Tag Archive: 2019 Visioning Activities

Misinformation Roundtable

This roundtable will bring together computer scientists along with experts from disciplines potentially to include electrical engineering, psychology, marketing, information science, and political science to discuss challenges in detecting and countering misinformation.

AI Roadmap

In fall 2018, the Computing Community Consortium (CCC) started a new initiative to create a Roadmap for Artificial Intelligence, led by Yolanda Gil (University of Southern California and President-Elect of AAAI) and Bart Selman (Cornell University). A series of three workshops were held in the Fall/Winter of 2018/2019, which resulted in a Roadmap produced in the Spring of 2019. The goal of the initiative was to identify challenges, opportunities, and pitfalls, and create a compelling report that will effectively inform future federal priorities—including future AI R&D Investments. An Executive Summary of the Roadmap is now available.
Fairness and Accountability Task Force will hold a visioning workshop on Economics and Fairness, May 22-23, 2019 in Cambridge, Massachusetts. This workshop will bring together computer science researchers with backgrounds in algorithmic decision making, machine learning, and data science with policy makers, legal experts, economists, and business leaders to discuss methods to ensure economic fairness in a data-driven world. '>

Economics and Fairness

The Computing Community Consortium's (CCC) Fairness and Accountability Task Force will hold a visioning workshop on Economics and Fairness, May 22-23, 2019 in Cambridge, Massachusetts. This workshop will bring together computer science researchers with backgrounds in algorithmic decision making, machine learning, and data science with policy makers, legal experts, economists, and business leaders to discuss methods to ensure economic fairness in a data-driven world.

Artificial Intelligence Roadmap Workshop 3 – Self Aware Learning

Given the increasingly pervasive use in AI technologies in all sectors of industry and government and the enormous potential for future AI-based technologies, NSF has asked the Computing Community Consortium to organize an AI Roadmap to help prioritize research investments. The third workshop theme is Learning and Robotics and will take place on January 17-18, 2019 in San Francisco. The chairs of the Self Aware Learning workshop are Fei-Fei Li (Stanford University) and Thomas G. Dietterich (Oregon State University). This is part of the AI Roadmap workshop series – view the series page here.

Artificial Intelligence Roadmap Workshop 2 – Interaction

Given the increasingly pervasive use in AI technologies in all sectors of industry and government, and the enormous potential for future AI-based technologies, NSF has asked the Computing Community Consortium to organize an AI Roadmap to help prioritize research investments. The second workshop theme is interaction and will take place on November 14-15, 2018 in Chicago. The chairs of the interaction workshop are Kathy McKeown (Columbia University) and Dan Weld (University of Washington). This is part of the AI Roadmap workshop series – view the series page here.

Content Generation for Workforce Training

The CCC held a visioning workshop in Atlanta, GA in March 2019 to discuss and articulate research visions for authoring rich graphical content for new workforce training. The workshop's goal was to articulate research challenges and needs and to summarize the current state of the practice in this area.

Thermodynamic Computing

Thermodynamics has been a historical concern in the engineering of conventional computing systems due to its role in power consumption, scaling, and device performance. Today, we see thermodynamics re-emerging in a new role as an algorithmic technique in areas such as machine learning, annealing, quantum, and neuromorphic systems. Recent theoretical developments in non-equilibrium thermodynamics suggest thermodynamics may become the basis of a new “thermodynamic computing” paradigm. For example, it may lead to computing systems that self-organize in response to external input.