的观点：任何值得你作为读者关注的写作，都是作者付出努力的结果。写作过程中的努力并不能保证最终产品值得阅读，但没有这种努力，也不会产生有价值的作品。阅读个人电子邮件时的关注方式与阅读商业报告时的关注方式是不同的，但在这两种情况下，只有在作者付出一些思考时，阅读才是合理的。

Recently, Google aired a commercial during the Paris Olympics for Gemini, its competitor to OpenAI’s GPT-4. The ad shows a father using Gemini to compose a fan letter, which his daughter will send to an Olympic athlete who inspires her. Google pulled the commercial after widespread backlash from viewers; a media professor called it “one of the most disturbing commercials I’ve ever seen.” It’s notable that people reacted this way, even though artistic creativity wasn’t the attribute being supplanted. No one expects a child’s fan letter to an athlete to be extraordinary; if the young girl had written the letter herself, it would likely have been indistinguishable from countless others. The significance of a child’s fan letter—both to the child who writes it and to the athlete who receives it—comes from its being heartfelt rather than from its being eloquent.
最近，谷歌在巴黎奥运会期间播放了一则关于 Gemini (其与 OpenAI 的 GPT-4 竞争的产品) 的广告。该广告展示了一位父亲使用 Gemini 来撰写给一名启发了他女儿的奥运运动员的粉丝信，该粉丝信将由他的女儿发送。由于观众广泛的反对，谷歌撤下了这则广告。一位媒体教授称其为 "我见过最令人不安的广告之一"。值得注意的是，即使不是创造力被取代，人们也会以这种方式作出反应。没有人期望一个孩子写给运动员的粉丝信会非凡出色；如果这位年轻女孩亲自写下这封信，它很可能与无数其他同类作品无异。一个孩子的粉丝信的意义 —— 不论对于写信的孩子还是收信的运动员 —— 来自于其诚挚的情感，而非其出色的语言表达。

Many of us have sent store-bought greeting cards, knowing that it will be clear to the recipient that we didn’t compose the words ourselves. We don’t copy the words from a Hallmark card in our own handwriting, because that would feel dishonest. The programmer Simon Willison has described the training for large language models as “money laundering for copyrighted data,” which I find a useful way to think about the appeal of generative-A.I. programs: they let you engage in something like plagiarism, but there’s no guilt associated with it because it’s not clear even to you that you’re copying.
我们中的许多人都发送过商店购买的贺卡，知道收件人很明显我们没有自己撰写这些词语。我们不会将哈尔马克卡上的词语抄写在我们自己的笔迹上，因为那会感觉很不诚实。程序员西蒙・威利森描述了大型语言模型的训练过程为 "版权数据的洗钱", 这让我觉得这很好地概括了生成式人工智能程序的吸引力：它们让你从事某种形式的剽窃行为，但由于连你自己都不清楚自己在复制，所以也不会有负罪感。

Some have claimed that large language models are not laundering the texts they’re trained on but, rather, learning from them, in the same way that human writers learn from the books they’ve read. But a large language model is not a writer; it’s not even a user of language. Language is, by definition, a system of communication, and it requires an intention to communicate. Your phone’s auto-complete may offer good suggestions or bad ones, but in neither case is it trying to say anything to you or the person you’re texting. The fact that ChatGPT can generate coherent sentences invites us to imagine that it understands language in a way that your phone’s auto-complete does not, but it has no more intention to communicate.
有些人声称大型语言模型并没有洗涤它们所训练的文本，而是从中学习，就像人类作家从所读的书中学习一样。但大型语言模型并非作家，它甚至不是语言的使用者。语言按定义是一种交流系统，需要交流的意图。你手机上的自动完成功能可能会提供好的建议，也可能会提供坏的建议，但在任何一种情况下，它都不是在试图向你或你正在给某人发短信的人说什么。虽然 ChatGPT 可以生成连贯的句子，这使我们想象它以一种与手机自动完成功能不同的方式理解语言，但它并没有任何交流的意图。

