This is possible at the moment only on a very limited scale. Ask Apple’s Siri or Amazon’s Alexa for a single fact—say, what year “Top Gun” came out—and you will get the answer. But ask them to assemble the facts to prove a case, even at a straightforward level— “Do gun laws reduce gun crime?”—and they will flounder. An advance in integrating knowledge would then have to be married to another breakthrough: teaching text-generation systems to go beyond sentences to structures. Mr Seabrook found that the longer the text he solicited from GPT-2, the more obvious it was that the work it produced was gibberish. Each sentence was fine on its own; remarkably, three or four back to back could stay on topic, apparently cohering. But machines are aeons away from being able to recreate rhetorical and argumentative flow across paragraphs and pages. Not only can today’s journalists expect to finish their careers without competition from the Writernator— today’s parents can tell their children that they still need to learn to write, too. Aside from making scribblers redundant, a common worry is that such systems will be able to flood social media and online comment sections with semi-coherent but angry ramblings that are designed to divide and enrage. In reality, that may not be much of a departure from the tenor of such websites now, nor much of a disaster. Perhaps a flood of furious auto-babble will force future readers to distinguish between the illusion of coherence and the genuine article. If so, the Writernator, much like the Terminator, would even come to do the world some good.
目前，这仅在非常有限的范围内是可能的 。如果你问苹果的Siri或亚马逊的Alexa一个简单的事实——比如《壮志凌云》在哪一年上映——你就会得到答案 。 但要求电脑收集事实来证明一个案件，即使是在一个简单的层面上——“枪支法能减少枪支犯罪吗?”——电脑就会解释不清楚 。在整合知识方面的进步必须与另一个突破相结合：训练文本生成系统超越句子层面注重结构层面 。西布鲁克先生发现，他向GPT-2索取的文本越长，就越明显地看出，GPT-2所做的文章是胡言乱语 。每个句子单拿出来都很好；值得注意的是，三四个连续的句子还可以保持话题的连贯性 。但机器要想在段落和页面中重现修辞和论证流，还需要很长时间 。如今的记者不仅可以在没有作家竞争的情况下完成职业生涯，如今的父母也可以告诉孩子，仍然需要学习写作 。除了让乱写乱画的人变得多余外，人们普遍担心的是，这样的系统将能够以半连贯但愤怒的漫谈淹没社交媒体和在线评论区，达到分裂和激怒他人的效果 。