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Emotions make us human. Researchers at The Alan Turing Institute in the United Kingdom (opens in new tab)<\/span><\/a> are using artificial intelligence and machine learning to push the state of the art in data science to better understand what makes us happy, angry and frustrated<\/a>.<\/p>\n \u201cOur research seeks to try and measure aspects of the world that we, as humans, are hugely aware of but that traditionally we’ve had near to no numbers on,\u201d says Suzy Moat, an associate professor of behavioral science at Warwick Business School and a Turing Fellow.<\/p>\n For example, many people believe that the environment they spend their time in profoundly impacts their day-to-day well-being. Until recently, however, scientists lacked the data to test this hypothesis, Moat explained. Now, the world is awash in data along with the computing resources to quantitatively analyze what settings make people happy.<\/p>\n \u201cWe found that people are happier in more scenic environments, even after controlling variables such as the weather, activities, local income, and, crucially, whether a setting is natural or urban,\u201d says Chanuki Seresinhe, a computational social sciences researcher at The Turing. \u201cOur results offer evidence that the aesthetics of the environments that policymakers choose to build or demolish may have consequences for our subjective well-being.\u201d<\/p>\n Seresinhe\u2019s findings are based on data she gathered from the smartphone app Mappiness, which maps happiness across space in the U.K., and crowdsourced ratings of the \u201cscenic-ness\u201d of photographs taken all across England from the online game Scenic-Or-Not.<\/p>\n Seresinhe analyzed three years of happiness measurements from more than 15,000 people, combined with nearly a million ratings of the scenic-ness of more than 100,000 locations in England, mapped to a 1-kilometer resolution. By using Azure GPUs (Graphical Processing Units) to run her deep learning models, she saved months of time.<\/p>\n \u201cI originally did my image processing on my own computer, taking months to do what now takes just days,\u201d she says. \u201cBut being able to correlate large, unstructured data sets using Azure is helping me really push out the horizons of my investigations. That\u2019s huge.\u201d<\/p>\n