‘Deep learning’ — the hot topic in AI Posted on : May 19 - 2018

Deep learning may be one of the most overhyped of modern technologies, but there is a good chance that it will one day become the secret sauce in many different business processes. For anyone entering the workforce now — or thinking about how to position their career for the long term — this would be a very good time to understand its implications better.

The term “deep learning” refers to the use of artificial neural networks to carry out a form of advanced pattern recognition. Algorithms are trained on large amounts of data, then applied to fresh data that is to be analysed. It has become the hottest subject in the field of artificial intelligence, thanks in particular to breakthroughs in image and language recognition in recent years that have approached or surpassed human levels of comprehension.

The potential scale of deep learning’s impact on business was laid out last month in a report from McKinsey Global Institute, Notes from the AI Frontier: Insight from Hundreds of Use Cases. Depending on the industry it is in, the value a company could hope to gain from applying this technology ranges from 1 to 9 per cent of its revenues, according to the consultants.

This points to trillions of dollars of potential impact on business — and the workers who are the first to learn how to apply it will be the big winners, according to Michael Chui, a McKinsey partner.

“If you learn sooner and faster, you have the chance to do much better relative to others,” he says. That applies not only to people with technical skills, but to any manager who works out how to use the technology to tackle business problems.

The reason that managers who learn how to make use of the technique have the chance to leap ahead of others, Chui explains, is that technologies such as deep learning can have an outsized impact throughout a business. “The technologies are levers of value creation . . . digital means you can do more, faster. If you can successfully scale something across an organisation or a customer base, you have that much more impact.”

The best way to think of deep learning is as a form of advanced analytics. Given enough data to train the algorithm, it can be used in many different tasks. The challenges include identifying the types of problem that are most susceptible to being solved with this technique, picking the particular approach that is best in any given situation and making sure the algorithms are fed with a good supply of high-quality and timely data. View More