THE NATIONALARTIFICIAL INTELLIGENCERESEARCH AND DEVELOPMENTSTRATEGIC PLAN: 2019 UPDATE

A Report by the
SELECT COMMITTEE ON ARTIFICIAL INTELLIGENCEof theNATIONAL SCIENCE & TECHNOLOGY COUNCIL

JUNE 2019

Dear Colleagues,
In his State of the Union address on February 5, 2019, President Trump stressed the importance ofensuring American leadership in the development of emerging technologies, including artificial
intelligence (AI), that make up the Industries of the Future. Reflecting this importance, on February 11,
2019, President Trump signed Executive Order 13859, which established the American Artificial
Intelligence Initiative. This Initiative is a whole-of-government approach for maintaining American
leadership in AI and ensuring that AI benefits the American people and reflects our Nation’s values. The
first directive in this Executive Order is for Federal agencies to prioritize AI research and development(R&D) in their annual budgeting and planning process. The attached National AI R&D Strategic Plan:
2019 Update highlights the key priorities for Federal investment in AI R&D.
Artificial intelligence presents tremendous opportunities that are leading to breakthroughs in
improved healthcare, safer and more efficient transportation, personalized education, significant
scientific discoveries, improved manufacturing, increased agricultural crop yields, better weather
forecasting, and much more. These benefits are largely due to decades of long-term Federal
investments in fundamental AI R&D, which have led to new theories and approaches for AI systems, aswell as applied research that allows the translation of AI into practical applications.
The landscape for AI R&D is becoming increasingly complex, due to the significant investments that are
being made by industry, academia, and nonprofit organizations. Additionally, AI advancements areprogressing rapidly. The Federal Government must therefore continually reevaluate its priorities for AI
R&D investments, to ensure that investments continue to advance the cutting edge of the field and are
not unnecessarily duplicative of industry investments.
In August of 2018, the Administration directed the Select Committee on AI to refresh the 2016 National
AI R&D Strategic Plan. This process began with the issuance of a Request for Information to solicit public
input on ways that the strategy should be revised or improved. The responses to this RFI, as well as anindependent agency review, informed this update to the Strategic Plan.
In this Strategic Plan, eight strategic priorities have been identified. The first seven strategies continue
from the 2016 Plan, reflecting the reaffirmation of the importance of these strategies by multiple
respondents from the public and government, with no calls to remove any of the strategies. The eighth
strategy is new and focuses on the increasing importance of effective partnerships between the Federal
Government and academia, industry, other non-Federal entities, and international allies to generatetechnological breakthroughs in AI and to rapidly transition those breakthroughs into capabilities.
While this Plan does not define specific research agendas for Federal agency investments, it does
provide an expectation for the overall portfolio for Federal AI R&D investments. This coordinatedFederal strategy for AI R&D will help the United States continue to lead the world in cutting-edge
advances in AI that will grow our economy, increase our national security, and improve quality of life.
Sincerely,

Michael Kratsios
Deputy Assistant to the President for Technology Policy
June 21, 2019

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

Table of Contents
Executive Summary ........................................................................................................................... iii
Introduction to the 2019 National AI R&D Strategic Plan ........................................................................ 1
AI R&D Strategy .................................................................................................................................. 5
Strategy 1: Make Long-Term Investments in AI Research ........................................................................... 72019 Update: Sustaining long-term investments in fundamental AI research................................................... 7
Advancing data-focused methodologies for knowledge discovery ................................................................ 9
Enhancing the perceptual capabilities of AI systems....................................................................................... 9
Understanding theoretical capabilities and limitations of AI ........................................................................ 10
Pursuing research on general-purpose artificial intelligence........................................................................ 10
Developing scalable AI systems ...................................................................................................................... 11
Fostering research on human-like AI .............................................................................................................. 11
Developing more capable and reliable robots ............................................................................................... 11
Advancing hardware for improved AI ............................................................................................................. 12Creating AI for improved hardware ................................................................................................................ 12
Strategy 2: Develop Effective Methods for Human-AI Collaboration ........................................................ 142019 Update: Developing AI systems that complement and augment human capabilities,with increasing
focus on the future of work .................................................................................................................................14
Seeking new algorithms for human-aware AI ................................................................................................ 17
Developing AI techniques for human augmentation ..................................................................................... 17
Developing techniques for visualization and human-AI interfaces ............................................................... 18
Developing more effective language processing systems ............................................................................. 18
Strategy 3: Understand and Address the Ethical, Legal, and Societal Implications of AI ........................... 192019 Update: Addressing ethical, legal, and societal considerations in AI ...................................................... 19
Improving fairness, transparency, and accountability by design ................................................................. 21
Building ethical AI ............................................................................................................................................ 21
Designing architectures for ethical AI ............................................................................................................. 21
Strategy 4: Ensure the Safety and Security of AI Systems ........................................................................ 232019 Update: Creating robust and trustworthy AI systems .............................................................................. 23
Improving explainability and transparency ................................................................................................... 25
Building trust ................................................................................................................................................... 25
Enhancing verification and validation ............................................................................................................ 25
Securing against attacks ................................................................................................................................. 26
Achieving long-term AI safety and value-alignment ...................................................................................... 26
Strategy 5: Develop Shared Public Datasets and Environments for AI Training and Testing ..................... 272019 Update: Increasing access to datasets and associated challenges ........................................................ 27
Developing and making accessible a wide variety of datasets to meet the needs of a diverse spectrumof AI interests and applications ...................................................................................................................... 29
Making training and testing resources responsive to commercial and public interests .............................. 30
Developing open-source software libraries and toolkits ............................................................................... 30
Strategy 6: Measure and Evaluate AI Technologies through Standards and Benchmarks ......................... 32
2019 Update: Supporting development of AI technical standards and related tools ...................................... 32
Developing a broad spectrum of AI standards ............................................................................................... 33
Establishing AI technology benchmarks ........................................................................................................ 34
Increasing the availability of AI testbeds ........................................................................................................ 34
Engaging the AI community in standards and benchmarks .......................................................................... 35
Strategy 7: Better Understand the National AI R&D Workforce Needs ...................................................... 37
2019 Update: Advancing the AI R&D workforce, including those working on AI systems and those working
alongside them, to sustain U.S. leadership ...................................................................................................... 37
Strategy 8: Expand Public–Private Partnerships to Accelerate Advances in AI ............................................ 40
Abbreviations ................................................................................................................................... 43– ii –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

