Deep Learning and the Future of Business

Featured article by Krish Kupathil, founder and CEO of Mobiliya

Artificial intelligence (AI) has been regarded as the stuff of science-fiction movies. From self-driving cars to image classification and face recognition, AI has transformed machines into self-thinkers, replete with human-like intelligence and reasoning. Deep learning, a subset of AI, has allowed for several practical applications of AI in our daily lives. Gaming has been one of the foremost areas where deep learning has made a huge impact: 2016 saw the defeat of South Korean Go master Lee Sedol by Google DeepMind’s AlphaGo program. Deep learning even promises to transform global businesses. It has already made a promising start in solving complex business needs and problems, which were otherwise beyond human capabilities. According to a Forrester research report, investment in AI will increase by 300 percent in 2017, a sign of things to come.

Deep learning will transform the modern workplace. Some of the first areas that will see major deep learning applications are:

Manufacturing:

Manufacturing is one of the most intensive and loss-prone verticals, with even a minor systemic error or manual lapse capable of triggering faulty products and causing massive losses. Fine-tuning manufacturing assembly lines with deep learning or deep neural networks will enable a system to produce a much greater number of finished products that pass quality control tests, making manufacturing increasingly profitable.

Healthcare:

Deep learning algorithms can spot early patterns among patients who are likely to develop life-threatening diseases like cancer in the next one to two years. It can even help in suggesting timelines for conducting and reviewing PET or PET-CT (Positron Emission Tomography–Computed Tomography) scans to check the probability of cancer. Deep learning algorithms can also identify critical parameters predicting hospitalization among other chronic diseases like diabetes.

Finance:

Most finance firms utilize proprietary systems to accurately predict stock market happenings and execute trades. However, these systems are primarily based on the concept of probability in determining the highest and lowest performing stocks. That said, sometimes high-volume trading at great speeds can turn even the least probable stocks profitable. Deep learning systems can better predict such variations while processing enormous quantities of data and trades at breakneck speeds. Likewise, deep learning-powered fraud models can also help credit companies accurately determine who to lend credit to and identify likely future defaulters.

Automotive

Advanced Driver Assistance Systems or ADAS is an area that leverages deep learning to provide robust driver assistance. Popular use cases are object detection, pedestrian detection, and traffic sign detection. These are the very core applications of deep learning which are required by autonomous or driverless cars as well. In addition, deep learning is also required for critical scenarios like detecting driver drowsiness and triggering alert, lane departure warning, blind spot detection and predictive braking. Thus, the next generation of vehicles have to be deep learning ready to deliver solid assistance to consumers.

Customer Service:

The future belongs to brands that deliver enhanced and highly personalized customer service. With deep learning, companies can personalize the emails, coupons and offers that every customer receives—all designed to serve customers better and build lasting customer relationships. Deep learning models can compare the previous buying trends of an individual with an enormous database of millions of other users, and from there provide relevant or allied product purchase suggestions. It can even recognize whether customers are looking to buy certain products as gifts rather than for themselves, which adds a new dimension to customer service and product recommendation.

New Business Models:

The idea of using drones for package delivery has been making rounds. Global ecommerce giant Amazon is already seriously considering this idea by applying machine learning and deep learning mechanisms, making supply chain systems faster, more accurate and efficient. Such drone-based delivery mechanisms allow retail, supply chain and logistics companies to reach areas that were otherwise beyond reach or too expensive.

For business owners and top executives of global businesses, the writing on the wall is clear. Embracing AI through practical deep learning models is the way ahead. While it need not necessarily be in the form of investing millions for sophisticated deep learning applications, even a modest start can go a long way toward eventually changing business for the better.

ABOUT MOBILIYA: Founded in 2011, Mobiliya provides device-to-cloud software engineering and system integration services with specialization in Internet-of-Things, enterprise software, augmented reality, embedded systems, security and automotive. Mobiliya’s engineers take pride in delivering high quality end-to-end solutions for the world’s leading companies. Headquartered in Dallas, Texas, the company has global engineering and delivery centers based out of USA, Canada, India, China and South Korea. For more information, visit www.mobiliya.com.

ABOUT THE AUTHOR: Krish Kupathil is the founder and CEO of Mobiliya. With a track record of over 25 years in building and growing companies and new markets, he has carried out strategic exits, pioneered enterprise mobility, cross-OS communication and collaboration services. His professional CPA qualification gives him a clear understanding of finance, tax and regulatory issues. A veteran of the mobile engineering industry and a recognized influencer worldwide in telecommunication and mobile software, Kupathil has shared his insights at conferences and has been published and quoted in numerous renowned publications. He is a trusted advisor to senior executives in blue chip companies such as Google, Microsoft, Deutsche Telekom, Qualcomm and Intel.