Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and
economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and
communicate. AI research can further our national priorities, including increased economic prosperity,
improved educational opportunities and quality of life, and enhanced national and homeland security.
Because of these potential benefits, the U.S. government has invested in AI research for many years.
Yet, as with any significant technology in which the Federal government has interest, there are not only
tremendous opportunities but also a number of considerations that must be taken into account in
guiding the overall direction of Federally-funded R&D in AI.
On May 3, 2016,the Administration announced the formation of a new NSTC Subcommittee on Machine
Learning and Artificial intelligence, to help coordinate Federal activity in AI.1
This Subcommittee, on June
15, 2016, directed the Subcommittee on Networking and Information Technology Research and
Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic
Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic
priorities for AI R&D, with particular attention on areas that industry is unlikely to address.
This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federallyfunded
AI research, both research occurring within the government as well as Federally-funded research
occurring outside of government, such as in academia. The ultimate goal of this research 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:
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
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.
View Full Text: https://www.nitrd.gov/PUBS/national_ai_rd_strategic_plan.pdf