本文摘要:LET me hazard a guess that you think a real person has written what you’re reading. Maybe you’re right. Maybe not. Perhaps you should ask me to confirm it the way your computer does when it demands that you type those letters and numbers c


LET me hazard a guess that you think a real person has written what you’re reading. Maybe you’re right. Maybe not. Perhaps you should ask me to confirm it the way your computer does when it demands that you type those letters and numbers crammed like abstract art into that annoying little box.让我来猜猜看,你指出你所读者的内容是由一个现实不存在的人写出的。你有可能是对的,也有可能是错的。也许你应当让我证实这种众说纷纭,就像你的电脑拒绝你将抽象化艺术般的字母和数字输出那个令人沮丧的小盒子一样。

Because, these days, a shocking amount of what we’re reading is created not by humans, but by computer algorithms. We probably should have suspected that the information assaulting us 24/7 couldn’t all have been created by people bent over their laptops.因为,目前有相当多的读者内容不是由人类撰写的,而是由计算机算法已完成的。我们有可能应当不会庞加莱,每天24小时向我们陷入绝境的信息有可能不几乎是由人类俯在笔记本电脑前撰写的。

It’s understandable. The multitude of digital avenues now available to us demand content with an appetite that human effort can no longer satisfy. This demand, paired with ever more sophisticated technology, is spawning an industry of “automated narrative generation.”这是可以解读的。人类的希望早已无法符合我们现在需要用于的各种数字渠道对内容的市场需求。

这种市场需求,再行再加更为成熟期的技术,杜绝了一个“文本自动分解”产业。Companies in this business aim to relieve humans from the burden of the writing process by using algorithms and natural language generators to create written content. Feed their platforms some data — financial earnings statistics, let’s say — and poof! In seconds, out comes a narrative that tells whatever story needs to be told.该领域中的公司目的利用算法和自然语言生成器撰写内容,使人类挣脱文学创作过程中的开销。将一些数据——比如金融收益数据——输出它们的平台,然后“嗖”的一声!几秒钟之内就不会产生一些内容,获取人们必须的各种报导。

These robo-writers don’t just regurgitate data, either; they create human-sounding stories in whatever voice — from staid to sassy — befits the intended audience. Or different audiences. They’re that smart. And when you read the output, you’d never guess the writer doesn’t have a heartbeat.这些机器人专栏作家并不只是反复数据;它们以合适目标受众的风格——从古板到开朗——写看上去看起来人类撰写的报导。它们十分聪慧。当你读者这些报导时,你绝不会猜中到这个作者没跳动。

Consider the opening sentences of these two sports pieces:想到这两篇体育报道的开篇语句。“Things looked bleak for the Angels when they trailed by two runs in the ninth inning, but Los Angeles recovered thanks to a key single from Vladimir Guerrero to pull out a 7-6 victory over the Boston Red Sox at Fenway Park on Sunday.”“周日,天使队(Angels)在第九局中领先两分时,情况看上去危急,但凭借弗拉迪米尔·葛雷诺(Vladimir Guerrero)夺得的关键一分,洛杉矶天使队挽救败局,在芬威球场(Fenway Park)以七比六的比数打败波士顿白袜队(Boston Red Sox)。”“The University of Michigan baseball team used a four-run fifth inning to salvage the final game in its three-game weekend series with Iowa, winning 7-5 on Saturday afternoon (April 24) at the Wilpon Baseball Complex, home of historic Ray Fisher Stadium.”“周六下午(4月24日),密歇根大学(University of Michigan)棒球队在威尔彭棒球场(Wilpon Baseball Complex)——具备历史意义的雷·费舍尔体育场(Ray Fisher Stadium)的所在地,通过夺得四分的第五局比赛,扭转局势,最后以七比五的比数夺得了与爱荷华棒球队在周末举办的三场比赛中的最后一场。

