This article is published in the September 2019 issue.

A 20-Year Community Roadmap for AI Research in the US is Released

By: Mark Hill, Liz Bradley, and Ann Schwartz DrobnisIn: /

The Computing Community Consortium (CCC) is pleased to release the completed Artificial Intelligence (AI) Roadmap, titled A 20-Year Community Roadmap for AI Research in the US! An HTML version is available here.

This roadmap is the result of a year long effort by the CCC and over 100 members of the research community, led by Yolanda Gil (University of Southern California and President of AAAI) and Bart Selman (Cornell University and President Elect of AAAI). Comments on a draft report of this roadmap were requested in May 2019. Thank you to everyone in the community who participated in workshops, helped write the report, submitted comments, and edited drafts. Your input and expertise helped make this roadmap extremely comprehensive.

From the Roadmap:

Major Findings

I – Enabled by strong algorithmic foundations and propelled by the data and computational resources that have become available over the past decade, AI is poised to have profound positive impacts on society and the economy.

II – To realize the potential benefits of AI advances will require audacious AI research, along with new strategies, research models, and types of organizations for catalyzing and supporting it.

III – The needs and roles of academia and industry, and their interactions, have critically important implications for the future of AI.

IV – Talent and workforce issues are undergoing a sea change in AI, raising significant challenges for developing the talent pool and for ensuring adequate diversity in it.

V – The rapid deployment of AI-enabled systems is raising serious questions and societal challenges encompassing a broad range of capabilities and issues.

VI – Significant strategic investments in AI by the United States will catalyze major scientific, technological, societal, and economic progress.

 

 

Recommendations

I — Create and Operate a National AI Infrastructure to serve academia, industry, and government through four interlocking capabilities:

  • Open AI platforms and resources: a vast interlinked distributed collection of “AI-ready” resources (such as curated high quality datasets, software, knowledge repositories, testbeds for personal assistants and robotics environments) contributed by and available to the academic research community, as well as to industry and government.
  • Sustained community-driven AI challenges: organized sequences of challenges that build on one another, posed by AI and domain experts to drive research in key areas, building upon—and adding to—the shared resources in the Open AI Platforms and Facilities.
  • National AI Research Centers: multi-university centers with affiliated institutions, focused on pivotal areas of long-term AI research (e.g., integrated intelligence, trust, and responsibility), with decade-long funding to support on the order of 100 faculty, 200 AI engineers, 500 students, and necessary computing infrastructure. These centers would offer rich training for students at all levels. Visiting fellows from academia, industry, and government will enable cross-cutting research and technology transition.
  • Mission-Driven AI Laboratories: living laboratories for AI development in targeted areas of great potential for societal impact. These would be “AI-ready” facilities, designed to allow AI researchers to access unique data and expertise, such as AI-ready hospitals, AI-ready homes, or AI-ready schools. They would work closely with the National AI Research Centers to provide requirements, facilitate applied research, and transition research results. These laboratories would be crucial for R&D, dissemination, and workforce training. They would have decade-long funding to support on the order of 50 permanent AI researchers, 50 visitors from AI Research Centers, 100-200 AI engineers and technicians, and 100 domain experts and staff.

 

II — Re-conceptualize and Train an All-Encompassing AI Workforce, building upon the National AI Infrastructure listed above to:

  •  Develop AI Curricula at All Levels: guidelines should be developed for curricula that encourage early and ongoing interest in and understanding of AI, beginning in K-12 and extending through graduate courses and professional programs.
  • Create Recruitment and Retention Programs for Advanced AI Degrees: including grants for talented students to obtain advanced graduate degrees, retention programs for doctoral-level researchers, and additional resources to support and enfranchise AI teaching faculty.
  • Engage Underrepresented and Underprivileged Groups: programs to bring the best talent into the AI research effort.
  • Incentivize Emerging Interdisciplinary AI Areas: initiatives to encourage students and the research community to work in interdisciplinary AI studies—e.g., AI safety engineering, as well as analysis of the impact of AI on society—will ensure a workforce and a research ecosystem that understands the full context for AI solutions.
  • Highlight AI Ethics and Policy: including the importance of the area of AI ethics and policy, and the imperative of incorporating ethics and related responsibility principles as central elements in the design and operation of AI systems.
  • Address AI and the Future of Work: these challenges are at the intersection of AI with other disciplines such as economics, public policy, and education. It is important to teach students how to think through the ethical and social implications of their work.
  • Train Highly Skilled AI Engineers and Technicians: support and build upon the National AI Infrastructure to grow the AI pipeline through community colleges, workforce retraining programs, certificate programs, and online degrees.

 

III — Core Programs for basic AI Research are critical. The new resources and initiatives described in this Roadmap cannot come at the expense of existing programs for funding AI research. These core programs—which provide well-established, broadbased support for research progress, for training young researchers, for integrating AI research and education, and for nucleating novel interdisciplinary collaborations—are critical complements to the broader initiatives described in this Roadmap, and they too will require expanded support.

All of this will require substantial, sustained federal investment over the course of the 20-year period covered by this Roadmap, but the outcomes will be transformative. The recommendations above are not only a scaffold for interdisciplinary, forward-looking R&D that will drive scientific and economic advances while taking into consideration issues around security, vulnerability, policy, and ethics. The recommendations in this Roadmap will also allow the retention of the best talent in fertile research settings, creating extensive human capital in this crucial technology area—another important benefit to society and the economy.

 

Thank you again to the community for your input. Please see the full Roadmap here. If you have any questions or want to help share the Roadmap, please email the CCC Director, Ann Schwartz Drobnis (adrobnis@cra.org).