The governments of many cities just starting smart city development will face a critical cold-start problem: how to develop a new smart city service with limited data. We investigate the common process of urban transfer learning, i.e., leveraging transfer learning to accelerate smart city development, and also provide city planners and relevant practitioners with guidelines for applying this novel learning paradigm.

Although vehicular crowdsourcing represents an emerging technology to assist many smart city applications, maintaining sensing data quality is still a challenge. This article considers the challenges and offers a potential solution for a hybrid scenario involving both driverless cars and human-controlled vehicles, within the limited task budget. H. Gao, C. H. Liu and W. Wang, "Hybrid Vehicular Crowdsourcing With Driverless Cars: Challenges and a Solution," in Computer, vol.

Ubisoft's game designers successfully used autonomous tools to develop an innovative virtual world. The authors discuss the reflective practices underlying this success and how autonomous tools enable more complex system design.

A cybersecurity blueprint for the Internet of Things and Industry 4.0 must address ethical behavior of users and developers as well as ways to secure the technology itself. The Future Life Institutes standards for AI practitioners point in this direction.

Although the robot taxi is a proof-of-concept, the volume market introduction of automated vehicles represents the main cyber-physical challenge, necessitating drastically increased design complexity. Challenges and possible architecture and design process solutions are discussed.

AI and blockchain are among the most disruptive technologies and will fundamentally reshape how we live, work, and interact. The authors summarize existing efforts and discuss the promising future of their integration, seeking to answer the question: What can smart, decentralized, and secure systems do for our society?

Science fiction often portrays future AI technology as having sophisticated emotional intelligence skills to the degree where technology can develop compassion. But where are we today? The authors provide insight into artificial emotional intelligence (AEI) and present three major areas of emotion--recognition, generation, and augmentation--needed to reach a new emotionally intelligent epoch of AI.

Recent increases in computing power, coupled with rapid growth in the availability and quantity of data have rekindled our interest in the theory and applications of artificial intelligence (AI). However, for AI to be confidently rolled out by industries and governments, users want greater transparency through explainable AI (XAI) systems. The author introduces XAI concepts, and gives an overview of areas in need of further exploration--such as type-2 fuzzy logic systems--to ensure such systems can be fully understood and analyzed by the lay user.

Future intelligent machines will be more human-friendly and human-like, while offering much higher throughput and automation, thus augmenting our (human) capabilities. Anthropomorphic machine learning is an emerging direction for future development in artificial intelligence (AI) and data science. This revolutionary shift offers human-like abilities to the next generation of machine learning with greater potential for underpinning breakthroughs in technology development as well as in various aspects of everyday life.

There are many frontiers in computing, each with its own unique profundity in the types of changes it brings forward. With AI, the changes we see will remind us that the playing field for humans and computers is not equal, and how this technology contributes to our lives will present both incredible opportunities as well as some troubling challenges. In this special issue, Computer's editor in chief introduces some of the emerging transformations the future of AI brings to us as humans.