人工智能：几分炒作几分现实？

就像蜂蜜会引来蜜蜂一样，技术趋势也会引来炒作。在互联网的黄金时期，单纯把“dotcom”一词与某家公司的名字挂钩就能带来股价的上涨。云计算、大数据、加密货币，这些词近年来轮番成为炒作圈的热点。每一股潮流都带来了真正前景广阔的技术发展、令人头晕脑胀的流行词、狂热的投资者以及提供启示的可靠顾问——当然，这是收费的。

作为当下最具典型性的技术潮流，人工智能领域已经涌现出了包罗万象的短语。然而，它被吹捧得过于无所不能，乃至于各企业都在冒险拔高对它的期望——也浪费了不少资金来尝试将这项技术用于它无法解决的问题。

想想泡沫出现的预警信号。风险投资家在赞助人工智能上已经超越了热心的范畴。研究公司PitchBook表示，这些人去年给1,028家人工智能相关的初创公司提供了资金，比起2013年的291家大幅提升。其中有26家公司的名字中包含“人工智能”，而五年前仅有一家。此外，还有大量会议打算向愚昧的管理者解释人工智能。在今年的瑞士达沃斯世界经济论坛（World Economic Forum）年度会议的议程中，有不少于11场涉及人工智能的专题讨论，题目都类似于“设计你的人工智能战略”、“为人工智能竞赛设立规则”。（《财富》杂志也赶了一次时髦，2018年在中国广州举办的《财富》全球科技论坛中也满是关于人工智能的讨论。）

结果就是，这个严肃的问题有了哗众取宠的风险。麻省理工学院（MIT）数字经济项目的研究员迈克尔·施拉格表示：“如果拥护者不谨慎一些，就会成功地让人工智能比特币化。”

别误解，人工智能远不只是一场短暂流行的潮流。它代表了一种全新的经营方式，是对自动化、基于感应器的工业监控和商业过程算法分析的现有趋势的强化。计算机科学已经帮助机器在完成日常工作上比人类更快。而结合了史上最强计算能力和多年来数字化数据积累的新型人工智能技术，意味着计算机可以第一次学习人类要求它们完成的任务，而不是单纯完成人类的指令。

Like bees to honey, tech trends generate hype. Merely appending the word “dotcom” to a company’s name drove up stock prices in the Internet’s salad days. Cloud computing, big data, and cryptocurrencies each have taken their turn in the hype cycle in recent years. Every trend brings genuinely promising technological developments, befuddling buzzwords, enthusiastic investors, and reassuring consultants offering enlightenment—for a fee, naturally.

Now the catchall phrase of artificial intelligence is shaping up as the defining technological trend of the moment. And yet, because the claims of what it will achieve are so grand, businesses risk raising their hopes for A.I. too high—and wasting money by trying to apply the technology to problems it can’t solve.

Consider the bubbly warning signs. Venture capitalists are beyond eager to fund A.I. They staked 1,028 A.I.-related startups last year, up from 291 in 2013, says researcher PitchBook. Twenty-six of those companies had “A.I.” in their names, compared with one five years earlier. Then there’s the profusion of conferences promising to explain A.I. to the benighted manager. At the annual meeting of the World Economic Forum in Davos, Switzerland, the agenda this year included no fewer than 11 panels that reference A.I., with names like “Designing Your A.I. Strategy” and “Setting Rules for the A.I. Race.” (Fortune has gotten into this act too: Its 2018 Global Tech Forum in Guangzhou, China, was dominated by A.I. discussions.)

The result is a serious subject running the risk of jumping the shark. “If advocates are not careful, they will have successfully Bitcoinized A.I.,” says Michael Schrage, a researcher at MIT’s Initiative on the Digital Economy.

Make no mistake—artificial intelligence is more than a fad. It represents a whole new way of doing business by turbocharging the existing trends of automation, sensor-based industrial monitoring, and algorithmic analysis of business processes. Computer science was already helping machines perform routine tasks more quickly than humans. The new techniques of A.I.—combined with ever faster computing power and the accumulation of years of digitized data—mean that for the first time computers learn the tasks humans require of them rather than merely doing as they’re told.

