The age of big data has seen a host of newtechniques for analyzing large data sets. But before any of those techniques can be applied, the target data has to be aggregated, organized, and cleaned up.
That turns out to be a shockingly time-consuming task. In a 2016 survey, 80 data scientists told the company CrowdFlower that, on average, they spent 80 percent of their time collecting and organizing data and only 20 percent analyzing it.
The population of the scenic ski-resort Davos, nestled in the Swiss Alps, swelled by nearly +3,000 people between the 17th and 20th of January. World leaders, academics, business tycoons, press and interlopers of all varieties were drawn to the 2017 World Economic Forum (WEF) Annual Meeting. The WEF is the foremost creative force for engaging the world’s top leaders in collaborative activities to shape the global, regional and industry agendas for the coming year and beyond. Perhaps unsurprisingly given recent geopolitical events, the theme of this year’s forum was Responsive and Responsible Leadership.
We need journalism about machine learning, artificial intelligence, and data science desperately. Not just to calm the public conversation, which always seems to be full of hype on these topics, but to make sure that work in our field is sustainable. And no one is going to make the case for our industry unless we do it ourselves.
In episode fifteen of Talking Machines, we talk with Max Welling, of the University of Amsterdam and University of California Irvine. We talk with him about his work with extremely large data and big business and machine learning.
When Google bought Boston Dynamics last December, the news made headlines, but it was not the first time the Internet giant has invested in DARPA-funded robotics. As part of Robohub’s Big Deals series, we asked Gill Pratt, Program Manager of DARPA’s Defense Sciences Office, to shed some light on what DARPA thinks about Google’s robotics acquisitions , and what it might mean to the robotics and open source communities.