Executive Summary
Artificial intelligence (AI) holds tremendous promise to benefit nearly all aspects of society, including
the economy, healthcare, security, the law, transportation, even technology itself. On February 11,
2019, the President signed Executive Order 13859, Maintaining American Leadership in Artificial
Intelligence. 1 This order launched the American AI Initiative, a concerted effort to promote and protect
AI technology and innovation in the United States. The Initiative implements a whole-of-government
strategy in collaboration and engagement with the private sector, academia, the public, and likeminded international partners. Among other actions, key directives in the Initiative call for Federal
agencies to prioritize AI research and development (R&D) investments, enhance access to high-quality
cyberinfrastructure and data, ensure that the Nation leads in the development of technical standards
for AI, and provide education and training opportunities to prepare the American workforce for the new
era of AI.
In support of the American AI Initiative, this National AI R&D Strategic Plan: 2019 Update defines thepriority areas for Federal investments in AI R&D. This 2019 update builds upon the first National AI R&D
Strategic Plan released in 2016, accounting for new research, technical innovations, and otherconsiderations that have emerged over the past three years. This update has been developed byleading AI researchers and research administrators from across the Federal Government, with input
from the broader civil society, including from many of America’s leading academic research
institutions, nonprofit organizations, and private sector technology companies. Feedback from these
key stakeholders affirmed the continued relevance of each part of the 2016 Strategic Plan while also
calling for greater attention to making AI trustworthy, to partnering with the private sector, and otherimperatives.
The National AI R&D Strategic Plan: 2019 Update establishes a set of objectives for Federally funded AI
research, identifying the following eight strategic priorities:
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. Increase understanding of how tocreate AI systems that effectively complement and augment human capabilities.
Strategy 3: Understand and address the ethical, legal, and societal implications of AI. Research AI
systems that incorporate ethical, legal, and societal concerns through technical mechanisms.
Strategy 4: Ensure the safety and security of AI systems. Advance knowledge of how to design AI systems
that are reliable, dependable, safe, and trustworthy.
Strategy 5: Develop shared public datasets and environments for AI training and testing. Develop andenable access to high-quality datasets and environments, as well as to testing and training resources.
Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. Develop a broad
spectrum of evaluative techniques for AI, including technical standards and benchmarks.
Strategy 7: Better understand the national AI R&D workforce needs. Improve opportunities for R&D
workforce development to strategically foster an AI-ready workforce.
Strategy 8: Expand public-private partnerships to accelerate advances in AI. Promote opportunities forsustained investment in AI R&D and for transitioning advances into practical capabilities, incollaboration with academia, industry, international partners, and other non-Federal entities.
1

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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEIntroduction to the 2019 National AI R&D Strategic Plan
Artificial intelligence enables computers and other automated systems to perform tasks that havehistorically required human cognition and what we typically consider human decision-making abilities.
Over the past several decades, AI has advanced tremendously and today promises better, moreaccurate healthcare; enhanced national security; improved transportation; and more effective
education, to name just a few benefits. Increased computing power, the availability of large datasets
and streaming data, and algorithmic advances in machine learning (ML) have made it possible for AI
development to create new sectors of the economy and revitalize industries. As more industries adopt
AI’s fundamental technologies, the field will continue to drive profound economic impact and qualityof-life improvements worldwide.
These advancements have been driven
primarily by Federal investments in AI R&D,
the expertise of America’s unsurpassed R&D
institutions, and the collective creativity ofmany of America’s most visionary
technology companies and entrepreneurs.
In 2016 the Federal Government publishedfirst National AI R&D Strategic Plan,
recognizing AI’s tremendous promise andneed for continued advancement. It wasdeveloped to guide the Nation in our AI R&D
investments, provide a strategic framework
for improving and leveraging America’s AI
capabilities, and ensure that thosecapabilities produce prosperity, security,and improved quality of life for theAmerican people for years to come.
The Plan defined several key areas ofpriority focus for the Federal agencies thatinvest in AI. These focus areas, or strategies,
include: continued long-term investments
in AI; effective methods for human-AI
collaboration; understanding and addressing
the ethical, legal, and societal implications
for AI; ensuring the safety and security of AI;
developing shared public datasets andenvironments for AI training and testing;measuring and evaluating AI technologies
through standards and benchmarks; andbetter understanding the Nation’s AI R&D

2
3

2019
Update

RFI responses inform the2019 National AI R&D Strategic Plan

In September 2018, the National Coordination Office for
Networking and Information Technology Research andDevelopment issued a Request for Information (RFI) 2 onbehalf of the Select Committee on Artificial Intelligence,
requesting input from all interested parties on the 2016
National
Artificial
Intelligence ResearchandDevelopment Strategic Plan. Nearly 50 responses were
submitted by researchers, research organizations,professional societies, civil society organizations, andindividuals; these responses are available online. 3
Many of the responses reaffirmed the analysis,organization, and approach outlined in the 2016
National AI R&D Strategic Plan. A significant number of
responses noted the importance of investing in theapplication of AI in areas such as manufacturing andsupply chains; healthcare; medical imaging;meteorology, hydrology, climatology, and relatedareas; cybersecurity; education; data-intensive physicalsciences such as high-energy physics; andtransportation. This interest in translational
applications of AI technologies has certainly increased
since the release of the 2016 National AI R&D Strategic
Plan. Other common themes echoed in the RFI
responses were the importance of developing
trustworthy AI systems, including fairness, ethics,
accountability, and transparency of AI systems; curated
and accessible datasets; workforce considerations; andpublic-private partnerships for furthering AI R&D.

https://www.nitrd.gov/news/RFI-National-AI-Strategic-Plan.aspx
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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

workforce needs. That work was prescient: today, countries around the world have followed suit andhave issued their own versions of this plan.
In the three years since the National AI R&D Strategic Plan was produced, new research, technicalinnovations, and real-world deployments have progressed rapidly. The Administration initiated this2019 update to the National AI R&D Strategic Plan to address these advancements, including a rapidly
evolving international AI landscape.
Notably, this 2019 Update to the National AI R&D Strategic Plan is, by design, solely concerned withaddressing the research and development priorities associated with advancing AI technologies. It does
not describe or recommend policy or regulatory actions related to the governance or deployment of AI,
although AI R&D will certainly inform the development of reasonable policy and regulatory frameworks.

