Machine learning-based approach: global trends, research directions, and regulatory standpoints

Abstract

The field of machine learning (ML) is sufficiently young that it is still expanding at an accelerating pace, lying at the crossroads of computer science and statistics, and at the core of artificial intelligence (AI) and data science. Recent progress in ML has been driven both by the development of new learning algorithms theory, and by the ongoing explosion in the availability of vast amount of data (often referred to as "big data") and low-cost computation. The adoption of ML-based approaches can be found throughout science, technology and industry, leading to more evidence-based decision-making across many walks of life, including healthcare, biomedicine, manufacturing, education, financial modeling, data governance, policing, and marketing. Although the past decade has witnessed the increasing interest in these fields, we are just beginning to tap the potential of these ML algorithms for studying systems that improve with experience. In this paper, we present a comprehensive view on geo worldwide trends (taking into account China, the USA, Israel, Italy, the UK, and the Middle East) of ML-based approaches highlighting the rapid growth in the last 5 years attributable to the introduction of related national policies. Furthermore, based on the literature review, we also discuss the potential research directions in this field, summarizing some popular application areas of machine learning technology, such as healthcare, cyber-security systems, sustainable agriculture, data governance, and nanotechnology, and suggest that the "dissemination of research" in the ML scientific community has undergone the exceptional growth in the time range of 2018–2020, reaching a value of 16,339 publications. Finally, we report the challenges and the regulatory standpoints for managing ML technology. Overall, we hope that this work will help to explain the geo trends of ML approaches and their applicability in various real-world domains, as well as serve as a reference point for both academia and industry professionals, particularly from a technical, ethical and regulatory point of view.