”If you can’t tell which was written by a human, you’re not alone. According to a study conducted by Christer Clerwall of Karlstad University in Sweden and published in Journalism Practice, when presented with sports stories not unlike these, study respondents couldn’t tell the difference. (Machine first, human second, in our example, by the way.)如果你无法辨别哪一篇是由人类写出的,那你不是唯一一个。瑞典卡尔斯塔得大学(Karlstad University)的克里斯兹·克莱瓦尔(Christer Clerwall)积极开展了一项研究,并在《新闻实践中》(Journalism Practice)上公开发表了涉及论文。

研究表明,当看见类似于的体育报道时,调查对象无法分辨其中的区别。(偷偷地说道一下,在我们获取的例子中,第一篇是机器写出的,第二篇是人写出的。)Algorithms and natural language generators have been around for a while, but they’re getting better and faster as the demand for them spurs investment and innovation. The sheer volume and complexity of the Big Data we generate, too much for mere mortals to tackle, calls for artificial rather than human intelligence to derive meaning from it all.算法和自然语言生成器早已不存在了一段时间,但随着对它们的市场需求性刺激了投资和创意,它们显得更加好,越来越快。

我们产生海量的大数据(Big Data),而且很简单,凡人无法处置,必须人工智能,而不是人类智能,来借此提供无意的信息。Set loose on the mother lode — especially stats-rich domains like finance, sports and merchandising — the new software platforms apply advanced metrics to identify patterns, trends and data anomalies. They then rapidly craft the explanatory narrative, stepping in as robo-journalists to replace humans.将之应用于大量资源,特别是在金融、体育和销售规划等数据多样的领域,这种新的软件平台就不会应用于先进设备的度量标准,去证实模式、趋势和异常数据。

然后,它们不会很快产生解释性文本,沦为替换人类的机器人记者。The Associated Press uses Automated Insights’ Wordsmith platform to create more than 3,000 financial reports per quarter. It published a story on Apple’s latest record-busting earnings within minutes of their release. Forbes uses Narrative Science’s Quill platform for similar efforts and refers to the firm as a partner.美联社(The Associated Press)每季度利用自动化洞察力公司(Automated Insights)的Wordsmith平台编写3000多篇金融报道。他们在苹果(Apple)公司发布近期创纪录收益几分钟之后,就公开发表了一篇报导。

福布斯(Forbes)利用描述科学公司(Narrative Science)的Quill平台编写类似于报导,相提并论该公司是他们的合作伙伴。Then we have Quakebot, the algorithm The Los Angeles Times uses to analyze geological data. It was the “author” of the first news report of the 4.7 magnitude earthquake that hit Southern California last year, published on the newspaper’s website just moments after the event. The newspaper also uses algorithms to enhance its homicide reporting.然后又经常出现了Quakebot,《洛杉矶时报》(The Los Angeles Times)利用这种算法分析地质数据。它是第一篇有关南加利福尼亚州去年再次发生的4.7级地震的新闻报道的“作者”。


地震再次发生后,该报立刻在其网站了公开发表了这篇报导。该报还利用算法强化命案报导。But we should be forgiven a sense of unease. These software processes, which are, after all, a black box to us, might skew to some predicated norm, or contain biases that we can’t possibly discern. Not to mention that we may be missing out on the insights a curious and fertile human mind could impart when considering the same information.如果我们回应深感一丝忧虑,这也是可以解读的。

这些软件程序却是对我们来说是一个黑盒子,它们有可能偏向于一些特定的基准,或包括我们有可能无法分辨的倾向性。更加不用说,我们可能会错失一个奇怪的、具备创造力的人类在思维完全相同的信息时所能产生的那种洞见。The mantra around all of this carries the usual liberation theme: Robo-journalism will free humans to do more reporting and less data processing.这一切所传达的呼声,包括着少见的和平主题——机器新闻将不会和平人类,使人类需要更好地展开报导,增加数据处理工作。

That would be nice, but Kristian Hammond, Narrative Science’s co-founder, estimates that 90 percent of news could be algorithmically generated by the mid-2020s, much of it without human intervention. If this projection is anywhere near accurate, we’re on a slippery slope.这称得上一件美事。但是,据描述科学牵头创始人克里斯蒂安·哈蒙德(Kristian Hammond)估算,到本世纪20年代中期,将有90%的新闻由计算机算法分解,其中大多都需要人工干预。

