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外刊晨讀
2018 年 年 5 月 月 15 日
Artificial Intelligence in Business
商業(yè)中的人工智能
GrAIt Expectations
遠大前程
LIE DETECTORS ARE not widely used in business, but Ping An, a Chinese
insurance company, thinks it can spot dishonesty. The company lets customers apply
for loans through its app. Prospective borrowers answer questions about their income
and plans for repayment by video, which monitors around 50 tiny facial expressions
to determine whether they are telling the truth. The program, enabled by artificial
intelligence (AI), helps pinpoint customers who require further scrutiny.
The path ahead is exhilarating but perilous. Around 85% of companies think AI will
offer a competitive advantage, but only one in 20 is “extensively” employing it today,
according to a report by MIT’s Sloan Management Review and the Boston Consulting
Group. Large companies and industries, such as finance, that generate a lot of data,
tend to be ahead and often build their own AI-enhanced systems. But many firms will
choose to work with the growing array of independent AI vendors, including cloud
providers, consultants and startups.
This is not just a corporate race but an international one, too, especially between
America and China. Chinese firms have an early edge, not least because the
government keeps a vast database of faces that can help train facial-recognition
algorithms; and privacy is less of a concern than in the West.
There will be plenty of opportunities to stumble. One difficult issue for companies
will be timing. Roy Bahat of Bloomberg Beta, a venture-capital firm, draws a
parallel between now and the first dotcom boom of the late 1990s: “Companies are
flailing to figure out what to spend money on.” If they invest huge sums in AI early
on, they run the risk of overcommitting themselves or paying large amounts for
worthless startups, as many did in the early days of the internet. But if they wait too
long, they may leave themselves open to disruption from upstarts, as well as from
rivals that were quicker to harness technology.
Mar 31st, 2018
The Economist《經(jīng)濟學人》
譯文
測謊儀并未在企業(yè)中廣泛應用，但中國平安保險公司相信自己能探測謊言。
這家公司讓客戶通過它的一款應用程序來申請貸款。未來的貸款人在視頻中回答
有關(guān)收入和還款計劃的問題。視頻會監(jiān)測他們的大概 50 個細微面部表情，判斷
他們是否在說真話。這套人工智能（AI）驅(qū)動的程序幫助篩查出需要進一步審核
的客戶。
前路令人振奮卻也危險重重。根據(jù)麻省理工學院的《斯隆管理評論》和波士
頓咨詢集團聯(lián)合撰寫的報告，約 85％的企業(yè)認為 AI 將帶來競爭優(yōu)勢，但只有 5％
的公司正在“廣泛”地使用它。生成大量數(shù)據(jù)的大企業(yè)和金融等行業(yè)往往走在前
頭，常常建立自己的 AI 增強系統(tǒng)。但許多企業(yè)會選擇與隊伍不斷擴大的獨立 AI
供應商合作，包括云供應商、咨詢公司和創(chuàng)業(yè)公司等。
這不僅是一項企業(yè)競賽，也是一場國際競逐——尤其在中美之間。中國企業(yè)
有一個先發(fā)優(yōu)勢，這主要是因為中國政府擁有一個龐大的人臉數(shù)據(jù)庫，可以用來
訓練面部識別算法。而且，與西方相比，中國人對隱私也不那么關(guān)切。
跌跤的機會很多。企業(yè)面臨的難題之一是對時機的把握。風險投資公司
