????根據(jù)數(shù)據(jù)貿(mào)然發(fā)送陌生郵件，感覺(jué)像是赤裸裸的侵犯?jìng)€(gè)人隱私,，正如塔吉特的孕婦廣告一樣,。嗨，我們的算法能預(yù)測(cè)到你的職業(yè)夢(mèng)想,！事實(shí)上,，確實(shí)有人認(rèn)為這是詐騙。但對(duì)于自愿參加了兩次聚會(huì)的75人來(lái)說(shuō),，這是對(duì)他們的一次檢驗(yàn),。

????莫里爾說(shuō)道：“人們會(huì)這樣說(shuō)：‘我想過(guò)創(chuàng)業(yè)，但我從來(lái)沒(méi)有告訴過(guò)任何人,?！谒腥硕己翢o(wú)察覺(jué)的時(shí)候，有人便選擇相信你——這種事真的很酷……他們雖然一直堅(jiān)持自己的夢(mèng)想但從未認(rèn)真考慮過(guò),，而你的信任可以強(qiáng)化他們的夢(mèng)想,。”莫里斯承認(rèn),，告訴人們他們被研究選中,，可能會(huì)改變最終的結(jié)果,。

????Bloomberg Beta負(fù)責(zé)人羅伊?巴哈特對(duì)于最終結(jié)果的多樣性感到欣慰。他說(shuō)：“數(shù)據(jù)不會(huì)有任何偏見(jiàn),。其中很多人有生以來(lái)第一次被賦以這樣的期望,。”

????Bloomberg Beta找出的“潛在創(chuàng)始人”以后是否會(huì)創(chuàng)建公司,，這還有待考證,。（雖然僅僅過(guò)去幾個(gè)月時(shí)間，但巴哈特表示“一大批人”已經(jīng)開(kāi)始了創(chuàng)業(yè),。）同樣,，該項(xiàng)目也沒(méi)有給Blommberg Beta帶來(lái)任何交易。（他說(shuō)道：“了解一個(gè)人是一個(gè)長(zhǎng)期的過(guò)程,，因此,，即使在未來(lái)兩年我們沒(méi)有對(duì)任何人進(jìn)行投資，我也可以接受,?！保┑ㄟ^(guò)創(chuàng)造性地使用數(shù)據(jù)，在交易流程中占據(jù)先機(jī),，這種做法將變得更為常見(jiàn),。Mattermark重新進(jìn)行了一次匿名研究，結(jié)果發(fā)現(xiàn),，其模型預(yù)測(cè)創(chuàng)始人的成功率比先前高出25倍,。

????這是利用數(shù)據(jù)促進(jìn)風(fēng)險(xiǎn)投資的方式之一。另外一種方式是什么,？在董事會(huì)中增設(shè)一名機(jī)器人,。正如香港創(chuàng)投公司Deep Knowledge Ventures的做法。該公司的機(jī)器人董事會(huì)成員,，使用機(jī)器學(xué)習(xí)預(yù)測(cè)最佳生命科學(xué)交易,，利用歷史數(shù)據(jù)來(lái)預(yù)測(cè)對(duì)于人類風(fēng)險(xiǎn)投資者來(lái)說(shuō)不太明顯的趨勢(shì)。正如德米特里?凱明斯基向美國(guó)科技網(wǎng)站Betabeat所解釋的那樣,，機(jī)器人在這個(gè)過(guò)程中不帶任何情緒：

????“人類是情緒化的,，帶有主觀性。他們會(huì)犯錯(cuò)誤,，但與機(jī)器不同,，人類也會(huì)做出明智的直覺(jué)決策。與VITAL類似的設(shè)備只能使用邏輯,。人類投資者的直覺(jué)與設(shè)備的邏輯,，絕對(duì)是完美的組合。犯錯(cuò)誤的風(fēng)險(xiǎn)將被降至最低?！?/p>

????當(dāng)然,，這種方式有些大膽。但為什么不試試呢,？巴哈特說(shuō)道：“當(dāng)你用數(shù)據(jù)完成之前只能由人類完成的事情時(shí),，總會(huì)有人持懷疑態(tài)度，這反而讓我們更想進(jìn)行嘗試,。彭博資訊推出第一款產(chǎn)品時(shí),，人們說(shuō)：‘不，只能由人類對(duì)債券進(jìn)行定價(jià),?！聦?shí)證明，計(jì)算機(jī)做某些事情會(huì)做得更好,?！保ㄘ?cái)富中文網(wǎng)）

????翻譯：劉進(jìn)龍/汪皓

????Cold-emailing people based on data could feel like a creepy invasion of privacy, like Target’s maternity ads. Hi, our algorithm knows your career dreams! Indeed, some people thought it was a scam. But for the self-selecting group of around 75 people that turned up at each party, it was validating.

????“People would say things like, ‘I thought about becoming a founder but I had never even told anyone,’” Morrill says. “When someone believes in you before anyone else—that’s what is really cool here . . . You can actually reinforce a dream they held very closely but never considered seriously.” Morrill admitted that telling people they were in the study probably changes the results.

????Roy Bahat, who leads Bloomberg Beta, was pleased by the diversity of the group. “The data doesn’t discriminate,” he says. “A lot of the people, this was the first time they ever got tapped on the shoulder for something like this.”

????Whether any of Bloomberg Beta’s potential founders have actually founded a company yet is another story. (It’s only been a few months; Bahat says “a bunch” are in the process.) Likewise, the project has not resulted in any deals for Bloomberg Beta. (“It was expected to be a long term process of getting to know people, so even if we fund zero people for the next two years, that’s fine by me,” he says.) But using data creatively to get a leg up on deal flow will only become more common. Mattermark re-ran a blind version of its study and found its model has a 25x better chance of predicting a founder.

????This is one way to boost venture investing with data. Another way? Add a robot to your board of directors, like Deep Knowledge Ventures, a firm in Hong Kong. The firm’s robot board member uses machine learning to predict the best life sciences deals, taking historical data sets to reveal trends that aren’t so obvious to human VC investors. As senior partner Dmitry Kaminskiyexplained to Betabeat, the robot takes emotion out of the process:

????“Humans are emotional and subjective. They can make mistakes, but unlike the machines they can make brilliant intuitive decisions. Machines like VITAL use only logic. The intuition of the human investors together with machine’s logic with give a perfect collaborative team. The risk of the mistake will be minimized.”

????Sure, it’s novel. But why not? “Whenever people are skeptical that you can use data to do something that previously only people had done, that makes us want to try it,” Bahat says. “When Bloomberg rolled out its first product, people were saying, ‘No, human beings have to be the ones to price bonds.’ Turns out a computer can do some of those things better.”