AI as an Administration Priority
Since 2017, the Administration has addressed the importance of AI R&D by emphasizing its role for
America’s future across multiple major policy documents, including the National Security Strategy, 4 theNational Defense Strategy, 5 and the FY 2020 R&D Budget Priorities Memo. 6
In May 2018, the Office of Science and Technology Policy (OSTP) hosted the White House Summit on
Artificial Intelligence for American Industry to begin discussing the promise of AI and the policies
needed to realize that promise for the American people and maintain U.S. leadership in the age of AI.
The Summit convened over 100 senior government officials, technical experts from top academic
institutions, heads of industrial research laboratories, and American business leaders.
In his State of the Union address on February 5, 2019, President Trump stressed the importance ofensuring American leadership in the development of emerging technologies, including AI, that make up
the Industries of the Future.
On February 11, 2019, the President signed Executive Order 13859, Maintaining American Leadership in
Artificial Intelligence. 7 This order launched the American AI Initiative, a concerted effort to promote andprotect AI technology and innovation in the United States. The Initiative implements a whole-ofgovernment strategy in collaboration and engagement with the private sector, academia, the public,
and like-minded international partners. Among other actions, key directives in the Initiative call for
Federal agencies to prioritize AI R&D investments, enhance access to high-quality cyberinfrastructureand data, ensure that the Nation leads in the development of technical standards for AI, and provide
education and training opportunities to prepare the American workforce for the new era of AI.

Development of the 2019 Update to the National AI R&D Strategic Plan
The 2016 National AI R&D Strategic Plan recommended that the many Federal agencies tasked withadvancing or adopting AI collaborate to identify critical R&D opportunities and support effective
coordination of Federal AI R&D activities, both intramural and extramural research. Reflecting theAdministration’s prioritization of AI, the National Science and Technology Council (NSTC) has
established a new framework to implement this recommendation, consisting of three unique NSTC
subgroups made up of members from across the Federal R&D agencies to cover (1) senior leadership4567

https://www.whitehouse.gov/wp-content/uploads/2017/12/NSS-Final-12-18-2017-0905.pdf
https://dod.defense.gov/Portals/1/Documents/pubs/2018-National-Defense-Strategy-Summary.pdf
https://www.whitehouse.gov/wp-content/uploads/2018/07/M-18-22.pdf
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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

and strategic vision, (2) operational planning and tactical implementation, and (3) research andtechnical expertise. These subgroups are:

• The Select Committee on AI, 8 consisting of the heads of departments and agencies principally

responsible for the government’s AI R&D, advises the Administration on interagency AI R&Dpriorities; considers the creation of Federal partnerships with industry and academia; establishes
structures to improve government planning and coordination of AI R&D; identifies opportunities
to leverage Federal data and computational resources to support our national AI R&D ecosystem;and supports the growth of a technical, national AI workforce.
• The NSTC Subcommittee on Machine Learning and Artificial Intelligence (MLAI), consisting ofagency AI leaders and administrators, serves as the operational and implementation arm of theSelect Committee, responsible for fulfilling tasking from the Select Committee; creating andmaintaining the National AI R&D Strategic Plan; identifying and addressing important policy issues
related to AI research, testing, standards, education, implementation, outreach, and relatedareas; and related activities.
• The AI R&D Interagency Working Group, operating under the NSTC’s Networking and Information
Technology R&D (NITRD) Subcommittee and consisting of research program managers andtechnical experts from across the Federal Government, reports to the MLAI Subcommittee; helps
coordinate interagency AI R&D programmatic efforts; serves as the interagency AI R&D communityof practice; and reports government-wide AI R&D spending through the NITRD Subcommittee’s
annual Supplement to the President’s Budget.
In September 2018, the Select Committee initiated an update to the 2016 Strategic Plan, beginning withan RFI seeking broad community input on whether and how the seven strategies of the 2016 National AI
R&D Strategic Plan merited revision or replacement (see sidebar). Independently, Federal departments
and agencies performing or funding AI R&D undertook their own assessments.

An Overview of the 2019 Update to the 2016 National AI R&D Strategic Plan
Together, the Select Committee on AI, the NSTC Subcommittee on Machine Learning and AI, and the AI
R&D Interagency Working Group of NITRD reviewed the input regarding the National AI R&D Strategic
Plan. Each of the original seven focus areas or strategies of the 2016 Plan was reaffirmed by multiple
respondents from the public and government, with no calls to remove any one strategy. These
strategies, updated in this 2019 Update to the Strategic Plan to reflect the current state of the art, are:Strategy 1: Make long-term investments in AI research;
Strategy 2: Develop effective methods for human-AI collaboration;
Strategy 3: Understand and address the ethical, legal, and societal implications of AI;
Strategy 4: Ensure the safety and security of AI systems;
Strategy 5: Develop shared public datasets and environments for AI training and testing;
Strategy 6: Measure and evaluate AI technologies through standards and benchmarks; andStrategy 7: Better understand the national AI R&D workforce needs.

