Plenty of Boston technology news this week, from acquisitions to fundings, machine-learning-powered awards predictions and more:
—The New York Times says police-affiliated scientists in China were using equipment from Waltham, MA-based Thermo Fisher Scientific (NYSE: TMO) to collect DNA data and build “an enormous system of surveillance and control” over members of a predominantly Muslim ethnic group to make them more subservient to the Communist Party. Thermo Fisher told the newspaper it would stop selling equipment in the part of China where the collection was taking place, and it’s working with American officials to get to the bottom of how its equipment was being used there.
—First, DataRobot took a swing at predicting the Grammy’s Song of the Year with its machine learning platform. (It did, with its prediction that Childish Gambino would walk away with the award for “This Is America.”) Now, the Boston-based startup trained its tool on the Oscars‘ Best Picture award. DataRobot analyzed reams of data about past nominees and winners from IMDb to determine which attributes are most predictive of winners. Then, it looked at this year’s nominees. The startup says “Roma” has the best chance of winning (17.8 percent), according to its data-crunching. Roma is followed by “A Star Is Born” (16.2 percent), “The Favourite” (15.5), and “Black Panther” (15.2).
—[Added.] Zoba, a startup trying to help companies deploy shared vehicles like bikes and scooters more efficiently in urban areas, has raised a $3 million seed round led by early stage technology investor CRV, whose general partner Izhar Armony will join the company’s board. Founder Collective, Kaggle founder and CEO Anthony Goldbloom, Xobni and Sincerely founder Matt Brezina, and investor and Dallas Mavericks owner Mark Cuban also participated in the round. The startups says its working with “industry-leading bike, scooter and car share companies in the U.S. and abroad” to predict demand for shared mobility services based on city layout and weather so companies can optimize availability in real time.
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