JUNE 2019CIOAPPLICATIONSEUROPE.COM8ore and more data is collected today at faster and faster rates, currently more than 2.5 exabytes (the equivalent of 5M laptop hard drives) per day, according to recent estimates by Domo, an American software company. Together with these ever-growing data resources, technologies and methodologies are being developed to extract "actionable insight". One successful approach is Machine Learning (ML) which extracts structure and relationships from the data. One common setting, so-called `supervised learning', establishes a mapping from independent variables, typically called features, to a dependent variable, typically called target. There have been considerable developments in the field of ML. For instance, in 2013, speech recognition technology still delivered error rates of 1 in 5 words, but today it gives largely accurate results (< 5 percent error), as we ourselves can easily experience through our smartphones. Image classification is on-a-par with human performance. Tech optimists argue that ML and Artificial Intelligence (AI) will soon be solving most of our problems. I will argue here that whilst it is certainly true that considerable progress has been made in the field, certain challenges will remain. These MJOHANNES BAUER, DATA SCIENTIST, IHS MARKITKnow the Limitations of YOUR MACHINEIN MYOPINIONJohannes Bauer
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