Deep learning, a variation of machine learning (ML), represents the major driver toward artificial intelligence(AI), reports Gartner.
As Deep learning delivers superior data fusion capabilities over other ML approaches, the analyst firm predicts that in two years, 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,” says Alexander Linden, research vice president at Gartner.
Gartner’s 2017 Hype Cycle for Emerging Technologies notes deep learning is receiving additional attention because it harnesses cognitive domains that were previously the exclusive territory of humans, mainly image and voice recognition and text understanding.
“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 recognise and understand a specific person’s speech.”
Gartner says 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.
Gartner points out 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.
Gartner’s advice for harnessing deep learning and related technologies around machine learning include starting with simple business problems for which there is consensus about the expected outcomes, and gradually moving toward complex business scenarios.