Bloomberg Beta 的羅伊?巴哈特（Roy Bahat）把眼下的狀況比作上世紀 90 年代末
的首個互聯(lián)網(wǎng)泡沫期：“對于該往哪兒投錢，企業(yè)無所適從。”如果它們早早地在
AI 上投入巨資，就要冒著對一文不值的創(chuàng)業(yè)公司過度依賴或為之浪費大筆金錢
的風險，就像互聯(lián)網(wǎng)早期許多公司的經(jīng)歷那樣。但如果它們等得太久，又有可能
把自己置于被市場新貴顛覆的境地，還可能被更快掌握了新技術(shù)的競爭對手沖擊。
背景介紹
Dotcom Boom 是指 1995 年至 2000 年期間出現(xiàn)的巨大互聯(lián)網(wǎng)投資泡沫，在
此期間各種互聯(lián)網(wǎng)初創(chuàng)公司市值飆漲，人們對“.com”和“e 字母打頭”的公司
瘋狂投資，以獲取巨額利益，這在當時成為可能。股價的飆升和買家炒作的結(jié)合，
以及風險投資的廣泛利用，使得這些企業(yè)摒棄了標準的商業(yè)模式，大部分缺乏切
實可行的計劃和管理能力。直到約 2000，2001 年，許多公司都面臨破產(chǎn)，互聯(lián)
網(wǎng)行業(yè)的繁榮也就宣告終結(jié)。
單詞
spot [sp?t]
vt. 認出，發(fā)現(xiàn)
例：
If you spot any mistakes in the book just mark them out.
如果你發(fā)現(xiàn)書中有錯誤，請標出來。
monitor [?m?n?t?r]
vt. 監(jiān)控，監(jiān)測
例：
The CIA had been closely monitoring their activities.
中央情報局密切地監(jiān)視著他們的活動。
pinpoint [?p?np??nt]
vt. 準確指出，確定
例：
He was able to pinpoint the precise location of the village.
他能準確找出那個村莊的位置。
scrutiny [?skru?t?ni]
n. 審查
例：
But it takes collective scrutiny and acceptance to transform a discovery claim into a
mature discovery.
但是將一項發(fā)現(xiàn)的申明轉(zhuǎn)變?yōu)橐豁棾墒斓陌l(fā)現(xiàn)是需要集體的審查和接受的。
（2012 年考研英語一閱讀理解 Part A Text 3）
exhilarating [?ɡ?z?l?re?t??]
adj. 令人興奮的
例：
My first bungee jumping was an exhilarating experience.
我第一次蹦極的經(jīng)歷很令人興奮。
perilous [?per?l?s]
adj. 非常危險的
例：
It was a perilous journey.
那是一次冒險的旅程。
array [??re?]
n. 一系列，一批，大量
例：
There was a vast array of colours to choose from.
有各種各樣的顏色可供選擇。
vendor [?vend?r]
n. 供應商，廠商
例：
The company has signed a partnership agreement with Chinese software vendor.
這個公司與中國的一家軟件供應商簽署了一份合作協(xié)議。
algorithm [??lɡ?r?e?m]
n. 算法
例：
An algorithm is a list of steps to follow in order to solve a problem.
算法是用來解決特定問題的一系列步驟。
stumble [?st?mbl]
vi. 絆倒
例：
She stumbled over a log.
她被一塊木頭絆了一跤。
draw a parallel
作對比
例：
It would be easy to draw a parallel between the two cultures.
將這兩種文化作個比較很容易就會發(fā)現(xiàn)二者的相似之處。
boom [bu?m]
n. 繁榮
例：
He made a fortune during the property boom.
在房地產(chǎn)繁榮時期他賺了大錢。
flail [fle?l]
vi. & vt. 揮動，胡亂擺動
例：
His arms were flailing in the air.
他的雙臂在空中胡亂揮舞著。
disruption [d?s?r?p?n]
n. 毀壞
例：
But today, a disruption to family fortunes can no longer be made up with extra
income from an otherwise-stay-at-home partner.
但如今，家庭財富的破壞再也不能由其他家庭成員的額外收入來彌補了。（2007
年考研英語閱讀理解 Part A Text 3）
harness [?hɑ?rn?s]
vt. 利用，控制
例：
They are very interested in harnessing new sources of power.
他們對開發(fā)利用新能源非常感興趣。
長難句
If they invest huge sums in AI early on, they run the risk of overcommitting
themselves or paying large amounts for worthless startups, as many did in the early
days of the internet.

轉(zhuǎn)載于:https://www.cnblogs.com/wanghui626/p/9064955.html

與50位技術(shù)專家面對面20年技術(shù)見證，附贈技術(shù)全景圖

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