It is very easy to get ChatGPT to emit a series of words such as “I am happy to see you.” There are many things we don’t understand about how large language models work, but one thing we can be sure of is that ChatGPT is not happy to see you. A dog can communicate that it is happy to see you, and so can a prelinguistic child, even though both lack the capability to use words. ChatGPT feels nothing and desires nothing, and this lack of intention is why ChatGPT is not actually using language. What makes the words “I’m happy to see you” a linguistic utterance is not that the sequence of text tokens that it is made up of are well formed; what makes it a linguistic utterance is the intention to communicate something.
很容易让 ChatGPT 发出一系列词语，如 "我很高兴见到你"。我们对于大型语言模型的工作原理还知之甚少，但有一点可以确定的是，ChatGPT 并不真的高兴见到你。狗狗和婴儿可以表达他们对你的高兴，即使他们还不会使用语言。ChatGPT 什么也感觉不到，也没有任何欲望，正是这种缺乏意图使得 ChatGPT 并不是在真正使用语言。让 "我很高兴见到你" 这句话成为语言表达的，不是因为它由合适的文本标记组成，而是因为它包含了传达信息的意图。

Because language comes so easily to us, it’s easy to forget that it lies on top of these other experiences of subjective feeling and of wanting to communicate that feeling. We’re tempted to project those experiences onto a large language model when it emits coherent sentences, but to do so is to fall prey to mimicry; it’s the same phenomenon as when butterflies evolve large dark spots on their wings that can fool birds into thinking they’re predators with big eyes. There is a context in which the dark spots are sufficient; birds are less likely to eat a butterfly that has them, and the butterfly doesn’t really care why it’s not being eaten, as long as it gets to live. But there is a big difference between a butterfly and a predator that poses a threat to a bird.
由于语言对我们来说是如此自然，很容易忘记它建立在主观感受和想要传达这种感受之上。当一个大型语言模型发出连贯的句子时，我们很容易将这些经历投射到它身上，但这种做法只是在犯模仿的错误；这与当蝴蝶在翅膀上进化出大的黑色斑点，使鸟类误以为它们是有大眼睛的掠食者一样。在某种情况下，这些黑斑就足够了；鸟类不太可能吃掉有这种黑斑的蝴蝶，而蝴蝶也不太在乎为什么它不被吃掉，只要它能活下来。但是，蝴蝶和对鸟类构成威胁的掠食者之间还是有很大的区别。

A person using generative A.I. to help them write might claim that they are drawing inspiration from the texts the model was trained on, but I would again argue that this differs from what we usually mean when we say one writer draws inspiration from another. Consider a college student who turns in a paper that consists solely of a five-page quotation from a book, stating that this quotation conveys exactly what she wanted to say, better than she could say it herself. Even if the student is completely candid with the instructor about what she’s done, it’s not accurate to say that she is drawing inspiration from the book she’s citing. The fact that a large language model can reword the quotation enough that the source is unidentifiable doesn’t change the fundamental nature of what’s going on.
使用生成式 AI 来帮助编写的人可能会声称他们是在从该模型接受过训练的文本中汲取灵感，但我再次认为，这与我们通常所说的一位作家从另一位作家那里汲取灵感有所不同。考虑一名大学生提交了一篇由一本书中的五页长引文组成的论文，并声称这一引文完美地传达了她想要表达的内容，胜过她自己的表述。即使学生对自己的做法对教师进行了完全坦诚的交代，也不准确地说她是在从她引用的那本书中汲取灵感。一个大型语言模型可以对引文进行重新措辞，使得原文出处无法被识别，但这并不改变正在发生的行为的本质性质。

As the linguist Emily M. Bender has noted, teachers don’t ask students to write essays because the world needs more student essays. The point of writing essays is to strengthen students’ critical-thinking skills; in the same way that lifting weights is useful no matter what sport an athlete plays, writing essays develops skills necessary for whatever job a college student will eventually get. Using ChatGPT to complete assignments is like bringing a forklift into the weight room; you will never improve your cognitive fitness that way.
正如语言学家 Emily M. Bender 指出的那样，老师们让学生写作文不是因为世界需要更多的学生作文。写作文的目的是加强学生的批判性思维技能；就像举重对于任何运动员都有益一样，写作文能培养大学生未来从事任何工作所需的技能。使用 ChatGPT 完成作业就像在健身房里使用叉车一样；这样永远无法提高您的认知能力。

