超早期投资：如何能在创业者还未创业的时候就成为伯乐？

    根据数据贸然发送陌生邮件，感觉像是赤裸裸的侵犯个人隐私，正如塔吉特的孕妇广告一样。嗨，我们的算法能预测到你的职业梦想！事实上，确实有人认为这是诈骗。但对于自愿参加了两次聚会的75人来说，这是对他们的一次检验。

    莫里尔说道：“人们会这样说：‘我想过创业，但我从来没有告诉过任何人。’在所有人都毫无察觉的时候，有人便选择相信你——这种事真的很酷……他们虽然一直坚持自己的梦想但从未认真考虑过，而你的信任可以强化他们的梦想。”莫里斯承认，告诉人们他们被研究选中，可能会改变最终的结果。

    Bloomberg Beta负责人罗伊•巴哈特对于最终结果的多样性感到欣慰。他说：“数据不会有任何偏见。其中很多人有生以来第一次被赋以这样的期望。”

    Bloomberg Beta找出的“潜在创始人”以后是否会创建公司，这还有待考证。（虽然仅仅过去几个月时间，但巴哈特表示“一大批人”已经开始了创业。）同样，该项目也没有给Blommberg Beta带来任何交易。（他说道：“了解一个人是一个长期的过程，因此，即使在未来两年我们没有对任何人进行投资，我也可以接受。”）但通过创造性地使用数据，在交易流程中占据先机，这种做法将变得更为常见。Mattermark重新进行了一次匿名研究，结果发现，其模型预测创始人的成功率比先前高出25倍。

    这是利用数据促进风险投资的方式之一。另外一种方式是什么？在董事会中增设一名机器人。正如香港创投公司Deep Knowledge Ventures的做法。该公司的机器人董事会成员，使用机器学习预测最佳生命科学交易，利用历史数据来预测对于人类风险投资者来说不太明显的趋势。正如德米特里•凯明斯基向美国科技网站Betabeat所解释的那样，机器人在这个过程中不带任何情绪：

    “人类是情绪化的，带有主观性。他们会犯错误，但与机器不同，人类也会做出明智的直觉决策。与VITAL类似的设备只能使用逻辑。人类投资者的直觉与设备的逻辑，绝对是完美的组合。犯错误的风险将被降至最低。”

    当然，这种方式有些大胆。但为什么不试试呢？巴哈特说道：“当你用数据完成之前只能由人类完成的事情时，总会有人持怀疑态度，这反而让我们更想进行尝试。彭博资讯推出第一款产品时，人们说：‘不，只能由人类对债券进行定价。’事实证明，计算机做某些事情会做得更好。”（财富中文网）

    翻译：刘进龙/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.”