This is just a rehashing of old news. Earlier this year, Mattermark, the company that Bloomberg Beta worked with, was making the bold claim, "Mattermark Founder

This is just a rehashing of old news. Earlier this year, Mattermark, the company that Bloomberg Beta worked with, was making the bold claim, "Mattermark Founder Prediction Is 25X Better Than Chance."[1] As I noted at the time: > Predicting who in a group of 1.5 million technology professionals is likely to start a company presents an unsupervised learning problem. Short of contacting all 1.5 million people and asking them, there is no way to confirm whether the predictions the system made are correct, so you cannot make claims about efficacy. > There are approaches used to deal with unsupervised learning problems, but there are no details in this post even indicating that the folks involved recognized they were dealing with an unsupervised learning problem in the first place. Instead, we just have claims like "While we believe the future founders group has a 17% chance — 25x higher..." for which no further information is provided. Beyond the unpublished methodology, it would be interesting to understand the data they modeled against. "Mattermark then mined publicly available information on social network sites such as Twitter, Facebook and LinkedIn to find information on one-and-a half-million professionals who were in some way connected to technology start-ups" is awfully ambiguous. What were the "in some way connected" criteria? Was there any validation of the data? Was de-duplication attempted, and if so, how was it performed? How was age determined when it often isn't displayed on public profiles? [1] https://news.ycombinator.com/item?id=7473203 https://news.ycombinator.com/item?id=7473203