Martha Heller recently wrote in CIO.com, “Sure, machine learning has already had a significant impact on the worlds of science and culture, and in life, but it will be years before CIOs need to start worrying about enterprise machine learning applications… right? No. That’s not true, according to Dan Olley, CTO of Elsevier, a global information solutions company. ‘If CIOs invested in machine learning three years ago, they would have wasted their money,’ Olley says. ‘But if they wait another three years, they will never catch up’.”

Heller goes on, “For years, computing has been stuck in an ‘if/then’ paradigm. ‘Computers are good at A=B, or A > B, but they are bad at A is similar to B’ says Olley. ‘Until now, only humans could handle ‘similar to’ situations, but with machine learning, we can train algorithms to perform highly complex functions from describing an image to making judgement calls.’ Say you want to sort and categorize all of your digital photos. ‘If every picture of a dog were identical, it would be easy for an application to recognize dog photos and tag them appropriately,’ Olley says. ‘But dogs are not identical to one another, so the machine needs to see a series of photos labelled ‘dog’ until it learns to recognize dogs in the abstract. But once it’s trained, the machine can sort those photos on its own’.”

She continues, “To Olley, machine learning fills a gap in technology that has existed for a long time: solving complex problems with pattern recognition. ‘With the majority of Elsevier’s revenue coming from technology-based products and services, we started using machine learning in our commercial products, but it’s equally applicable to internal IT platforms,’ Olley says. ‘Take the classic challenge of matching customer contacts and addresses or spotting trends in your financial data. The more you train the application to ‘understand’ the data, the better your predictive analytics’.”

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