Hot Trend in Artificial Intelligence — Deep Learning

by August 19, 2018

Deep learning might be an overhyped term in Artificial Intelligence (AI) among the modern technologies today, but there is a good possibility that it will drive the business processes of tomorrow. For newbies entering the workforce—or strategizing to position their careers in the long term—this will be an appropriate time to understand its implications effectively.

The term “deep learning” involves the application of artificial neural networks to carry out advanced pattern recognition. Deep learning algorithms are trained on large amounts of data. Once trained, these algorithms are applied on to fresh data to draw insights. Deep learning has become a hot trend in the field of artificial intelligence, credits to its success in image and language recognition in recent years that have surpassed human levels of comprehension.

According to a report from McKinsey Global Institute, a company could hope to gain 1 to 9 percent of its revenues through the application of deep learning depending on the industry the algorithms are deployed in. Michael Chui, a McKinsey partner, adds that technologies such as deep learning can have an outsized impact throughout a business process.


Business Potential in Deep Learning

Most of the business potential in deep learning would emerge from two broad domains: marketing and sales, and supply chains and manufacturing. Companies operating in consumer industries stand to gain the maximum from deep learning to expand its marketing and sales targets. Examples of applications include customer service management, creating personalized offers, acquiring customers through micromarketing and customer-centric prices and promotions.

Application of deep learning in supply chain and manufacturing domains include yield optimization, predictive maintenance of equipment, procurement analytics and inventory optimization. Based on the capabilities of the participating industries, these benefits will take time to prove their worth. There is, for instance, a huge shortage of skilled deep learning professionals. Data scientists and machine learning specialists are among the most sought-after IT experts, attracting the highest pay packets.

However, deep learning will not be restricted to technical specialists alone. Future general business managers will need to understand the applications of deep learning including problem identification and extracting maximum ROI from managing diverse teams with more technical skills.


Impediments to Deep Learning

On the road to deep learning, there are plenty of stumbling blocks. The biggest obstacles involve data, starting with how to collect, clean and label it that makes them practical for training machine learning systems. Michael Chui adds, “Often, there is a lot of data already in existence and little of it gets used”. Frequent problems occur when data collected for one purpose are used as inputs to a different problem, without making adjustments for gaps in the dataset. Constant changes in the patterns of data being collected imply that machine learning models often need to be updated; additionally, the algorithms are required to be retrained at least every month to keep them relevant as pointed by the McKinsey report.

Most companies are at early stages of strategizing how to apply deep learning in their own business processes— if they have thought about this trendy AI Technology at all. But for the future, generation of managerial and IT recruits, deep Learning could one day become a core skill.