全球增長(zhǎng)停滯了，貿(mào)易爭(zhēng)端損害著從上海到斯圖加特到西雅圖的制造商們。盡管經(jīng)濟(jì)狀況似乎不妙，但一些最熱衷的支持者們認(rèn)為工業(yè)4.0仍然活躍，發(fā)展良好。

工業(yè)4.0是一個(gè)涵蓋廣泛的概念，它指的是運(yùn)用大數(shù)據(jù)、改進(jìn)的機(jī)器人和人工智能系統(tǒng)來(lái)運(yùn)營(yíng)業(yè)務(wù)。人們寄望于它，在未來(lái)十年乃至更長(zhǎng)時(shí)間里，成為全球增長(zhǎng)的主要驅(qū)動(dòng)力。是的，甚至包括制造業(yè)。

到2035年，人工智能賦能的行業(yè)，將為全球經(jīng)濟(jì)創(chuàng)造14萬(wàn)億美元的價(jià)值，這是咨詢業(yè)巨頭埃森哲的預(yù)測(cè)。

這一估測(cè)，具體來(lái)說(shuō)是來(lái)自于埃森哲實(shí)驗(yàn)室的全球高級(jí)執(zhí)行董事馬克·卡萊爾·比利爾德，他在周三參加阿姆斯特丹的人工智能世界峰會(huì)作主題演講時(shí)，輕松地說(shuō)出了這一數(shù)據(jù)。他還舉了實(shí)例，有研究追蹤了一項(xiàng)在人工智能賦能自動(dòng)化領(lǐng)域增長(zhǎng)迅速的業(yè)務(wù)：呼叫中心。五年前，人工智能機(jī)器人可以成功解決十分之一的客戶電話，而如今這個(gè)數(shù)字是60%。

另外他還預(yù)測(cè)說(shuō)，這一自動(dòng)化的推進(jìn)，不會(huì)像一些悲觀的經(jīng)濟(jì)學(xué)家擔(dān)心的那樣，消滅很多人類的工作機(jī)會(huì)。

但是，在科技人士慶祝一個(gè)階段的成功之前，有一個(gè)警告得聽(tīng)一聽(tīng)。

卡萊爾·比利爾德說(shuō)，人工智能不會(huì)造成對(duì)工作機(jī)會(huì)的威脅，“因?yàn)檫@些系統(tǒng)并不是很聰明?！比斯ぶ悄芗捌涓鞣N迭代：機(jī)器學(xué)習(xí)、自然語(yǔ)言處理、機(jī)器視覺(jué)、音像識(shí)別等很好地應(yīng)用在高度專業(yè)的任務(wù)中。在一些方面它干得不錯(cuò)，比如預(yù)測(cè)明天的天氣、訂購(gòu)電影票、晚高峰時(shí)幫你找出最快的回家線路等等。各種商業(yè)活動(dòng)越來(lái)越多地使用企業(yè)級(jí)人工智能，從搜集到的巨量的有結(jié)構(gòu)和無(wú)結(jié)構(gòu)的數(shù)據(jù)中尋找出意義，剔除低效并節(jié)省成本。

但卡萊爾·比利爾德也提到，人工智能還是有盲點(diǎn)。它被訓(xùn)練出解讀特定數(shù)據(jù)庫(kù)的能力，卻不能從復(fù)雜世界里引導(dǎo)出意義或者背景知識(shí)。人工智能是專家，卻不是雜家。因此，要讓這些系統(tǒng)真正智能化，還有許多工作要做。

紐約大學(xué)的心理與神經(jīng)科學(xué)教授、《重啟人工智能》（Rebooting AI）一書的作者加里·馬庫(kù)斯，作了一番更加坦白的評(píng)估。他稱深度學(xué)習(xí)——即人工智能的一個(gè)分支，僅需少量甚至無(wú)需人類的監(jiān)管就能處理巨量數(shù)據(jù)——是一個(gè)錯(cuò)誤的命名。它適用于一些狹義特定的項(xiàng)目，但就其大被吹噓的潛力，馬庫(kù)斯表示質(zhì)疑，比如它能改革交通系統(tǒng)（自動(dòng)駕駛汽車）和醫(yī)療系統(tǒng)（分析巨量的核磁共振圖像來(lái)找出癌癥擴(kuò)展的跡象）嗎？馬庫(kù)斯說(shuō)，“深度學(xué)習(xí)不代表深度理解?！?/p>

“有多少放射科醫(yī)生被深度學(xué)習(xí)系統(tǒng)替代了？”他自問(wèn)自答，“一個(gè)也沒(méi)有?！?/p>

卡萊爾·比利爾德認(rèn)為，要使得人工智能系統(tǒng)真正有效，需要把它們?cè)O(shè)計(jì)成負(fù)責(zé)任的、具有透明度的以及沒(méi)有偏見(jiàn)的——而不只是干活超級(jí)快的小兔子。只有那時(shí)，人工智能系統(tǒng)才能達(dá)成其所有的潛力。

在人工智能世界峰會(huì)的首日，許多先期的探討都是關(guān)于建立所謂具有倫理的人工智能系統(tǒng)的必要性。馬庫(kù)斯和卡萊爾·比利爾德，與其他人一樣，也敦促開發(fā)界要構(gòu)建出負(fù)責(zé)任的、透明的、不帶偏見(jiàn)的人工智能系統(tǒng)。

卡萊爾·比利爾德說(shuō)，除非它是負(fù)責(zé)任的，“否則沒(méi)有人會(huì)信任它，也沒(méi)有人會(huì)使用它。”（財(cái)富中文網(wǎng)）

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譯者：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.”