跨过这些坎，人工智能将为全球经济贡献14万亿美元

全球增长停滞了，贸易争端损害着从上海到斯图加特到西雅图的制造商们。尽管经济状况似乎不妙，但一些最热衷的支持者们认为工业4.0仍然活跃，发展良好。

工业4.0是一个涵盖广泛的概念，它指的是运用大数据、改进的机器人和人工智能系统来运营业务。人们寄望于它，在未来十年乃至更长时间里，成为全球增长的主要驱动力。是的，甚至包括制造业。

到2035年，人工智能赋能的行业，将为全球经济创造14万亿美元的价值，这是咨询业巨头埃森哲的预测。

这一估测，具体来说是来自于埃森哲实验室的全球高级执行董事马克·卡莱尔·比利尔德，他在周三参加阿姆斯特丹的人工智能世界峰会作主题演讲时，轻松地说出了这一数据。他还举了实例，有研究追踪了一项在人工智能赋能自动化领域增长迅速的业务：呼叫中心。五年前，人工智能机器人可以成功解决十分之一的客户电话，而如今这个数字是60%。

另外他还预测说，这一自动化的推进，不会像一些悲观的经济学家担心的那样，消灭很多人类的工作机会。

但是，在科技人士庆祝一个阶段的成功之前，有一个警告得听一听。

卡莱尔·比利尔德说，人工智能不会造成对工作机会的威胁，“因为这些系统并不是很聪明。”人工智能及其各种迭代：机器学习、自然语言处理、机器视觉、音像识别等很好地应用在高度专业的任务中。在一些方面它干得不错，比如预测明天的天气、订购电影票、晚高峰时帮你找出最快的回家线路等等。各种商业活动越来越多地使用企业级人工智能，从搜集到的巨量的有结构和无结构的数据中寻找出意义，剔除低效并节省成本。

但卡莱尔·比利尔德也提到，人工智能还是有盲点。它被训练出解读特定数据库的能力，却不能从复杂世界里引导出意义或者背景知识。人工智能是专家，却不是杂家。因此，要让这些系统真正智能化，还有许多工作要做。

纽约大学的心理与神经科学教授、《重启人工智能》（Rebooting AI）一书的作者加里·马库斯，作了一番更加坦白的评估。他称深度学习——即人工智能的一个分支，仅需少量甚至无需人类的监管就能处理巨量数据——是一个错误的命名。它适用于一些狭义特定的项目，但就其大被吹嘘的潜力，马库斯表示质疑，比如它能改革交通系统（自动驾驶汽车）和医疗系统（分析巨量的核磁共振图像来找出癌症扩展的迹象）吗？马库斯说，“深度学习不代表深度理解。”

“有多少放射科医生被深度学习系统替代了？”他自问自答，“一个也没有。”

卡莱尔·比利尔德认为，要使得人工智能系统真正有效，需要把它们设计成负责任的、具有透明度的以及没有偏见的——而不只是干活超级快的小兔子。只有那时，人工智能系统才能达成其所有的潜力。

在人工智能世界峰会的首日，许多先期的探讨都是关于建立所谓具有伦理的人工智能系统的必要性。马库斯和卡莱尔·比利尔德，与其他人一样，也敦促开发界要构建出负责任的、透明的、不带偏见的人工智能系统。

卡莱尔·比利尔德说，除非它是负责任的，“否则没有人会信任它，也没有人会使用它。”（财富中文网）

译者：宣峰

Global growth is stalling. Trade wars are hammering manufacturers, from Shanghai to Stuttgart to Seattle. But, awful as today’s economic outlook appears, Industry 4.0 is alive and well, its most ardent backers say.

Industry 4.0 is the catch-all term for the implementation by businesses of big data, improved robotics and artificial intelligence systems. And it’s still expected to be a major driver in global growth over the next decade, and beyond. Yes, even in manufacturing.

By 2035, this A.I.-powered push will provide a $14 trillion boost to the global economy, consulting giant Accenture predicts.

That’s the assessment of Marc Carrel-Billiard, global senior managing director at Accenture Labs, who rattled off these numbers during his keynote presentation at World Summit A.I. in Amsterdam on Wednesday. By way of example, he cited research that traced the progress in one growing area of A.I.-powered automation: call centers. Five years ago, A.I. bots could successfully resolve one out every ten customer phone calls. Today, he said, it’s 60%.

Moreover, he predicted, this push to automate will not be the jobs-killer the more bearish economists out there fear.

But before technologists take a victory lap, there’s a caveat.

They’re not a threat to jobs, he says, “because these systems are not very intelligent.” AI—and its many iterations: machine learning, natural language processing, machine vision, image- and voice-recognition—is well adapted at highly specialized tasks. It does a decent job telling you what the weather will be tomorrow, or ordering movie tickets or helping you find the fastest route home during the evening commute. All manner of businesses are using A.I. increasingly on the enterprise level to make sense of the vast flows of structured and unstructured data they collect to root out inefficiencies, and save costs.

But, as Carrel-Billiard notes, A.I. still has a blind spot. It’s trained to interpret certain data sets, not infer meaning or context from a complicated world. A.I. is a specialist, not a generalist, he says. And therefore, much work is needed to make these systems truly intelligent.

Gary Marcus, professor of psychology and neural science at New York University and author of Rebooting AI, is even more frank in his assessment. He calls deep learning— the subset of A.I. that can make sense of huge amounts of data with little to no oversight from human minders—a misnomer. It’s good at narrowly focused tasks, but he questions its much-ballyhooed potential to, for example, revolutionize transportation (self-driving cars) and medicine (analyzing huge volumes of MRI scans for signs of cancerous growths). “Deep learning is no substitute for deep understanding,” he says.

“The number of radiologists who’ve been replaced by deep-learning systems?” he asks. “Zero.”

Carrel-Billiard, for one, believes that in order for A.I. systems to be truly effective they need to be designed to be accountable, transparent and free of bias—not just super-fast task rabbits. Only then will such systems reach their full potential.

On day one of the World Summit A.I., much of the early discussion was about the need to build so-called ethical A.I. systems. Marcus and Carrel-Billiard, among others, challenged the development community to build A.I. systems that are accountable, transparent and free of bias.

Unless it’s responsible, Carrel-Billiard says, “nobody will trust it, and nobody will use it.”