人工智能最終將如何偷走白領(lǐng)的工作

人工智能最終將如何偷走白領(lǐng)的工作

New Yorkers might imagine they are safe from automation. Your city, after all, is a white-collar city: a place packed with lawyers and accountants, architects and consultants, bankers and marketeers, doctors and teachers. Your work, the argument goes, is far too “complex” to be done by any machine — even the most capable. This view, though, is very likely to be wrong.

紐約人可能會(huì)認(rèn)為，他們不會(huì)受到自動(dòng)化的影響。畢竟，他們的城市是一個(gè)白領(lǐng)城市:一個(gè)律師和會(huì)計(jì)師、建筑師和顧問、銀行家和市場(chǎng)營銷人員、醫(yī)生和教師云集的地方。他們認(rèn)為，自己的工作足夠“復(fù)雜”，以至于任何機(jī)器都無法完成——即使是最有能力的機(jī)器。然而，這種觀點(diǎn)很可能是錯(cuò)誤的。

It is true that, in the 20th century, the most dramatic effects of technological change were confined to blue-collar work, the world of farmers and factory workers. The British agricultural industry, for instance, produces five times as much today as it did in 1861, but requires only around a tenth of the number of workers; its manufacturing sector produces about 150 percent more than in 1948, yet requires 60 percent fewer people to do it.

的確，在20世紀(jì)，技術(shù)變革最顯著的影響僅限于藍(lán)領(lǐng)工人、農(nóng)民和工廠工人。例如，英國農(nóng)業(yè)今天的產(chǎn)量是1861年的五倍，但只需要十分之一的工人;美國制造業(yè)的產(chǎn)量比1948年增加了150%，但是需要的勞動(dòng)力卻減少了60%。

But in the 21st century, as this progress relentlessly continues, these effects will seep out from blue-collar corners of economic life and flood the white-collar world as well.

但在21世紀(jì)，隨著這一進(jìn)步不懈地繼續(xù)下去，這些影響將從經(jīng)濟(jì)生活的藍(lán)領(lǐng)角落滲透出來，并淹沒白領(lǐng)世界。

It is increasingly clear that a lot of what white-collar New Yorkers do is not that “complex” or difficult after all. In fact, when you break down any job into all the “tasks” that make it up, it is obvious that people do a lot of different things in their work — and many of these are actually relatively simple. This point was driven home in a 2017 study by McKinsey & Company, which reviewed 820 occupations in the United States. While fewer than 5 percent of these could be completely automated with existing technologies, more than 60 percent were made up of tasks of which at least 30 percent could be automated, the study found. In short, most jobs — including white-collar ones — already involve a sizeable chunk of activity that can be automated.

越來越明顯的是，紐約白領(lǐng)做的很多事情根本不是那么“復(fù)雜”或困難。事實(shí)上，當(dāng)你把任何一項(xiàng)工作分解成所有組成它的“任務(wù)”時(shí)，很明顯，人們?cè)谒麄兊墓ぷ髦凶隽撕芏嗖煌氖虑?mdash;—其中許多實(shí)際上是相對(duì)簡(jiǎn)單的。麥肯錫公司(McKinsey & Company) 2017年的一項(xiàng)研究充分說明了這一點(diǎn)。該研究考察了美國的820個(gè)職業(yè)。研究發(fā)現(xiàn)，雖然只有不到5%的任務(wù)可以通過現(xiàn)有技術(shù)實(shí)現(xiàn)完全自動(dòng)化，但超過60%的任務(wù)可以由至少30%的任務(wù)實(shí)現(xiàn)自動(dòng)化。簡(jiǎn)而言之，大多數(shù)工作——包括白領(lǐng)工作——已經(jīng)包含了大量可以自動(dòng)化的活動(dòng)。

Traditionally, many experts have believed that machines had to copy the way human beings think and reason in order to outperform them. Getting a computer to diagnose an illness, for example, meant asking doctors to explain their thought processes and trying to get a machine to copy those same lines of reasoning.

傳統(tǒng)上，許多專家認(rèn)為，為了超越人類，機(jī)器必須模仿人類的思維和推理方式。例如，讓一臺(tái)電腦診斷一種疾病，意味著要求醫(yī)生解釋他們的思維過程，并試圖讓一臺(tái)機(jī)器復(fù)制這些相同的推理過程。

This was why “complex” tasks once seemed so hard to automate: Doctors would likely say they based a diagnosis on instinct or judgment or intuition, things that are impossible to capture in a set of rules for a computer to follow. And if human beings struggle to explain exactly how they perform a task, it’s difficult to tell a machine how to do it.

這就是為什么“復(fù)雜”的任務(wù)一度看起來很難自動(dòng)化的原因:醫(yī)生們可能會(huì)說，他們的診斷是基于直覺、判斷或直覺，而這些東西是不可能用一套規(guī)則讓電腦遵循的。如果人類很難準(zhǔn)確地解釋他們是如何完成一項(xiàng)任務(wù)的，那么就很難告訴機(jī)器如何完成這項(xiàng)任務(wù)。