White House Frontiers Conference Panel on the Future of Health and Medical Research. Photo by Ann Drobnis.
This blog post was co-authored by CCC Staff, Greg Hager, Computing Community Consortium (CCC) Past Chair and Professor in the Department of Computer Science at Johns Hopkins University, and Beth Mynatt, CCC Chair, Professor and Director of Georgia Tech’s Institute for People and Technology.
Last week the President hosted the White House Frontiers Conference in Pittsburgh, an event that was co-hosted by the University of Pittsburgh and Carnegie Mellon University and attended by hundreds of scientific leaders in our community.
The Computing Community Consortium (CCC) Chair and Director attended the event, which had many speakers from previous CCC events.
The Frontiers Conference presentations and panel discussions were inspiring and thought provoking. I came away impressed with the scale of the scientific visions described and am doubly committed to working on the tough challenges that face our community. – Beth Mynatt, CCC Chair
You can see the President’s remarks and panel discussion on the future of health and medical research here.
This event was coordinated with the release of two reports. In an earlier post, we highlighted a report released by the White House on the future directions and considerations for AI called Preparing for the Future of Artificial Intelligence.
Although it received less press, a companion report, the National Artificial Intelligence Research and Development Strategic Plan, was also released last week. This report was drafted by the National Science and Technology Council Networking and Information Technology Research and Development (NITRD) Task Force on Artificial Intelligence and lays out a strategic plan for Federally-funded research and development in AI. The goal is “to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts.”
To achieve this goal, this AI R&D Strategic Plan identifies the following priorities for Federally-funded AI research (some of which also appear in the White House release):
Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI.
Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems.
Strategy 3: Understand and address the ethical, legal, and societal implications of AI. We expect AI technologies to behave according to the formal and informal norms to which we hold our fellow humans. Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals.
Strategy 4: Ensure the safety and security of AI systems. Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely, in a controlled, well-defined, and well-understood manner. Further progress in research is needed to address this challenge of creating AI systems that are reliable, dependable, and trustworthy.
Strategy 5: Develop shared public datasets and environments for AI training and testing. The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources.
Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and evaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques.
Strategy 7: Better understand the national AI R&D workforce needs. Advances in AI will require a strong community of AI researchers. An improved understanding of current and future R&D workforce demands in AI is needed to help ensure that sufficient AI experts are available to address the strategic R&D areas outlined in this plan.
The AI R&D Strategic Plan closes with two recommendations:
Recommendation 1: Develop an AI R&D implementation framework to identify S&T opportunities and support effective coordination of AI R&D investments, consistent with Strategies 1-6 of this plan.
Recommendation 2: Study the national landscape for creating and sustaining a healthy AI R&D workforce, consistent with Strategy 7 of this plan.
See the full National Artificial Intelligence Research and Development Strategic Plan to learn more.
