本文转自：英文外刊双语阅读
Don’t fear the Writernator
不要害怕机器写手
Computer-generated writing will never replace the human kind
计算机生成的文字永远不会取代人类
Many people will be familiar with automated writing through two features of Gmail. Smart Reply proffers brief answers to routine emails. If someone asks “Do you want to meet at 3pm?”, Gmail offers one-click responses such as “Sure!”
许多人将通过Gmail的两个特性来熟悉自动写作。Smart Reply 会给常规邮件提供简短的回复。如果有人问“你想下午三点见面吗?”，Gmail会提供一键式的回答，如“当然!”
More strikingly, Smart Compose kicks in as you write, suggesting endings to your sentences. Both are not only rendered in flawless English; they often eerily seem to have guessed what you want to say. If someone sends bad news, Smart Reply might offer “Ugh.”
更引人注目的是，当你写作时，Smart Compose会发挥作用，为句子提供结尾。这两个功能不仅用完美的英语呈现；还常常奇怪地似乎猜到了你想说什么。如果某人发来不好的消息，Smart Reply可能会说“呃”
The New Yorker’s John Seabrook recently described a more powerful version of this technology, called GPT-2, which can ably mimic his magazine’s style. Such systems use a digital network of billions of artificial “neurons” with virtual “synapses”—the connections between neurons—that strengthen as the network “learns”, in this case from 40 gigabytes-worth of online writing. The version Mr Seabrook tested was refined with back-issues of the New Yorker.
《纽约客》的（特约撰稿人）约翰•西布鲁克最近描述了这种技术的一个更强大的版本——GPT-2，可以巧妙地模仿撰稿风格。这些系统使用了一个由数十亿人造“神经元”和虚拟“突触”（神经元之间的联系）组成的数字网络，突触随网络的“学习”而增强，这可以在相当于40千兆的网络写作中找到例子。西布鲁克先生测试的版本在《纽约客》的后刊中进行了修改。
The metaphor of the brain is tempting, but “neurons” and “synapses” deserve those scare-quotes. The system is merely making some—admittedly very sophisticated—statistical guesses about which words follow which in a New Yorker-style sentence.
大脑这个比喻听起来很诱人，但是“神经元”和“突触”值得我们引用一下。该系统只是对该杂志风格的句子中哪个词跟哪个词做了一些统计上的猜测，诚然，这些猜测非常复杂。
At a simple level, imagine beginning an email with “Happy…” Having looked at millions of other emails, Gmail can plausibly guess that the next word will be “birthday”. GPT-2 makes predictions of the same sort.
简单地说，想象一下以“Happy…”开头的邮件。查看了数以百万计的其他电子邮件后，Gmail似乎可以合理地猜测下一个单词将是“birthday”。GPT-2做出了类似的预测。
What eludes computers is creativity. By virtue of having been trained on past compositions, they can only be derivative. Furthermore, they cannot conceive a topic or goal on their own, much less plan how to get there with logic and style.
计算机无法达到的是创造力。由于是在过去的文章上受训练，机器写手只能是衍生物。此外，他们不能自己构思一个主题或目标，更不用说如何用逻辑和风格去实现它。
At various points in the online version of his article, readers can see how GPT-2 would have carried on writing Mr Seabrook’s piece for him. The prose gives the impression of being human. But on closer inspection it is empty, even incoherent.
在这篇文章的网络版中，读者可以看到GPT-2是如何继续为西布鲁克先生撰写这篇文章的。文章刚开始给人一种人写的印象。但仔细一看，它是空的，甚至是不连贯的。
Meaningless prose is not only the preserve of artificial intelligence. There is already a large quantity of writing that seems to make sense, but ultimately doesn’t, at least to a majority of readers. In 1996 Alan Sokal famously submitted a bogus article to a humanities journal, with ideas that were complete nonsense but with language that expertly simulated fashionable post-modernist academic prose.
毫无意义的散文不仅是人工智能的专利。已经有大量表面看起来有意义的文章，但其实没有意义，至少对大多数读者来说是这样。1996年，著名的Alan Sokal向一份人文杂志提交了一篇伪造的文章，其观点完全是胡说八道，但其语言却熟练地模仿了时髦的后现代主义学术散文。
It was accepted. Three scholars repeated the ruse in 2017, getting four of 20 fake papers published. Humans already produce language that is devoid of meaning, intentionally and otherwise.
这篇文章发表了。2017年，三名学者重复了这一伎俩，发表了20篇假论文中的4篇。人类已经有意或无意地创造了没有意义的语言。
But to truly write, you must first have something to say. Computers do not. They await instructions. Given input, they provide output. Such systems can be seeded with a topic, or the first few paragraphs, and be told to “write”. While the result may be grammatical English, this should not be confused with the purposeful kind. To compose meaningful essays, the likes of GPT-2 will first have to be integrated with databases of real-world knowledge.
但要真正写作，你必须先有话要说。机器写手做不到这一点。只能等待指令。给定输入，电脑提供输出。这样的系统可以植入一个主题，或前几段，并收到“写”的指令。虽然结果可能是合乎语法的英语，但不应将其与有目的的英语混淆。要写出有意义的文章，像GPT-2这样的项目首先必须与真实世界的知识数据库相结合。