8

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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

Many responses to the RFI called for greater Federal Government R&D engagement with the private
sector, given the fast rise of privately funded AI R&D, and the rapid adoption of AI by industry. As a result,the 2019 Update incorporates a new, eighth strategy:
Strategy 8: Expand public-private partnerships to accelerate advances in AI.
Feedback from the public and Federal agencies identified a number of specific challenges to further AI
development and adoption. These challenges, many of which cut across multiple agencies, provide
enhanced insight into ways that this National AI R&D Strategic Plan can guide the course of AI R&D in
America, and many closely relate to the themes addressed in the 2019 Executive Order on Maintaining
American Leadership in Artificial Intelligence. Examples include the following:• Research at the frontiers. Even though machine learning has brought phenomenal new
•
•
•
•
•
•
•

capabilities in the past several years, continued research is needed to further push the frontiers ofML, as well as to develop additional approaches to the tough technical challenges of AI (Strategy 1).
Positive impact. As AI capabilities grow, the United States must place increased emphasis on
developing new methods to ensure that AI’s impacts are robustly positive into the future(Strategies 1, 3, and 4).
Trust and explainability. Truly trustworthy AI requires explainable AI, especially as AI systems growin scale and complexity; this requires a comprehensive understanding of the AI system by thehuman user and the human designer (Strategies 1, 2, 3, 4, and 6).
Safety and security. Researchers must devise methods to keep AI systems and the data they use
secure so that the Nation can leverage the opportunities afforded by this technology while also
maintaining confidentiality and safety (Strategies 4, 5, and 6).
Technical standards. As the Nation develops techniques to expand both AI abilities and assurance,
it must test and benchmark them; when the techniques are ready, they should be turned into
technical standards for the world (Strategy 6).
Workforce capability. Accomplishing these goals will require growing a skilled AI R&D workforce thatis currently limited and in high demand; the United States must be creative and bold in training andacquiring the skilled workforce it needs to lead the world in AI research and applications (Strategy 7).
Partnerships. Advances in AI R&D increasingly require effective partnerships between the Federal
Government and academia, industry, and other non-Federal entities to generate technologicalbreakthroughs in AI and to rapidly transition those breakthroughs into capabilities (Strategy 8).
Cooperation with allies. Additionally, the Plan recognizes the importance of international
cooperation for successful implementation of these goals, while protecting the American AI R&D
enterprise from strategic competitors and adversarial nations.

Structure of this 2019 Update to the 2016 National AI R&D Strategic Plan
This updated National AI R&D Strategic Plan incorporates the original text from the 2016 version,
including the following section on R&D Strategy (except for minor edits) and the original 2016 wordingof the first seven strategies. For each strategy, 2019 updates to the 2016 National R&D Strategic Plan areprovided in shaded boxes at the top of the original seven strategies; these highlight updated imperatives
and/or new focus areas for the strategies. Text below the shaded boxes is as it originally appeared in the2016 National AI R&D Strategic Plan, providing observations and context that remain important today(note that some of the original details may have become out of date in the intervening period). In
addition, as noted previously, a new eighth strategy is added in this 2019 Update, on expanding publicprivate partnerships in AI R&D.

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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEAI R&D Strategy
The research priorities outlined in this AI R&D Strategic Plan focus on areas that industry is unlikely toaddress on their own, and thus, areas that are most likely to benefit from Federal investment. Thesepriorities cut across all of AI to include needs common to the AI sub-fields of perception, automatedreasoning/planning, cognitive systems, machine learning, natural language processing, robotics, andrelated fields. Because of the breadth of AI, these priorities span the entire field, rather than only
focusing on individual research challenges specific to each sub-domain. To implement the plan,detailed roadmaps should be developed that address the capability gaps consistent with the plan.
One of the most important Federal research priorities, outlined in Strategy 1, is for sustained long-term
research in AI to drive discovery and insight. Many of the investments by the U.S. Federal Government
in high-risk, high-reward 9 fundamental research have led to revolutionary technological advances wedepend on today, including the Internet, GPS, smartphone speech recognition, heart monitors, solar
panels, advanced batteries, cancer therapies, and much, much more. The promise of AI touches nearly
every aspect of society and has the potential for significant positive societal and economic benefits.
Thus, to maintain a world leadership position in this area, the United States must focus its investments
on high-priority fundamental and long-term AI research.
Many AI technologies will work with and alongside humans, thus leading to important challenges in
how to best create AI systems that work with people in intuitive and helpful ways. 10 The walls between
humans and AI systems are slowly beginning to erode, with AI systems augmenting and enhancing
human capabilities. Fundamental research is needed to develop effective methods for human-AI
interaction and collaboration, as outlined in Strategy 2.
AI advancements are providing many positive benefits to society and are increasing U.S. national
competitiveness. 11 However, as with most transformative technologies, AI presents some societal risks
in several areas, from jobs and the economy to safety, ethical, and legal questions. Thus, as AI scienceand technology develop, the Federal Government must also invest in research to better understandwhat the implications are for AI for all these realms, and to address these implications by developing AI
systems that align with ethical, legal, and societal goals, as outlined in Strategy 3.
A critical gap in current AI technology is a lack of methodologies to ensure the safety and predictableperformance of AI systems. Ensuring the safety of AI systems is a challenge because of the unusualcomplexity and evolving nature of these systems. Several research priorities address this safety
challenge. First, Strategy 4 emphasizes the need for explainable and transparent systems that are
trusted by their users, perform in a manner that is acceptable to the users, and can be guaranteed toact as the user intended. The potential capabilities and complexity of AI systems, combined with thewealth of possible interactions with human users and the environment, makes it critically important toinvest in research that increases the security and control of AI technologies. Strategy 5 calls on theFederal Government to invest in shared public datasets for AI training and testing to advance theprogress of AI research and to enable a more effective comparison of alternative solutions.
Strategy 6 discusses how standards and benchmarks can focus R&D to define progress, close gaps, anddrive innovative solutions for specific problems and challenges. Standards and benchmarks are“High-risk, high-reward” research refers to visionary research that is intellectually challenging but has the potential
to make deeply positive, transformative impacts on the field of study.
10
See 2016 Report of the One Hundred Year Study on Artificial Intelligence, which focuses on the anticipated uses andimpacts of AI in the year 2030; https://ai100.stanford.edu/2016-report.
11
J. Furman, “Is This Time Different? The Opportunities and Challenges of Artificial Intelligence,” Council of Economic
Advisors remarks, New York University: AI Now Symposium, July 7, 2016.
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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

