
DATA SCIENCE AND MACHINE LEARNING
Undoubtedly, a machine learning algorithm is forming a more seamless world by adding a new dimension to our daily lives. The community also motivated a more comprehensive and coordinated adoption of machine learning technologies, including utilizing machine learning in essential areas such as education, government services, and community wellbeing, to improve the quality of social life.
We see a positive influence of machine learning as a result of these initiatives in the customer service industry, which is progressively turning to machine learning in managing its growing needs. Mobile applications are now developed to sustain seamless government service delivery by seeing chatbots used across retail, banking, healthcare and government services.

Numerous transformational technologies such as an autonomous vehicle, robotics, artificial intelligence (AI), the Internet of Things (IoT) and 3D printing, is the new era of the fourth industrial revolution. AI is one of the most paramount technologies driving the fourth industrial revolution through machine learning and Deep Learning. Machine learning has enormous potential and uses cases across businesses, including retail, security, healthcare, and transportation.
Businesses that are profound in taking up machine learning must focus on two things – the business case benefits and the use case benefits. They must adopt an agile and phased strategy to start building momentum.
A research firm, Venture Scanner, emphasize that the adoption of Machine Learning technologies in marketing organizations increased by 44% in 2018, as compared to 2017. The use of this technology and algorithm by customer service teams is projected to increase by 143% over the next 18 months.
However, the most significant challenges to implementing machine learning are the perceived threat of redundancy or unemployment. In reality, machine learning should be developed to adapt to different purposes in the labour market rather than make it redundant. Machine learning might support the efficiency in the workforce, as labour intensive and monotonous tasks can be done by machines, while humans focus on higher-level qualitative tasks.
The World Economic Forum (WEF) reports that despite machine learning and algorithms substituting 75 million jobs by 2022, they will create another 133 million new roles by the same period.
Machine learning technologies and algorithms are influencing our everyday lives in numerous ways. One of the largest beneficiaries of machine learning Technology is security. For instance, in the Middle East, machine learning is being executed for image processing, facial recognition, and predictive analytics. This technology is driving its possibility to deliver more personalized experiences in the retail sector through chatbots.
In healthcare, machine learning is helping the industry shift from traditional methods by persuading the use of comprehensive algorithms and software to support doctors in diagnosing patients and disease. When it comes to energy and R&D, machine learning is allowing precision drilling, reservoir management and driving safety and production. Machine learning is also poised to transform the transportation sector globally. In the UAE, the first autonomous taxis have launched trials and projected to introduce them on the roads soon, once safety and traffic feasibility have been addressed.
In conclusion, machine learning represents a tremendous revenue growth opportunity widely. For example, in the UAE, the government has disclosed a strategy to position the country as a hub for Artificial Intelligence by leveraging machine learning technology across government services and the private sector. It also targets to recruit and train people to work in industries that will employ machine learning engineer shortly. This is a massive opportunity for businesses looking to grow their machine learning capabilities in the future.
About the Author
Wie Kiang is a researcher who is responsible for collecting, organizing, and analyzing opinions and data to solve problems, explore issues, and predict trends.
He is working in almost every sector of Machine Learning and Deep Learning. He is carrying out experiments and investigations in a range of areas, including Computer Vision, Natural Language Processing, and Reinforcement Learning.