上期答案
While AI handles the mundane, humans can thrive in areas that require empathy, critical thinking, and innovation. This balance ensures that technology serves as a catalyst for human potential, rather than a competitor.
catalyst n.催化剂；诱因
虽然人工智能可以处理单调的事情，但人类可以在需要同理心、批判性思维和创新的领域茁壮成长。这种平衡确保了技术是人类潜能的催化剂，而不是竞争对手。
本期内容
早上好，读者朋友们，今天分享的文章节选自《麻省理工技术评论》。在科技飞速发展的今天，人工智能（AI）已不再仅仅是科幻小说中的概念，它正以惊人的速度融入我们的日常生活，成为推动社会进步的重要力量。而在这场AI浪潮中，一个引人注目的热点话题——“萝卜快跑”，正以其独特的魅力，引领我们窥见智慧出行的璀璨未来。它不仅仅是一个出行平台的名称，更是自动驾驶技术从实验室走向大众生活的生动实践。想象一下，一个能够学习、理解、推理、甚至创造的世界，它超越了传统计算的边界，正悄然重塑着我们的生活方式、工作模式乃至整个社会的运行逻辑。
What is AI?
什么是人工智能？
1
Artificial intelligence is the hottest technology of our time. But what is it? It sounds like a stupid question, but it’s one that’s never been more urgent. Here’s the short answer: AI is a catchall term for a set of technologies that make computers do things that are thought to require intelligence when done by people. Think of recognizing faces, understanding speech, driving cars, writing sentences, answering questions, creating pictures. But even that definition contains multitudes.
catchall n.笼统的一类
multitude n.大量，众多
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人工智能是我们这个时代最热门的技术。但它是什么呢?这听起来像是一个愚蠢的问题，但这是一个从未如此紧迫的问题。简短的回答是:人工智能是一个总称，指的是一组技术，这些技术能让计算机完成那些被认为需要人工智能才能完成的事情。想想识别面孔、理解语音、驾驶汽车、写句子、回答问题、创作图片。但即使是这样的定义也包含了很多内容。
And that right there is the problem. What does it mean for machines to understand speech or write a sentence? What kinds of tasks could we ask such machines to do? And how much should we trust the machines to do them?AI has acolytes, with a faith-like belief in the technology’s current power and inevitable future improvement. Artificial general intelligence is in sight, they say; superintelligence is coming behind it. And it has heretics, who pooh-pooh such claims as mystical mumbo-jumbo. In short, AI has come to mean all things to all people, splitting the field into fandoms. It can feel as if different camps are talking past one another, not always in good faith.
acolyte n.侍从；助手；赞礼
heretic n. 异教徒，异端者
mumbo-jumbo 繁文缛节
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这就是问题所在。对于机器来说，理解语音或书写句子意味着什么?我们可以让这样的机器完成什么样的任务?我们应该在多大程度上相信机器能做到这些?人工智能有信徒，他们对这项技术目前的力量和未来不可避免的改进抱有坚定的信念。他们说，通用人工智能指日可待;超级智能紧随其后。它也有异端者，他们把这种说法视为神秘的胡言乱语。简而言之，人工智能对所有人来说都意味着一切，将这个领域划分为各个领域。这让人感觉不同的阵营似乎在各执一词，并不总是真诚的。
These models are trained to complete sentences. They appear to be able to do a lot more—from solving high school math problems to writing computer code to passing law exams to composing poems. When a person does these things, we take it as a sign of intelligence. What about when a computer does it? Is the appearance of intelligence enough? We have built machines with humanlike behavior but haven’t shrugged off the habit of imagining a humanlike mind behind them. This leads to over-egged evaluations of what AI can do; it hardens gut reactions into dogmatic positions, and it plays into the wider culture wars between techno-optimists and techno-skeptics.
