Artificial Intelligence (AI) as a research field has steadily grown in the past 60 years. However, the last decade has seen exponential growth in its applications, outside the university research laboratories and corporate supercomputing centres. From a research and development area, it has now become a “mainstream” topic. It has become a ubiquitous part of people’s daily lives. There are hundreds of research centres all over the world working on building new technologies using AI, while many more technology companies, from large to small and medium enterprises (SMEs), and even start-ups are trying to yield the benefits from this new technological paradigm.
The Governments are perhaps the last significant entity to enter in this new “AI wave”. Nevertheless, governments have an important role to play when a new technology is practically changing the way people live, and a large part of the population is affected by this technology. It is similar to the Information Technology (IT) wave of the 1990s, but this one is affecting the people on a more complex and profound level. Many countries now have their official government strategies to tap the benefits of AI, as well as to regulate its use and applications. There are large sums of money being invested or promised to be invested over a time-period by governments of these countries.
While there are many exciting aspects regarding the growth of AI as a field, the presented work is an attempt to review how these government strategies are designed, what are the main concerns and objectives, and which are the areas the governments mainly focus while investing their money and efforts in building their strategies for AI. The strategies of several countries were reviewed and a global AI landscape is presented by considering many key indicators involving healthcare, education, defence, and economy. It is also important to look at how the governments, and their respective countries, are prepared to tackle the growth of AI for public domains, which countries are leading others and in which areas, and which are the strong and weak areas in their respective strategies, among other things. Two global AI indices were considered for such a comparative analysis. In the end, the current state-of-the-art and a possible future direction are summarised.
Vaibhav Shah is currently a Research Assistant at the United Nations University Operating Unit on Policy-Driven Electronic Governance (UNU-EGOV), where his research focus is Smart Cities, especially from the governance point of view, as well as the technologies needed for the Smart City development. Besides this, he is also an Assistant Professor at the Department of Information Systems, University of Minho, teaching Master’s Degree students in Information Systems and Management, and Telecommunications and Informatics Engineering. He is also a member of the ALGORITMI research centre at the same university where he has collaborated on several European and International research projects. He has also prepared and delivered a short course on Artificial Intelligence, covering topics from introduction to applications, at the Graduate School of Management of the St. Petersburg University, Russia, in 2019.
Vaibhav received his PhD in Industrial Engineering (with Machine Learning) from the School of Engineering at the University of Minho, Portugal, in 2015, where he started working as a researcher in 2007. He received his MSc in Information Science and Computer Engineering, with Honours, with specialisation in Intelligent Systems from the St. Petersburg State Polytechnic University, Russia, in 2005. He worked as a Project Leader for an R&D project developing a pattern recognition algorithm for handwritten character recognition system for ancient Indic manuscripts, while working in India, from 2005 to 2007, and has experience in commercial software development since 1999. He has published around 40 scientific papers in International journals, conferences and indexed series. His research experience and interests include Artificial Intelligence, Grammatical Inference, Human-Machine Interface and Interaction, Knowledge Engineering & Management, Environment Conservation, Blockchain, Health Informatics, Neuroscience, Cognitive Systems, and Applications of Machine Learning in Biological engineering and related areas.
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