Not all writing needs to be creative, or heartfelt, or even particularly good; sometimes it simply needs to exist. Such writing might support other goals, such as attracting views for advertising or satisfying bureaucratic requirements. When people are required to produce such text, we can hardly blame them for using whatever tools are available to accelerate the process. But is the world better off with more documents that have had minimal effort expended on them? It would be unrealistic to claim that if we refuse to use large language models, then the requirements to create low-quality text will disappear. However, I think it is inevitable that the more we use large language models to fulfill those requirements, the greater those requirements will eventually become. We are entering an era where someone might use a large language model to generate a document out of a bulleted list, and send it to a person who will use a large language model to condense that document into a bulleted list. Can anyone seriously argue that this is an improvement?
并非所有写作都需要创意性、真挚性或高质量，有时只需要简单存在。这种写作可能为其他目标提供支持，例如吸引广告浏览量或满足官僚要求。当人们被要求生产这种文本时，我们很难责备他们利用现有工具加快这个过程。但是，世界是否因拥有更多付出了最少努力的文档而变得更好呢？如果我们拒绝使用大型语言模型，低质量文本的要求就会消失，这种说法是不切实际的。然而，我认为随着我们越来越多地使用大型语言模型来满足这些要求，这些要求最终将越来越多。我们正进入一个时代，一个人可能会使用大型语言模型从要点列表生成一份文件，然后将其发送给另一个人，由后者使用大型语言模型将该文件压缩回要点列表。有人真的能够认真地说这是一种进步吗？

It’s not impossible that one day we will have computer programs that can do anything a human being can do, but, contrary to the claims of the companies promoting A.I., that is not something we’ll see in the next few years. Even in domains that have absolutely nothing to do with creativity, current A.I. programs have profound limitations that give us legitimate reasons to question whether they deserve to be called intelligent at all.
并非不可能有某一天我们会拥有可以完成人类所有行为的计算机程序，但与那些推广人工智能的公司声称的情况不同，这种情况在未来几年内是不会出现的。即使在与创造力毫无关系的领域，当前的人工智能程序也存在重大局限性，这让我们有正当理由质疑它们是否真的有资格被称为智能。

The computer scientist François Chollet has proposed the following distinction: skill is how well you perform at a task, while intelligence is how efficiently you gain new skills. I think this reflects our intuitions about human beings pretty well. Most people can learn a new skill given sufficient practice, but the faster the person picks up the skill, the more intelligent we think the person is. What’s interesting about this definition is that—unlike I.Q. tests—it’s also applicable to nonhuman entities; when a dog learns a new trick quickly, we consider that a sign of intelligence.
计算机科学家弗朗索瓦・肖利提出了以下区分：技能是你在任务中的表现有多好，而智力是你获得新技能的效率有多高。我认为这很好地反映了我们对人类的直观理解。大多数人只要足够练习就能学会新技能，但学习速度越快，我们就越认为这个人更聪明。这个定义的有趣之处在于，与智力测试不同，它也适用于非人类实体；当一只狗很快学会新把戏时，我们就认为这是一种智力的体现。

In 2019, researchers conducted an experiment in which they taught rats how to drive. They put the rats in little plastic containers with three copper-wire bars; when the mice put their paws on one of these bars, the container would either go forward, or turn left or turn right. The rats could see a plate of food on the other side of the room and tried to get their vehicles to go toward it. The researchers trained the rats for five minutes at a time, and after twenty-four practice sessions, the rats had become proficient at driving. Twenty-four trials were enough to master a task that no rat had likely ever encountered before in the evolutionary history of the species. I think that’s a good demonstration of intelligence.
2019 年，研究人员进行了一项实验，他们教会了老鼠如何驾驶。他们把老鼠放在小塑料容器里，容器有三个铜丝杆；当老鼠用爪子触碰其中一个杆时，容器就会向前、左转或右转。老鼠能看到房间另一边有一块食物盘，并试图让自己的车辆朝那里移动。研究人员每次训练老鼠 5 分钟，经过 24 个练习场次后，老鼠已经能熟练地驾驶了。仅 24 次练习就足以掌握这种在该物种进化史上可能从未出现过的任务。我认为这是对智力的良好证明。

