Earlier this week, Google open-sourced TensorFlow. According to Matt Cutts, who has run Google’s spam algorithms for years, TensorFlow is essentially Google’s “secret-sauce.” That said, Google clearly believes machine learning is incredibly important and is willing to invest billions in R&D. So why would they be willing open source their core technology?
This all started with a simple question: could we train an algorithm to determine if a Twitter account belonged to a man or a woman? With that in mind, we ran a simple data categorization job, fired up our brand new CrowdFlower AI feature, and tried to answer just that. What we found was, well, pretty damn interesting. But no spoilers. We'll get to all that in a second. Let's take a step back and start at the beginning.
A few weeks ago, we made an announcement at the Rich Data Summit. It deals not just with where we want our company to go, but where we think machine learning and data science are going in the coming years. Which is to say: human-in-the-loop machine learning.
Last week, we hosted the first annual Rich Data Summit in our hometown of San Francisco. And not to toot our own horn, but it went pretty damn well. We'll be posting videos of all our great talks next week (our video folks are working on it as we speak), but we wanted to share a few themes that kept popping up at the conference in case you missed out. Here are the 5 things we learned at the Rich Data Summit:
In the last 10 years, there has been a powerful push for governments at all levels to open the datasets they develop to the public. In 2015, the 3rd International Open Data Conference held in Ottawa, Canada showcased surprisingly rapid progress in the development of principles, standards, measurement metrics and road maps for the growth of open data. With broad statements of support and participation by a rapidly growing set of national, state and civic governments, the momentum is putting increasing pressure on all governments organizations to continue to do even more. And they should.