essential for measuring and evaluating AI systems and ensuring that AI technologies meet criticalobjectives for functionality and interoperability.
Finally, the growing prevalence of AI technologies across all sectors of society creates new pressures
for AI R&D experts. Opportunities abound for core AI scientists and engineers with a deep
understanding of the technology who can generate new ideas for advancing the boundaries ofknowledge in the field. The Nation should take action to ensure a sufficient pipeline of AI-capabletalent. Strategy 7 addresses this challenge.
Figure 1 (updated in this 2019 version of the Plan) provides a graphical illustration of the overall
organization of this AI R&D Strategic Plan. Across the bottom row of boxes are the crosscutting,underlying foundations that affect the development of all AI systems; these foundations are describedin Strategies 3-7 and the new Strategy 8. The next layer higher (middle row of boxes) includes many
areas of research that are needed to advance AI. These R&D areas (including use-inspired basic
research) are outlined in Strategies 1-2. 12 Across the top row of boxes in the graphic are examples ofapplications that are expected to benefit from advances in AI. Together, these components of the AI
R&D Strategic Plan define a high-level framework for Federal investments that can lead to impactfuladvances in the field and positive societal benefits.

Figure 1. Organization of the AI R&D Strategic Plan (2019 update, to include Strategy 8). A combination ofcrosscutting R&D foundations (in the lower row) are important for all AI research. Many AI R&D areas (in the
middle row) can build upon these crosscutting foundations to impact a wide array of societal applications(in the top row). The numbers in brackets indicate the number of the Strategy in this plan that further
develops each topic. The ordering of these strategies does not indicate a priority of importance.

12

Throughout this document, “basic research” includes both pure basic research and use-inspired basic research—
the so-called Pasteur’s Quadrant defined by Donald Stokes in his 1997 book of the same name—referring to basic
research that has use for society in mind. For example, the fundamental NIH investments in IT are often called useinspired basic research.

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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEStrategy 1: Make Long-Term Investments in AI Research
2019
UpdateSustaining long-term investments in fundamental AI research

Since the release of the 2016 National
AI R&D Strategic Plan, powerful newcapabilities, primarily ML applications
to well-defined tasks, have continued
to emerge. These capabilities havedemonstrated impacts in a diverse
array of applications, such asclassifying genetic sequences, 20,21
managing limited wireless spectrum
resources, 22 interpreting medical
images, 23 and grading cancers. 24 These
rapid advances required decades ofresearch for the technologies andapplications to mature. 25 To maintainthis progress in ML to achieveadvancements in other areas of AI, andto strive toward the long-term goal ofgeneral-purpose AI, the Federal
Government must continue to foster
long-term, fundamental research in ML
and AI. This research will give rise totransformational technologies and, inturn, breakthroughs across all sectorsof society.
Much of the current progress in thefield has been in specialized, welldefined tasks often driven bystatistical ML, such as classification,
recognition, and regression (i.e.,“narrow AI systems”). Surveys of theLong-term, fundamental AI research:
Recent agency R&D programs
Since the release of the 2016 National AI R&D Strategic Plan,a number of agencies have initiated AI R&D programs for
Strategy 1:
 NSF has continued to fund foundational research in AI,
spanning ML, reasoning and representation, computer
vision, computational neuroscience, speech andlanguage, robotics, and multi-agent systems. NSF haslaunched new joint funding opportunities with otheragencies—notably with DARPA in the area of highperformance, energy-efficient hardware for real-time
ML13 and with USDA-NIFA on AI for agriculturalscience 14—and with industry. 15,16 In addition, NSF’s
Harnessing the Data Revolution Big Idea 17 supports
research on the foundations of data science, which willserve as a driver of future ML and AI systems.
 DARPA announced in September 2018 a multiyear
investment in new and existing programs called the “AI
Next” campaign. 18 Key campaign areas include
improving the robustness and reliability of AI systems;
enhancing the security and resiliency of ML/AI
technologies; reducing power, data, and performanceinefficiencies; and pioneering the next generation of AI
algorithms and applications, such as explainability andcommonsense reasoning.
 The NIH Strategic Plan for Data Science 19 of September
2018 aims to advance access to data science technology
and ML/AI capability for the biomedical researchcommunity toward data-driven healthcare research.

https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505640&org=NSF
https://www.nsf.gov/pubs/2019/nsf19051/nsf19051.jsp
15
https://www.nsf.gov/pubs/2019/nsf19018/nsf19018.jsp
16https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505651
17https://www.nsf.gov/cise/harnessingdata/
18
https://www.darpa.mil/work-with-us/ai-next-campaign
19
https://datascience.nih.gov/strategicplan
20
https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html
21https://irp.nih.gov/catalyst/v26i4/machine-learning
22
https://www.spectrumcollaborationchallenge.com/
23
https://news-medical.net/news/20190417/Workshop-explores-the-future-of-artificial-intelligence-in-medical-imaging.aspx
24
https://www.nature.com/articles/nature21056
25
https://www.nitrd.gov/rfi/ai/2018/AI-RFI-Response-2018-Yolanda-Gil-AAAI.pdf
13
14

