Deep Learning to Provide Best-in-Class Performance for Demand, Fraud and Failure Predictions By 2019

Around 80 percent of data scientists will have deep learning in their toolkits by 2018


Deep learning, a variation of machine learning (ML), represents the major driver toward artificial intelligence (AI). As deep learning delivers superior data fusion capabilities over other ML approaches, Gartner Inc. predicts that, by 2019, deep learning will be a critical driver for best-in-class performance for demand, fraud and failure predictions.

"Deep learning is here to stay and expands ML by allowing intermediate representations of the data," said Alexander Linden, research VP at Gartner. "It ultimately solves complex, data-rich business problems. Deep learning can, for example, give promising results when interpreting medical images in order to diagnose cancer early. It can also help improve the sight of visually impaired people, control self-driving vehicles, or recognize and understand a specific person's speech."

Deep learning also inherits all the benefits of ML. Several breakthroughs in cognitive domains demonstrate this. Baidu's speech-to-text services are outperforming humans in similar tasks; PayPal is using deep learning as a best-in-class approach to block fraudulent payments and has cut its false-alarm rate in half, and Amazon is also applying deep learning for best-in-class product recommendations.

Today, most common use cases of ML through deep learning are in image, text and audio processing — but increasingly also in predicting demand, determining deficiencies around service and product quality, detecting new types of fraud, streaming analytics on data in motion, and providing predictive or even prescriptive maintenance. However, ML and AI initiatives require more than just data and algorithms to be successful. They need a blend of skills, infrastructure and business buy-in.

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