As technological advances continue to rise globally, smart production systems require innovative solutions to increase the sustainability of businesses while reducing costs. This means that businesses have to embrace emerging technologies ranging from IoT, Artificial intelligence, 5G, robotics, biometrics, 3D printing, and many more. AI-driven technologies such as cloud computing, big data, cognitive analysis, machine learning, virtual reality, present new industrial paradigms that if well incorporated would facilitate global sustainability. Of great interest is machine learning, a subset of AI that continues to become an important topic for today’s tech giants.
One would wonder, what is machine learning? Well, machine learning is a paradigm that refers to learning from past experiences (previous data) to improve future performance. The field’s sole focus is automatic learning methods. Learning, in this case, refers to the modification of algorithms based on past “experiences” automatically without external assistance from humans.
Many companies are already designating the use of ML to enhance efficiency in their businesses.75% of Netflix users select films recommended to them by the company’s machine learning algorithms while open jobs on LinkedIn request ML expertise in the US further reflecting its growing dominance in all businesses. This brings up the question, what are the applications of ML?
ML addresses the problem of getting computers to re-program themselves whenever exposed to new data based on some initial learning strategies provided. The applications of ML thus include; text interpretation, facial recognition software, biosurveillance, spam filtering, fraud detection, predictive analytics, call center virtual assistants, automation control such as self-driving cars to mention just a few.
In retail, its application is on the websites that recommend items a customer may like based on previous purchases through analysis of buying history. Human resources applications of ML are on resume screening whereas banks and other businesses in the financial industry use ML to identify important insights in data and to prevent fraud. Credit unions are expected to adopt ML by the end of 2020 to automate routine tasks and free up human underwriters to focus on providing more personalized services including fraud management. This is according to the Fannie Mae survey of mortgage lenders. Similarly, according to Enterprise Technology Trends, Salesforce Research 2019, 83% of IT leaders say AI and ML are transforming customer engagement and 69% say it is transforming their business.
With the rising adoption of ML in various sectors of the economies globally, it is evident that ML provides great opportunities not only for the business owners but also for the customers. To be noted, however, is the fact that its adoption will be determined by the level of technological investment, its development pace, and the rate of speed at which its benefits are realized.