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field have noted that long-term investments in fundamental research are needed to continuebuilding on these advances in ML. Further, parallel sustained efforts are required to fully realize thevision of “general-purpose AI”—systems that exhibit the flexibility and versatility of human
intelligence in a broad range of cognitive domains. 26,27,28,29
Emphasis is needed on the development of further ML capabilities to interactively and persistently
learn, the connection between perception and attention, and the incorporation of learned models
into comprehensive reasoning architectures. 30 Beyond ML, critical research is also needed in othercore areas of AI, including in commonsense reasoning and problem solving, probabilistic reasoning,combinatorial optimization, knowledge representation, planning and scheduling, natural language
processing, decision making, and human-machine interaction. Advances in these areas will in turnenable collaborative robotics and shared and fully autonomous systems (see Strategy 2). The grandchallenge of understanding human intelligence requires significant investments in shared resources
and infrastructure.25 Broad consensus exists for foundational investments in drivers of ML and AI as
well, including data provenance and quality, novel software and hardware paradigms and platforms,
and the security of AI systems. 31,32 For example, as AI software performs increasingly complex
functions in all aspects of daily life and all sectors of the economy, existing software development
paradigms will need to evolve to meet software productivity, quality, and sustainability requirements.
Recent Federal investments have prioritized these areas of fundamental ML and AI research (seesidebar) as well as the use of ML and AI across numerous application sectors, including defense,
security, energy, transportation, health, agriculture, and telecommunications. Ultimately, AI
technologies are critical for addressing a range of long-term challenges, such as constructing
advanced healthcare systems, a robust intelligent transportation system, and resilient energy andtelecommunication networks.
For AI applications to become widespread, they must be explainable and understandable (seeStrategy 3). These challenges are particularly salient for fostering collaborative human-AI
relationships (see Strategy 2). Today, the ability to understand and analyze the decisions of AI
systems and measure their accuracy, reliability, and reproducibility is limited. Sustained R&D
investments are needed to advance trust in AI systems to ensure they meet society’s needs andadequately address requirements for robustness, fairness, explainability, and security.
A long-term commitment to AI R&D is essential to continue and expand current technical advancesand more broadly ensure that AI enriches the human experience. Indeed, the 2019 Executive Orderon Maintaining American Leadership in Artificial Intelligence notes:
Heads of implementing agencies that also perform or fund R&D (AI R&D agencies), shallconsider AI as an agency R&D priority, as appropriate to their respective agencies’
missions… Heads of such agencies shall take this priority into account when developing
budget proposals and planning for the use of funds in Fiscal Year 2020 and in future years.
Heads of these agencies shall also consider appropriate administrative actions to increase
focus on AI for 2019.

https://ai100.stanford.edu/sites/g/files/sbiybj9861/f/ai100report10032016fnl_singles.pdf
http://cdn.aiindex.org/2018/AI%20Index%202018%20Annual%20Report.pdf
28
https://cra.org/ccc/visioning/visioning-activities/2018-activities/artificial-intelligence-roadmap/
29
https://www.microsoft.com/en-us/research/research-area/artificial-intelligence/30
https://cra.org/ccc/events/artificial-intelligence-roadmap-workshop-3-learning-and-robotics/
31
https://cra.org/ccc/wp-content/uploads/sites/2/2016/04/AI-for-Social-Good-Workshop-Report.pdf
32
https://openai.com/blog/ai-and-compute/
26
27

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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEAI research investments are needed in areas with potential long-term payoffs. While an importantcomponent of long-term research is incremental research with predictable outcomes, long-termsustained investments in high-risk research can lead to high-reward payoffs. These payoffs can be seenin 5 years, 10 years, or more. A 2012 National Research Council report emphasizes the critical role ofFederal investments in long-term research, noting “the long, unpredictable incubation period—
requiring steady work and funding—between initial exploration and commercial deployment.” 33 It
further notes that “the time from first concept to successful market is often measured in decades.” Welldocumented examples of sustained fundamental research efforts that led to high-reward payoffs
include the World Wide Web and deep learning. In both cases, the basic foundations began in the 1960s;
it was only after 30+ years of continued research efforts that these ideas materialized into thetransformative technologies witnessed today in many categories of AI.
The following subsections highlight some of these areas. Additional categories of important AI research
are discussed in Strategies 2 through 6.

Advancing data-focused methodologies for knowledge discovery
As discussed in the 2016 Federal Big Data Research and Development Strategic Plan, 34 many
fundamental new tools and technologies are needed to achieve intelligent data understanding andknowledge discovery. Further progress is needed in the development of more advanced machinelearning algorithms that can identify all the useful information hidden in big data. Many open researchquestions revolve around the creation and use of data, including its veracity and appropriateness for AI
system training. The veracity of data is particularly challenging when dealing with vast amounts of data,
making it difficult for humans to assess and extract knowledge from it. While much research has dealt
with veracity through data quality assurance methods to perform data cleaning and knowledge
discovery, further study is needed to improve the efficiency of data cleaning techniques, to create
methods for discovering inconsistencies and anomalies in the data, and to develop approaches forincorporating human feedback. Researchers need to explore new methods to enable data andassociated metadata to be mined simultaneously.
Many AI applications are interdisciplinary in nature and make use of heterogeneous data. Further
investigation of multimodality machine learning is needed to enable knowledge discovery from a wide
variety of different types of data (e.g., discrete, continuous, text, spatial, temporal, spatio-temporal,graphs). AI investigators must determine the amount of data needed for training and to properly
address large-scale versus long-tail data needs. They must also determine how to identify and processrare events beyond purely statistical approaches; to work with knowledge sources (i.e., any type of
information that explains the world, such as knowledge of the law of gravity or of social norms) as well
as data sources, integrating models and ontologies in the learning process; and to obtain effective
learning performance with little data when big data sources may not be available.

Enhancing the perceptual capabilities of AI systems
Perception is an intelligent system’s window into the world. Perception begins with (possibly
distributed) sensor data, which comes in diverse modalities and forms, such as the status of the systemitself or information about the environment. Sensor data are processed and fused, often along witha priori knowledge and models, to extract information relevant to the AI system’s task such as

National Research Council Computer Science Telecommunications Board, Continuing Innovation in Information
Technology (The National Academies Press, Washington, D.C., 2012), 11; https://doi.org/10.17226/13427.
34
https://www.nitrd.gov/PUBS/bigdatardstrategicplan.pdf
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geometric features, attributes, location, and velocity. Integrated data from perception forms
situational awareness to provide AI systems with the comprehensive knowledge and a model of thestate of the world necessary to plan and execute tasks effectively and safely. AI systems would greatly
benefit from advancements in hardware and algorithms to enable more robust and reliable perception.
Sensors must be able to capture data at longer distances, with higher resolution, and in real time.
Perception systems need to be able to integrate data from a variety of sensors and other sources,
including the computational cloud, to determine what the AI system is currently perceiving and to allow
the prediction of future states. Detection, classification, identification, and recognition of objects
remain challenging, especially under cluttered and dynamic conditions. In addition, perception ofhumans must be greatly improved by using an appropriate combination of sensors and algorithms, sothat AI systems can work more effectively with people.10 A framework for calculating and propagating
uncertainty throughout the perception process is needed to quantify the confidence level that the AI
system has in its situational awareness and to improve accuracy.

