This book provides a structured overview of artificial intelligence-empowered applied software engineering. Evolving technological advancements in big data, smartphone and mobile software applications, the Internet of Things and a vast range of application areas in all sorts of human activities and professions lead current research towards the efficient incorporation of artificial intelligence enhancements into software and the empowerment of software with artificial intelligence. This book at hand, devoted to Novel Methodologies to Engineering Smart Software Systems Novel Methodologies to Engineering Smart Software Systems, constitutes the first volume of a two-volume Handbook on Artificial Intelligence-empowered Applied Software Engineering. Topics include very significant advances in (i) Artificial Intelligence-Assisted Software Development and (ii) Software Engineering Tools to develop Artificial Intelligence Applications, as well as a detailed Survey of Recent Relevant Literature. Professors, researchers, scientists, engineers and students in artificial intelligence, software engineering and computer science-related disciplines are expected to benefit from it, along with interested readers from other disciplines.
Evolving technological advancements in big data, smartphone and mobile software applications, the Internet of Things and a vast range of application areas in all sorts of human activities and professions, lead current research toward the efficient incorporation of artificial intelligence enhancements into software and the empowerment of software with artificial intelligence. The book at hand, devoted to Smart Software Applications in Cyber-Physical Systems, constitutes the second volume of a two-volume Handbook on Artificial Intelligence-empowered Applied Software Engineering. Topics include very significant advances in Smart Software Applications in (i) Scientific Document Processing, (ii) Enterprise Modeling, (iii) Education, (iv) Health care and Medicine, and (v) Infrastructure Monitoring. Professors, researchers, scientists, engineers, and students in artificial intelligence, software engineering, and computer science-related disciplines are expected to benefit from it, along with interested readers from other disciplines.
In recent years, a diffuse way of promoting information and communication technologies is through the quantification of data, such as indices of references, access counters to webpages, total views of videos on YouTube, and others that are often superficial for didactics. Some data are presented as magnets to attract students to education courses—education as a commercial product and not as a public good freely accessible to all. This is an attractive format in offline and online communication channels, but it lacks metrics to build and evaluate knowledge and experiences with a critical perspective. Assessment Methods and Success Factors for Digital Education and New Media analyzes and evaluates the efficiency of the teaching-learning process and dissemination of knowledge to make the most of the potential of new information and communication technologies, examining the theories and practices of interactive content, under the formula of constructive criticism. Covering topics such as digital challenges, information architecture, and interactive design, this premier reference source is an excellent resource for educators and administrators of both K-12 and higher education, software developers, interactive designers, preservice teachers, teacher educators, evaluators of interactive systems, government officials, librarians, researchers, and academicians.
The past decade has witnessed a wide adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight into their widespread implementation has resulted in harmful outcomes that could have been avoided with proper oversight. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes responsible AI, a holistic approach for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. It's an ambitious undertaking that requires a diverse set of talents, experiences, and perspectives. Data scientists and nontechnical oversight folks alike need to be recruited and empowered to audit and evaluate high-impact AI/ML systems. Author Patrick Hall created this guide for a new generation of auditors and assessors who want to make AI systems better for organizations, consumers, and the public at large. Learn how to create a successful and impactful responsible AI practice Get a guide to existing standards, laws, and assessments for adopting AI technologies Look at how existing roles at companies are evolving to incorporate responsible AI Examine business best practices and recommendations for implementing responsible AI Learn technical approaches for responsible AI at all stages of system development