卡耐基梅隆大学（Carnegie Mellon University）的机器学习教授汤姆·米切尔表示，它引发的结果不亚于“影响未来十年社会和生活方式的主要推动力量之一”。对商业而言也是如此：研究公司IDC预测，未来三年内在人工智能上的投入将接近800亿美元。咨询公司埃森哲（Accenture）的首席技术和创新官保罗·多尔蒂推测这个数字还可能偏低，因为“它没有计算各公司围绕人工智能进行转型的投资”。

但是，就像其他任何激动人心的技术一样，人工智能可以实现的事情也是有限的。无人驾驶汽车就是完美的例子。我们已经有了在理想情况下让这些汽车行驶的技术，但即使是Alphabet无人驾驶汽车子公司Waymo的首席执行官约翰·克拉福西克也承认如果没有人类的操控，它们永远也不可能在所有的天气条件下工作。此外，计算机很擅长学习定义明确的任务，例如识别照片中的人物，或是准确将演讲转成文字。但理解人类动机或根据文字得出微妙的结论（通过人类所擅长的洞察力）仍然超出了机器的能力范围。卡耐基梅隆大学的米切尔表示：“我们在让这类功能产品化方面仍然处于相当早期的阶段。”

还有很多事无法通过人工智能实现，这应该会让很多首席执行官放下心来。斯坦福大学（Stanford University）的技术经济学教授苏珊·埃塞在她的高管教育课程上打消了经理们对于自身价值的疑虑，认清了他们招聘的人工智能科学家的局限性。埃塞表示：“他们新招入的博士，却在回答为什么起不到效果的方面没有经验，也不知道哪些项目不能做。”她说，人工智能无可非议地“带来了魔幻般的感觉”。但它最擅长在设计者准备好的情境里进行分析，而不是对从未见过的问题做出决定。埃塞表示：“让人工智能帮你管理一切，这是错误的认知。”

换句话说，人工智能不是解决所有问题的杀手锏。蒙特利尔的软件初创公司Element AI的首席执行官让-弗朗索瓦·加涅提醒客户称，只有当人工智能获取足够量的数据之后，解决方案才会变得出色。他表示：“让各个机构瞩目的机遇，其实是获取自适应系统的能力。这是一场旅程。那不是你可以花钱购买，然后启动开关就能使用的技术。按照人工智能的定义，它需要时间学习。”

加涅认为，建立一个有效人工智能的过程，好比“教导孩子做正确的事情，与他成年后拥有正确的行为”的差异。我们恐怕还需要同样长的时间，才能弄清企业是否准确把握了如今这个人工智能的契机。而还有另一种可能：人工智能只是又一个极度昂贵而又难以捉摸的金钱陷阱。（财富中文网）

本文的另一个版本登载于《财富》杂志2019年2月刊，标题为《请以批判的目光看待炒作人工智能的商人》。

译者：严匡正

The result, says Tom Mitchell, a machine-learning professor at Carnegie Mellon University, is nothing less than “one of the major forces for society and lifestyle of the next decade.” And commerce too: Researcher IDC predicts spending on A.I. will near $80 billion in three years. Paul Daugherty, chief technology and innovation officer of consultant Accenture, reckons that figure will prove low because “it doesn’t account for the investment companies are making in transformation around A.I.”

Yet, as is the case with any exciting technology, there are limits to what A.I. can accomplish. Self-driving cars are the perfect example. We already have the technology for them to operate under ideal circumstances, but even John Krafcik—CEO of Alphabet’s self-driving car subsidiary Waymo—admits they’ll never be able to drive in all weather conditions without some human input. What’s more, computers are very good at learning clearly defined tasks, like identifying people in photographs or accurately transcribing speech. But understanding human motivations or drawing nuanced conclusions from text—insights at which humans excel—remains beyond the machines. Says CMU’s Mitchell, “We’re still in the very early stages of trying to productize this.”

What A.I. can’t yet do ought to be of some comfort to CEOs. Susan Athey, a professor of the economics of technology at Stanford University, reassures managers in her executive education courses of their worth—and also the limitations of the A.I. scientists they hire. “New Ph.D.s are all bought in, but they don’t have the experience of what doesn’t work, which projects not to do,” she says. A.I., says Athey, justifiably “feels magical.” But it is best at analyzing situations its designers have prepared it to interpret, as opposed to making decisions on subjects it hasn’t seen before. “It’s just not right that your A.I. will manage for you,” says Athey.

A.I., in other words, is no silver bullet. Jean-François Gagné, CEO of the Montreal software startup Element AI, reminds clients that A.I. solutions are only as good as the accumulated data being fed into them. “The opportunity every organization is looking at is the ability to have adaptive systems,” he says. “It is a journey. It is not something you can buy and suddenly flip a switch. By the very definition of A.I., it takes time to learn.”

Gagné analogizes the process of building a useful A.I. to the difference between “teaching your children the right thing versus getting the right behavior in adulthood.” It will take at least as long to know if businesses were able to properly grasp this A.I. moment—or if it was another extremely expensive and elusive money pit.

A version of this article appears in the February 2019 issue of Fortune with the headline “Cast a Critical Eye Over the A.I. Hype Merchants.”