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Arturo Announces Seed Series Close and Spin-out from American Family Insurance

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CHICAGO & MADISON, Wis.--(BUSINESS WIRE)--Arturo, an artificial intelligence (AI) and deep-learning property analytics start-up, announced today the close of Seed Series Transaction, with lead investor and Arturo co-developer American Family Insurance. Following the close of the transaction, Madison-based American Family Insurance will remain a customer as well as an investor in Arturo. Arturo grew out of investment and more than three years of research by American Family Insurance on the application of artificial intelligence (AI) and deep learning to satellite, aerial, drone, and ground-level imagery to accurately assess physical property characteristics for residential and commercial properties. Arturo's AI-powered analytics generate detailed property information often in under five seconds. It enables a variety of businesses that insure, lend, invest, or manage residential or commercial properties to make more informed decisions and better manage risk with the most up to date information available.


Topical Plenary Speakers

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Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018. Prof. Willett received the National Science Foundation CAREER Award in 2007, was a member of the DARPA Computer Science Study Group 2007-2011, and received an Air Force Office of Scientific Research Young Investigator Program award in 2010. Prof. Willett has also held visiting researcher positions at the Institute for Pure and Applied Mathematics at UCLA in 2004, the University of Wisconsin-Madison 2003-2005, the French National Institute for Research in Computer Science and Control (INRIA) in 2003, and the Applied Science Research and Development Laboratory at GE Medical Systems (now GE Healthcare) in 2002.


Multi-university collaboration will use data science to find the next El Nino

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Hurricane Harvey, shown in 2017. A new data project hopes to sniff out weather patterns. The El Nino and La Nina patterns in the Pacific Ocean are notorious for their long-distance effects on weather as far away as Africa and the Midwestern United States. But climate experts also know of several other such patterns, known as teleconnections, and believe that there are many more to be discovered. The new TRIPODS Climate project, a collaboration among the University of Wisconsin–Madison, the University of Chicago, and the University of California, Irvine, will develop novel data science tools to sniff out these hidden patterns, improving weather forecasts and scientific understanding of global climate.


Jews In America: Ivanka Trump Calls For Religious Tolerance After JCC Threats But What About Muslim Ban?

International Business Times

Ivanka Trump made a statement Monday on Twitter to show her support for the Jewish community after 11 Jewish Community Centers received bomb threats on Presidents Day. The JCCs in Birmingham, Milwaukee, Cleveland, St. Paul, Tampa, Chicago, Tulsa, Houston, Buffalo and Nashville were evacuated in response to the threats. The centers were reopened after investigators did not find any explosive device. The recent threats follow the FBI's hate crime investigation last month when about 60 threats were received by JCCs. President Donald Trump has not yet commented on the latest incident.


Chris Christie thinks he'd be president if Trump hadn't run, report says

FOX News

FILE - In this July 3, 2017, file photo, New Jersey Gov. Chris Christie speaks in Trenton, N.J. Christie says he confronted a Chicago Cubs fan during Sunday's game against the Milwaukee Brewers because the man said "some really lousy, awful stuff" with a lot of children around.