「釋義」

算法是為了解決一個(gè)問(wèn)題而設(shè)計(jì)的一種策略。解決不同的問(wèn)題，需要不同的算法。

計(jì)算機(jī)算法，是用計(jì)算機(jī)解決問(wèn)題的方法、步驟，常用于計(jì)算、數(shù)據(jù)處理和自動(dòng)推理。

作為一個(gè)有效方法，算法包含了一系列定義清晰的指令，并在有限步驟中清楚地表述出來(lái)。

?

「應(yīng)用場(chǎng)景」

對(duì)于大多數(shù)管理者，做預(yù)測(cè)是工作的一部分：HR決定聘用人選，是預(yù)測(cè)誰(shuí)工作最出色；營(yíng)銷人員選擇分銷渠道，是預(yù)測(cè)產(chǎn)品在哪里最好賣；風(fēng)險(xiǎn)投資人決定是否投資某家初創(chuàng)企業(yè)，是預(yù)測(cè)它能否成功。為做好種種商業(yè)預(yù)測(cè)，越來(lái)越多企業(yè)現(xiàn)在求助于計(jì)算機(jī)算法——這種技術(shù)能以驚人速度完成超大規(guī)模分析過(guò)程。

Most managers’ jobs involve making predictions. When HR specialists decide whom to hire, they’re predicting who will be most effective. When marketers choose which distribution channels to use, they’re predicting where a product will sell best. When VCs determine whether to fund a start-up, they’re predicting whether it will succeed. To make these and myriad other business predictions, companies today are turning more and more to computer algorithms, which perform step-by-step analytical operations at incredible speed and scale.

?

算法能讓預(yù)測(cè)更準(zhǔn)確，但也會(huì)帶來(lái)風(fēng)險(xiǎn)，尤其是在我們不理解這些算法的情況下。諸如此類廣為人知的例子不勝枚舉。Netflix為了更精確地了解用戶看電影的口味，曾拿出100萬(wàn)美元征集內(nèi)容推薦算法，很多數(shù)據(jù)科學(xué)家組隊(duì)參賽。然而這種算法只在用戶挑選DVD時(shí)能較為準(zhǔn)確地推薦，隨著Netflix的用戶轉(zhuǎn)向在線電影，其偏好與算法的預(yù)測(cè)結(jié)果就會(huì)出現(xiàn)偏離。

Algorithms make predictions more accurate—but they also create risks of their own, especially if we do not understand them. High-profile examples abound. When Netflix ran a million-dollar competition to develop an algorithm that could identify which movies a given user would like, teams of data scientists joined forces and produced a winner. But it was one that applied to DVDs—and as Netflix’s viewers transitioned to streaming movies, their preferences shifted in ways that didn’t match the algorithm’s predictions.

?

《你要管理你的算法》

邁克爾·盧卡，喬恩·克萊因伯格，森迪爾·穆萊納坦

2016年8月刊

“Algorithms Need Managers, Too”

by Michael Luca , Jon Kleinberg and Sendhil Mullainathan

編輯：馬冰侖