Understanding theoretical capabilities and limitations of AI
While the ultimate goal for many AI algorithms is to address open challenges with human-like solutions,
we do not have a good understanding of what the theoretical capabilities and limitations are for AI and
the extent to which such human-like solutions are even possible with AI algorithms. Theoretical work
is needed to better understand why AI techniques—especially machine learning—often work well inpractice. While different disciplines (including mathematics, control sciences, and computer science)are studying this issue, the field currently lacks unified theoretical models or frameworks to understandAI system performance. Additional research is needed on computational solvability, which is anunderstanding of the classes of problems that AI algorithms are theoretically capable of solving, andlikewise, those that they are not capable of solving. This understanding must be developed in thecontext of existing hardware, in order to see how the hardware affects the performance of thesealgorithms. Understanding which problems are theoretically unsolvable can lead researchers todevelop approximate solutions to these problems, or even open up new lines of research on new
hardware for AI systems. For example, when invented in the 1960s, Artificial Neural Networks (ANNs)
could only be used to solve very simple problems. It only became feasible to use ANNs to solve complex
problems after hardware improvements such as parallelization were made, and algorithms were
adjusted to make use of the new hardware. Such developments were key factors in enabling today’s
significant advances in deep learning.

Pursuing research on general-purpose artificial intelligence
AI approaches can be divided into “narrow AI” and “general AI.” Narrow AI systems perform individualtasks in specialized, well-defined domains, such as speech recognition, image recognition, andtranslation. Several recent, highly-visible, narrow AI systems, including IBM Watson and DeepMind’s
AlphaGo, have achieved major feats. 35,36 Indeed, these particular systems have been labeled“superhuman” because they have outperformed the best human players in Jeopardy! and Go,
respectively. But these systems exemplify narrow AI, since they can only be applied to the tasks forwhich they are specifically designed. Using these systems on a wider range of problems requires asignificant re-engineering effort. In contrast, the long-term goal of general AI is to create systems that35
36

In 2011, IBM Watson defeated two players considered among the best human players in the Jeopardy! game.
In 2016, AlphaGo defeated the reigning world champion of Go, Lee Se-dol. Notably, AlphaGo combines deeplearning and Monte Carlo search—a method developed in the 1980s—which itself builds on a probabilistic method
discovered in the 1940s.

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exhibit the flexibility and versatility of human intelligence in a broad range of cognitive domains,
including learning, language, perception, reasoning, creativity, and planning. Broad learning
capabilities would provide general AI systems the ability to transfer knowledge from one domain toanother and to interactively learn from experience and from humans. General AI has been an ambitionof researchers since the advent of AI, but current systems are still far from achieving this goal. The
relationship between narrow and general AI is currently being explored; it is possible that lessons fromone can be applied to improve the other and vice versa. While there is no general consensus, most AI
researchers believe that general AI is still decades away, requiring a long-term, sustained research
effort to achieve it.

Developing scalable AI systems
Groups and networks of AI systems may be coordinated or autonomously collaborate to perform tasks
not possible with a single AI system, and may also include humans working alongside or leading theteam. The development and use of such multi-AI systems creates significant research challenges inplanning, coordination, control, and scalability of such systems. Planning techniques for multi-AI
systems must be fast enough to operate and adapt in real time to changes in the environment. They
should adapt in a fluid manner to changes in available communications bandwidth or systemdegradation and faults. Many prior efforts have focused on centralized planning and coordination
techniques; however, these approaches are subject to single points of failure, such as the loss of theplanner, or loss of the communications link to the planner. Distributed planning and control techniques
are harder to achieve algorithmically, and are often less efficient and incomplete, but potentially offer
greater robustness to single points of failure. Future research must discover more efficient, robust, andscalable techniques for planning, control, and collaboration of teams of multiple AI systems and humans.

Fostering research on human-like AI
Attaining human-like AI requires systems to explain themselves in ways that people can understand.
This will result in a new generation of intelligent systems, such as intelligent tutoring systems andintelligent assistants that are effective in assisting people when performing their tasks. There is asignificant gap, however, between the way current AI algorithms work and how people learn andperform tasks. People are capable of learning from just a few examples, or by receiving formal
instruction and/or “hints” to performing tasks, or by observing other people performing those tasks.
Medical schools take this approach, for example, when medical students learn by observing anestablished doctor performing a complex medical procedure. Even in high-performance tasks such as
world-championship Go games, a master-level player would have played only a few thousand games
to train him/herself. In contrast, it would take hundreds of years for a human to play the number ofgames needed to train AlphaGo. More foundational research on new approaches for achieving humanlike AI would bring these systems closer to this goal.

Developing more capable and reliable robots
Significant advances in robotic technologies over the last decade are leading to potential impacts in a
multiplicity of applications, including manufacturing, logistics, medicine, healthcare, defense andnational security, agriculture, and consumer products. While robots were historically envisioned forstatic industrial environments, recent advances involve close collaborations between robots andhumans. Robotics technologies are now showing promise in their ability to complement, augment,enhance, or emulate human physical capabilities or human intelligence. However, scientists need tomake these robotic systems more capable, reliable, and easy-to-use.

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Researchers need to better understand robotic perception to extract information from a variety of
sensors to provide robots with real-time situational awareness. Progress is needed in cognition andreasoning to allow robots to better understand and interact with the physical world. An improved
ability to adapt and learn will allow robots to generalize their skills, perform self-assessment of theircurrent performance, and learn a repertoire of physical movements from human teachers. Mobility andmanipulation are areas for further investigation so that robots can move across rugged and uncertain
terrain and handle a variety of objects dexterously. Robots need to learn to team together in a seamlessfashion and collaborate with humans in a way that is trustworthy and predictable.

