The field of Artificial Intelligence (AI) has been witnessing a recent upsurge in research. Multiple software companies are shifting their focus to developing intelligent systems; and many others are deploying AI paradigms to their existing processes. In parallel, the academic research community is injecting AI paradigms to provide solutions to traditional engineering problems. AI has evidently been proved useful to Software Engineering (SE). When one observes the SE phases (Requirements, Design, Development, Testing, Release and Maintenance), it becomes clear that multiple AI paradigms (such as: Neural Networks, Machine Learning, Knowledge-Based Systems, Natural Language Processing) could be applied to improve the process and eliminate many of the major challenges that the SE field has been facing. At the Edge of Intelligence is a major guide to provide intelligent solutions to some of the most important SE problems.
At the Edge of Intelligence challenges the current conventional wisdom of the state-of-the-art for applying AI to SE, as well as providing a call for action for AI democratization and re-definig the path forward. The book also covers multiple examples of AI applications in a variety of domains (healthcare, image analysis, government, and others). Moreover, the book focuses on challenges and prospects of AI through open source code, data openness, and open science in general, and how would that affect the future of AI, software, and recent technological waves.
At the Edge of Intelligence serves as a major guide to software developers for injecting intelligent methods in their software and the process of development. Additionally, this book is beneficial to software team managers in running their teams, evaluating time and cost, among other methods that can elevate team’s productivity. Moreover, many scientists and researchers are increasingly interested in applying data science, data openness, code openness, and open source programming. AI is increasingly becoming a multidisciplinary field, it is getting applied across multiple domains (such as SE, healthcare, government, among others) and this book is the first to clearly address the disadvantages, future trends, dangers, and idiosyncrasies of that activity. The authors cover data science applications in multiple domains (with a focus on software engineering). More importantly, this book raises awareness about a very relevant issue for developers: openness of data, openness of code, and general open science. The book covers how ‘openness’ will be a major player in AI in the very near future. All the mentioned points make this book stand out among many others, and introduce an important aspect that has been missing for a long time in the research fields of AI and SE.
Feras A. Batarseh is an Associate Professor with the Department of Biological Systems Engineering at Virginia Tech (VT) and the Director of A3 (AI Assurance and Applications) Lab. His research spans the areas of AI Assurance, Cyberbiosecurity, AI for Agriculture and Water, and Data-Driven Public Policy. His work has been published at various prestigious journals and international conferences. Additionally, Dr. Batarseh published multiple chapters and books, his two recent books are: “Federal Data Science”, and “Data Democracy”, both by Elsevier’s Academic Press.
Dr. Batarseh is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), the Agricultural and Applied Economical Association (AAEA), and the Association for the Advancement of Artificial Intelligence (AAAI). He has taught AI and Data Science courses at multiple universities including George Mason University (GMU), University of Maryland – Baltimore County (UMBC), Georgetown University, and George Washington University (GWU).
Dr. Batarseh obtained his Ph.D. and M.Sc. in Computer Engineering from the University of Central Florida (UCF) (2007, 2011), a Juris Masters of Law from GMU (2022), and a Graduate Certificate in Project Leadership from Cornell University (2016). He currently holds courtesy appointments with the Center for Advanced Innovation in Agriculture (CAIA), National Security Institute (NSI), and the Department of Electrical and Computer Engineering at VT.
Ruixin Yang is an Associate Professor in the Department of Geography and GeoInformation Sciences (GGS) ― College of Science at George Mason University (GMU), Fairfax, VA. He received his PhD in Aerospace Engineering from University of Southern California (USC) in 1990. His research work ranged from Fluid Dynamics to Astrophysics and General Relativity to Data Science, Information Systems, Data Mining, and Earth Systems Science. Dr. Yang led a software development team that built several prototypes for earth science information systems. His recent research is focused on data mining methods for hurricane-related earth science. He has published several referred papers on earth science data search, online analysis, metadata management, content-based search, and big data analytics.
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