AI Research Roadmap – Request for Comments
The Computing Community Consortium (CCC) requests comments by May 28, 2019 on a draft of A 20-Year Community Roadmap for AI Research in the US. See the links and logistics at the end of this page to submit feedback.
This draft arises from a community process that has already involved more than one hundred professionals. In Fall 2018, the Computing Community Consortium (CCC) started an initiative to create a Roadmap for Artificial Intelligence to be led by Yolanda Gil (University of Southern California and President of AAAI) and Bart Selman (Cornell University and President Elect of AAAI). A series of three workshops were held in Winter 2019, followed by a Community Town Hall at AAAI 2019. The goal of the initiative is to identify challenges, opportunities, and pitfalls, and create a compelling report that will effectively inform future federal priorities—including future AI R&D Investments, as was prioritized recently by the White House in President Trump’s executive order on Maintaining American Leadership in Artificial Intelligence and the National Science Foundation’s (NSF) Statement on the executive order to maintain American leadership in artificial intelligence.
University of Southern California / President of AAAI
Cornell University / President Elect of AAAI
The 20-year AI Research Roadmap that was produced by this community effort includes the following specific recommendations:
I — Create and Operate a National AI Infrastructure to serve academia, industry, and government through four interlocking capabilities:
a) Open AI platforms and resources: a vast interlinked distributed collection of “AI-ready” resources (curated high-quality datasets, software libraries, knowledge repositories, instrumented homes and hospitals, robotics environments, cloud-scale computing services, etc.) contributed by and available to the academic research community, as well as to industry and government. Recent major innovations from companies demonstrate that AI breakthroughs require large-scale hardware investments and open-source software infrastructures, both of which require substantial ongoing investments.
b) Sustained community-driven AI challenges: organizational structures that coordinate the formulation of grand-challenge problems 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.
c) National AI Research Centers: physical and virtual facilities that bring together Faculty Fellows from a range of academic institutions and Industry Fellows from industry and government in multi-year funded projects focused on pivotal areas of long-term AI research.
d) Mission-Driven AI Laboratories: living laboratories that provide sustained infrastructure, facilities, and human resources to support the Open AI Platforms and the AI Challenges, and work closely with the National AI Research Centers to integrate results to address critical AI challenges in vertical sectors of public interest such as health, education, policy, ethics, and science.
II — Re-conceptualize and Train an All-Encompassing AI Workforce, building upon the elements of the National AI Infrastructure listed above to create:
a) Development of 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.
b) 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.
c) Engaging Underrepresented and Underprivileged Groups: programs to bring the best talent into the AI research effort.
d) Incentivizing Emerging Interdisciplinary AI Areas: initiatives to encourage students and the research community to work in interdisciplinary AI studies—e.g., AI-related policy and law, 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.
e) Training Highly Skilled AI Engineers and Technicians, to support and build upon the Open AI Platform to grow the AI pipeline through community colleges, workforce retraining programs, certificate programs, and online degrees.
III – Core Programs for AI Research are critical. These new resources and initiatives cannot come at the expense of existing programs for funding theoretical and applied AI. These core programs—which provide well-established, broad-based 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.
Here is a link to the whole report (117 pages).
Here are links to individual sections:
Title Page, Executive Summary, and Table of Contents (7 pages)
- Introduction (5 pages)
- Major Societal Drivers for Future Artificial Intelligence Research (4 pages)
- Overview of Core Technical Areas of AI Research Roadmap: Workshop Reports (72 pages)
- Major Findings (4 pages)
- Recommendations (22 pages)
- Conclusions (1 page)
- Appendices (participants and contributors) (2 pages)