Now consider the current A.I. programs that are widely acclaimed for their performance. AlphaZero, a program developed by Google’s DeepMind, plays chess better than any human player, but during its training it played forty-four million games, far more than any human can play in a lifetime. For it to master a new game, it will have to undergo a similarly enormous amount of training. By Chollet’s definition, programs like AlphaZero are highly skilled, but they aren’t particularly intelligent, because they aren’t efficient at gaining new skills. It is currently impossible to write a computer program capable of learning even a simple task in only twenty-four trials, if the programmer is not given information about the task beforehand.
现在考虑一下那些被广泛称赞其表现的当前人工智能程序。由谷歌的 DeepMind 开发的 AlphaZero 程序比任何人类棋手都更擅长下棋，但在训练过程中它下了四千四百万盘棋，远远超出了人类一生能下的盘数。要让它掌握一种新的游戏，它就需要经历同样庞大的训练量。根据 Chollet 的定义，像 AlphaZero 这样的程序虽然技能出众，但并不真正智能，因为它们在获取新技能方面效率并不高。要编写一个计算机程序，使其能在仅 24 次尝试中就学会一个简单的任务，如果程序员事先没有获得该任务的信息，这在目前是不可能实现的。

Self-driving cars trained on millions of miles of driving can still crash into an overturned trailer truck, because such things are not commonly found in their training data, whereas humans taking their first driving class will know to stop. More than our ability to solve algebraic equations, our ability to cope with unfamiliar situations is a fundamental part of why we consider humans intelligent. Computers will not be able to replace humans until they acquire that type of competence, and that is still a long way off; for the time being, we’re just looking for jobs that can be done with turbocharged auto-complete.
自动驾驶汽车经过数百万英里的驾驶训练，仍会撞上翻倒的拖车，因为这种情况并不常见于其训练数据中，而初学驾驶的人却能认识到应该停下来。我们解决代数方程的能力之外，应对陌生情况的能力才是人类智慧的根本体现。计算机在具备这种胜任能力之前，还无法完全取代人类；目前我们只是在寻找可以利用强大的自动完成功能来完成的工作。

Despite years of hype, the ability of generative A.I. to dramatically increase economic productivity remains theoretical. (Earlier this year, Goldman Sachs released a report titled “Gen AI: Too Much Spend, Too Little Benefit?”) The task that generative A.I. has been most successful at is lowering our expectations, both of the things we read and of ourselves when we write anything for others to read. It is a fundamentally dehumanizing technology because it treats us as less than what we are: creators and apprehenders of meaning. It reduces the amount of intention in the world.
尽管多年来有大肆宣传，生成式人工智能极大提高经济生产力的能力仍只是理论上的。(今年年初，高盛发布了一份题为 "Gen AI: 投入过高，利益过小？" 的报告。) 生成式人工智能最成功的任务是降低我们对所读内容和自己所写内容的期望。这是一种根本上贬低人性的技术，因为它把我们视为低于我们所拥有的创造力和理解力。它减少了世界上的本意。

Some individuals have defended large language models by saying that most of what human beings say or write isn’t particularly original. That is true, but it’s also irrelevant. When someone says “I’m sorry” to you, it doesn’t matter that other people have said sorry in the past; it doesn’t matter that “I’m sorry” is a string of text that is statistically unremarkable. If someone is being sincere, their apology is valuable and meaningful, even though apologies have previously been uttered. Likewise, when you tell someone that you’re happy to see them, you are saying something meaningful, even if it lacks novelty.
有些人辩护大型语言模型，说人类所说或写的大多并非特别原创。这是事实，但也是无关紧要的。当有人对你说 "我很抱歉" 时，过去其他人也说过对不起并不重要；这句话在统计上也可能并不突出。如果有人表达的是真诚的，他们的道歉就是有价值和意义的，尽管以前也有人说过。同样，当你告诉别人你很高兴见到他们时，你所说的也是有意义的，即使缺乏新颖性。

Something similar holds true for art. Whether you are creating a novel or a painting or a film, you are engaged in an act of communication between you and your audience. What you create doesn’t have to be utterly unlike every prior piece of art in human history to be valuable; the fact that you’re the one who is saying it, the fact that it derives from your unique life experience and arrives at a particular moment in the life of whoever is seeing your work, is what makes it new. We are all products of what has come before us, but it’s by living our lives in interaction with others that we bring meaning into the world. That is something that an auto-complete algorithm can never do, and don’t let anyone tell you otherwise. ♦
对于艺术来说，情况也大致如此。无论你创作小说、绘画还是电影，你都在与观众之间进行交流。你创作的作品并不一定要与人类历史上所有前作截然不同才有价值；重要的是你自己在说些什么，这源自你独特的人生经历，在观者欣赏你的作品时产生共鸣。我们都是前人的产物，但正是通过与他人互动而活着，我们才赋予世界以意义。这是自动补全程序永远无法做到的，任何人都不应让你怀疑这一点。