by Angela Guess
In a recent article by Shivon Zilis and James Cham, both partners at Bloomberg Beta, the pair write, “Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year’s landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over ‘problem first’ machine intelligence—we’ve invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. (Our fund focuses on the future of work, so there are some machine intelligence domains where we invest more than others.)”
The article continues, “At the same time, the hype around machine intelligence methods continues to grow: the words “deep learning” now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like ‘big data’ (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one. We favor those who apply technology thoughtfully. What’s the biggest change in the last year? We are getting inbound inquiries from a different mix of people. For v1.0, we heard almost exclusively from founders and academics. Then came a healthy mix of investors, both private and public. Now overwhelmingly we have heard from existing companies trying to figure out how to transform their businesses using machine intelligence.”
Photo credit: Bloomberg Beta