Editorial: Special Section on Services Computing Management for Artificial Intelligence and Machine Learning

F IFTEEN years ago, few would have imagined that employees could work entirely remotely or that an entire business infrastructure could exist on the Internet. With the adoption of services computing, a service that allows companies to access processing and data storage through the Internet, these business models are becoming a reality. Services computing requires a multidisciplinary lens that integrates science and technology to bridge the gap between business services and information technology (IT) services [item 1) in the Appendix]. Services computing management involves 1) ensuring services computing strategy which is allied with how the organization manages IT and how IT is aligned with organizational strategy, 2) designing, building, sourcing, and deploying resilient computing solutions, trusted, efficient, and address quality of service (QoS) expectations, and 3) overseeing all matters related to business and IT services operations and resources both across business domains and within domains such as retail, finance, healthcare, logistics, and others [item 2) in the Appendix]. The goal of services computing is to enable IT services and computing technology to perform business services more efficiently and effectively [item 3) in the Appendix]. The pervasive nature of services computing management is exhibited in almost all industry settings [item 4) in the Appendix]. In everyday life, new business service innovations will give rise to an emergent dataand information-focused economy that will only pick up steam as both consumer and business utilization of Internet of Things are advanced. Concomitantly, we are moving toward an era of artificially intelligent (AI) (e.g., cognitive computing) services, which are deployed in multiscale, complex distributed architectures. Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. Computers are increasingly capable of doing things that humans could once do exclusively. Today smart machines are becoming like humans by recognizing voices, processing natural language, learning, and interacting and learning with the physical world through their vision, smell, touch, and other senses, mobility, and motor control. In some cases, they do a much faster and better job than humans at recognizing patterns, performing rule-based analysis on a very large amount of data, and solving both structured and unstructured problems [item 5) in the Appendix].