Collaborating Authors

A 20-Year Community Roadmap for Artificial Intelligence Research in the US Artificial Intelligence

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.

DSAA 2016


Data driven scientific discovery is an important emerging paradigm for computing in areas including social, service, Internet of Things, sensor networks, telecommunications, biology, health-care and cloud. Under this paradigm, Data Science is the core that drives new researches in many areas, from environmental to social. There are many associated scientific challenges, ranging from data capture, creation, storage, search, sharing, modeling, analysis, and visualization. Among the complex aspects to be addressed we mention here the integration across heterogeneous, interdependent complex data resources for real-time decision making, streaming data, collaboration, and ultimately value co-creation. Data science encompasses the areas of data analytics, machine learning, statistics, optimization and managing big data, and has become essential to glean understanding from large data sets and convert data into actionable intelligence, be it data available to enterprises, Government or on the Web.

Tomorrow's Digital Transformation Battles Will Be Fought at the Edge


Gartner's recently released "Magic Quadrant for Industrial IoT Platforms" outlines how organizations can leverage the Internet of Things (IoT) to drive their digital transformation initiatives. In particular, Gartner believes that "By 2020, on-premises Internet of Things (IoT) platforms coupled with edge computing will account for up to 60% of industrial IoT (IIoT) analytics, up from less than 10% today."[1] More real-time sensor and device data coupled with more computational power are driving analytics "to the edge", which will yield new business and operational monetization opportunities for organizations looking to become more effective at leveraging data and analytics to power their business models (see Figure 1). IoT will be a significant Digital Transformation enabler – enabling new opportunities to integrate digital capabilities into the organization's assets, products and operational processes in order to improve efficiency, enhance customer value, mitigate risk, and uncover new monetization opportunities. IoT value creation occurs when the IoT Analytics collide with IoT Applications (like predictive maintenance, manufacturing performance optimization, waste reduction, reducing obsolete and excessive inventory, and first-time-fix) to deliver measurable sources of business and operational value (see Figure 2).