IEEE Computer


Automated Driving: The Cyber-Physical Perspective

IEEE Computer

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: A Disruptive Integration

IEEE Computer

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?


The Age of Artificial Emotional Intelligence

IEEE Computer

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.


Toward Human-Understandable, Explainable AI

IEEE Computer

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.


Toward Anthropomorphic Machine Learning

IEEE Computer

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.


The Expanding Frontier of Artificial Intelligence

IEEE Computer

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.


Social Media–based Conversational Agents for Health Management and Interventions

IEEE Computer

Conversational agents could provide timely and cost-effective social support to promote behavioral changes and improve healthcare outcomes. The authors evaluated the performance of their social media-based conversational agent in a smoking cessation program. Results showed that the presence of a conversational agent effectively increased participant engagement and enhanced their smoking cessation outcomes.


Machine Learning and Manycore Systems Design: A Serendipitous Symbiosis

IEEE Computer

Tight collaboration between manycore system designers and machine-learning experts is necessary to create a data-driven manycore design framework that integrates both learning and expert knowledge. Such a framework will be necessary to address the rising complexity of designing large-scale manycore systems and machine-learning techniques.


Standards: Roadmapping Computer Technology Trends Enlightens Industry

IEEE Computer

The IEEE Standards Association's Industry Connection program, with its International Roadmap for Devices and Systems (IRDS), takes over the Semiconductor Industry Association's International Technology Roadmap for Semiconductors (ITRS).


Understanding Social Networks Using Transfer Learning

IEEE Computer

Akin to human transfer of experiences, transfer learning as a subfield of machine learning adapts knowledge acquired in one domain to a new domain. The authors systematically investigate how this concept might be applied to the study of users on emerging Web platforms, proposing a transfer learning–based approach, TraNet.