Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering

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.