倘若这个预测相似事实,那么我们就不会处在一个滑坡之上。It’s mainly robo-journalism now, but it doesn’t stop there. As software stealthily replaces us as communicators, algorithmic content is rapidly permeating the nooks and crannies of our culture, from government affairs to fantasy football to reviews of your next pair of shoes.目前,机器新闻早已占有主导,但它未早已负于。随着软件悄悄代替我们沦为传播者,从政府事务到梦幻足球,再行到对你下一双鞋子的评价,算法分解的内容也在很快向我们文化中的各个角落和缝隙渗入。Automated Insights states that its software created one billion stories last year, many with no human intervention; its home page, as well as Narrative Science’s, displays logos of customers all of us would recognize: Samsung, Comcast, The A.P., Edmunds.com and Yahoo. What are the chances that you haven’t consumed such content without realizing it?自动化洞察力公司认为,其软件去年一共创作了10亿个报导,许多都没人工干预;它和描述科学公司的主页上,展出着我们耳熟能详的客户标志:三星(Samsung)、康卡斯特(Comcast)、美联社、Edmunds.com和雅虎(Yahoo)。

所以你近于有可能在没意识的情况下消费了这种内容。Books are robo-written, too. Consider the works of Philip M. Parker, a management science professor at the French business school Insead: His patented algorithmic system has generated more than a million books, more than 100,000 of which are available on Amazon. Give him a technical or arcane subject and his system will mine data and write a book or report, mimicking the thought process, he says, of a person who might write on the topic. Et voilà, “The Official Patient’s Sourcebook on Acne Rosacea.”机器人还在写书。来想到法国的欧洲工商管理学院(Insead)管理科学教授菲利普·M·帕克(Philip M. Parker)的作品:他的专利算法系统早已分解了多达100万本图书,其中有10万多本在亚马逊上销售。

他说道,给他一个技术性或难懂晦涩的话题,他的系统就能仿效有可能早已题目展开文学创作的人的思维过程,挖出数据,编写一本书或一篇报告。比如,《红斑痤疮患者官方资料》(The Official Patient’s Sourcebook on Acne Rosacea)。Narrative Science claims it can create “a narrative that is indistinguishable from a human-written one,” and Automated Insights says it specializes in writing “just like a human would,” but that’s precisely what gives me pause. The phrase is becoming a de facto parenthetical — not just for content creation, but where most technology is concerned.描述科学声称它可以创作“与出自于人类的作品分毫不差的文本”。

自动化洞察力则称之为它的专长是“像一个人一样”文学创作,但这正是让我忧虑的地方。这种众说纷纭事实上早已沦为一段插入语——不只是对内容创作,而且对于大多数科技都是如此。Our phones can speak to us (just as a human would). Our home appliances can take commands (just as a human would). Our cars will be able to drive themselves (just as a human would). What does “human” even mean?我们的手机可以(像一个人一样)和我们说出。

我们的家用电器需要(像一个人一样)拒绝接受指令。我们的汽车将能(像一个人一样)自行驾驶员。那么,“人”到底是什么意思?With technology, the next evolutionary step always seems logical. That’s the danger. As it seduces us again and again, we relinquish a little part of ourselves. We rarely step back to reflect on whether, ultimately, we’re giving up more than we’re getting.在科技的协助下,下一个革命性的进展或许总变得顺理成章。

这就是危险性所在。鉴于它重复诱使我们,我们就不会退出一小部分自己。我们很少不会前进一步,反省我们最后退出的东西否比获得的更好。Then again, who has time to think about that when there’s so much information to absorb every day? After all, we’re only human.再者,当每天都有这么多信息必须吸取的时候,谁还有时间去思维这那个问题?却是,我们只是人类。

Related: Interactive Quiz: Did a Human or a Computer Write This? A shocking amount of what we’re reading is created not by humans, but by computer algorithms. Can you tell the difference? Take the quiz.涉及内容:对话解说:这是人还是计算机写出的?现在我们写的内容中,由计算机算法而非人类撰写的比例非常之低。你能区分吗?来试试。



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