shrug off 摆脱，甩掉
over-egged 过分夸张的
gut adj.本能的
gut reaction 本能反应
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这些模型被训练来完成句子。他们似乎能做更多的事情——从解决高中数学问题到编写计算机代码，从通过法律考试到写诗。当一个人做这些事情时，我们认为这是智力的标志。如果是电脑来做呢?有智慧的外表就足够了吗?我们已经制造出具有类似人类行为的机器，但并没有摆脱想象它们背后有类似人类思维的习惯。这导致了对AI能力的过度评估;它将直觉反应固化为教条立场，并加剧了技术乐观主义者和技术怀疑论者之间更广泛的文化战争。
The other path favored neural networks, computer programs that would try to learn those rules by themselves in the form of statistical patterns. The Dartmouth proposal mentions it almost as an aside (referring variously to “neuron nets” and “nerve nets”). Though the idea seemed less promising at first, some researchers nevertheless continued to work on versions of neural networks alongside symbolic AI. But it would take decades—plus vast amounts of computing power and much of the data on the internet—before they really took off. Fast-forward to today and this approach underpins the entire AI boom.
statistical adj. 统计的，统计学的
underpin v. 支持，巩固
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另一条路则倾向于神经网络，即试图以统计模式的形式自行学习这些规则的计算机程序。达特茅斯的提案几乎只是顺便提到了这一点（分别提到了“神经元网络”和“神经网络”）。尽管这个想法最初看起来不太有希望，但一些研究人员仍然继续研究神经网络和符号人工智能的版本。但它们真正起飞还需要几十年的时间——加上大量的计算能力和互联网上的大量数据。快进到今天，这种方法支撑了整个人工智能的繁荣。
AI is many things. But I don’t think it’s humanlike. I don’t think it’s the solution to all (or even most) of our problems. It isn’t ChatGPT or Gemini or Copilot. It isn’t neural networks. It’s an idea, a vision, a kind of wish fulfillment. And ideas get shaped by other ideas, by morals, by quasi-religious convictions, by worldviews, by politics, and by gut instinct. “Artificial intelligence” is a helpful shorthand to describe a raft of different technologies. But AI is not one thing; it never has been, no matter how often the branding gets seared into the outside of the box.
moral n. 道德准则
shorthand n.简略的表达;速记法
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人工智能包含许多内容。但我不认为它像人类。我不认为它是我们所有（或甚至大多数）问题的解决方案。它不是ChatGPT、Gemini或Copilot。它不是神经网络。它是一个想法、一种愿景、一种愿望的实现。这些想法由其他想法、道德观念、准宗教信仰、世界观、政治观点和直觉塑造而成。“人工智能”是一个有用的缩写词，用来描述一系列不同的技术。无论这个标签被多么频繁地烙印在盒子的外面，但人工智能不是一成不变的，它从来都不是。
END
写作句式积累
Powerful computers won’t be enough to get us there: the AI field will also need “powerful ideas”—new theories of how thinking happens, new algorithms that might reproduce it.
强大的计算机并不足以让我们实现这一目标：人工智能领域还需要“强大的思想”——关于思考如何发生的新理论，以及可能重现思考的新算法。
长难句解析
Behind ChatGPT is a monster called GPT-4, a large language model /built from a vast neural network /that has ingested more words than most of us could read in a thousand lifetimes.
主句是：Behind ChatGPT is a monster called GPT-4，使用了倒装结构。a large language model 是a monster的同位语，built from a vast neural network 是过去分词短语作后置定语。that has ingested ... 是定语从句，修饰限定先行词a vast neural network。
翻译：ChatGPT背后是一个被称为GPT-4的怪物，它是一个由庞大的神经网络构建的大型语言模型，这个神经网络已经消化了比我们大多数人一辈子能读到的还要多的词汇。
翻译练习
AI has always been a sort of really vague concept that can expand endlessly to encompass all kinds of ideas.Suddenly, everybody has a concrete thing to refer to.What is AI? For millions of people the answer was now: ChatGPT.
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