Advancing hardware for improved AI
While AI research is most commonly associated with advances in software, the performance of AI
systems has been heavily dependent on the hardware upon which it runs. The current renaissance indeep machine learning is directly tied to progress in GPU-based hardware technology and its improved
memory, 37 input/output, clock speeds, parallelism, and energy efficiency. Developing hardware
optimized for AI algorithms will enable even higher levels of performance than GPUs. One example is“neuromorphic” processors that are loosely inspired by the organization of the brain and, in somecases, optimized for the operation of neural networks. 38
Hardware advances can also improve the performance of AI methods that are highly data-intensive.
Further study of methods to turn on and off data pipelines in controlled ways throughout a distributed
system is called for. Continued research is also needed to allow machine learning algorithms toefficiently learn from high-velocity data, including distributed machine learning algorithms thatsimultaneously learn from multiple data pipelines. More advanced machine learning-based feedback
methods will allow AI systems to intelligently sample or prioritize data from large-scale simulations,experimental instruments, and distributed sensor systems, such as Smart Buildings and the Internet ofThings (IoT). Such methods may require dynamic I/O decision-making, in which choices are made inreal time to store data based on importance or significance, rather than simply storing data at fixed
frequencies.

Creating AI for improved hardware
While improved hardware can lead to more capable AI systems, AI systems can also improve theperformance of hardware. 39 This reciprocity will lead to further advances in hardware performance,since physical limits on computing require novel approaches to hardware designs. 40 AI-based methods
could be especially important for improving the operation of high-performance computing (HPC)
systems. Such systems consume vast quantities of energy. AI is being used to predict HPC performanceand resource usage, and to make online optimization decisions that increase efficiency; moreadvanced AI techniques could further enhance system performance. AI can also be used to create

GPU stands for graphics processing unit, which is a power- and cost-efficient processor incorporating hundreds ofprocessing cores; this design makes it especially well suited for inherently parallel applications, including most AI
systems.
38
Neuromorphic computing refers to the ability of hardware to learn, adapt, and physically reconfigure, taking
inspiration from biology or neuroscience.
39
M. Milano and L. Benini, “Predictive Modeling for Job Power Consumption in HPC Systems,” In Proceedings of High
Performance Computing: 31st International Conference, ISC High Performance 2016 (Springer Vol. 9697, 2016).
40
These physical limits on computing are called Dennard scaling, and lead to high on-chip power densities and thephenomenon called “dark silicon”, where different parts of a chip need to be turned off in order to limit
temperatures and ensure data integrity.
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self-reconfigurable HPC systems that can handle system faults when they occur, without humanintervention. 41
Improved AI algorithms can increase the performance of multi-core systems by reducing data
movements between processors and memory—the primary impediment to exascale computing
systems that operate 10 times faster than today’s supercomputers. 42 In practice, the configuration of
executions in HPC systems are never the same, and different applications are executed concurrently,
with the state of each different software code evolving independently in time. AI algorithms need to be
designed to operate online and at scale for HPC systems.

A. Cocaña-Fernández, J. Ranilla, and L. Sánchez, “Energy-efficient allocation of computing node slots in HPC
clusters through parameter learning and hybrid genetic fuzzy system modeling,” Journal of Supercomputing 71(2015):1163-1174.
42
Exascale computing systems can achieve at least a billion billion calculations per second.
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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEStrategy 2: Develop Effective Methods for Human-AI Collaboration
2019
UpdateDeveloping AI systems that complement and augment human capabilities,
with increasing focus on the future of work

Since the release of the 2016 National AI
R&D Strategic Plan, national interest has
grown in human-AI collaboration. When AI
systems complement and augment humancapabilities, humans and AI become
partners across a range of shared to fullyautonomous scenarios. In particular,
human-AI collaboration has been elevatedas both a challenge and an opportunity inthe context of the future of work.
In the past three years, newly establishedas well as longstanding conferences,
workshops, and task forces haveprioritized
human-AI
collaboration
broadly. For example, the Conference on
Human Computation and Crowdsourcing
has grown from a workshop to a majorinternational conference that fosters
research in the intersection of AI andhuman-computer interaction (HCI). 45 In
2018, the Association for the Advancementof Artificial Intelligence selected human-AI
collaboration as the emerging topic for its
annual conference. 46 In May 2019, thelargest conference on human-computerinteraction, CHI, included a workshop on“Bridging the Gap Between AI and HCI.”47
The journal Human-Computer Interaction
put out a call in March 2019 forsubmissions for a special issue on“unifying human-computer interaction
and artificial intelligence.” 48

Human-AI Collaboration:
Recent agency R&D programs
Since the release of the 2016 National AI R&D StrategicPlan, several agencies have initiated efforts for Strategy 2:
 NSF’s Future of Work at the Human-Technology
Frontier 43 Big Idea is supporting socio-technicalresearch enabling a future where intelligent
technologies collaborate synergistically withhumans to achieve broad participation in theworkforce and improve the social, economic, andenvironmental benefits across a range of work
settings.
 NOAA (National Oceanographic and Atmospheric
Administration)isadvancinghuman-AI
collaboration for hurricane, tornado, and othersevere weather predictions where the human
forecaster and an AI system work together toimprove severe weather warning generation andto identify distinct patterns that are precursors to
extreme events. Sometimes referred to as “humans
above the loop,” human forecasters oversee the AI
system's predictions and direct the outcomes.
 NIH has ongoing research in natural language
processing based on a database of 96.3 million
facts extracted from all MEDLINE citations
maintained by the National Library of Medicine.
 A 2019 DOE workshop report on Scientific Machine
Learning identified priority research directions,major scientific use cases, and the emerging trendthat human-AI collaborations will transform theway science is done. 44

https://www.nsf.gov/eng/futureofwork.jsp
DOE workshop report, Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial
Intelligence: https://www.osti.gov/biblio/1478744.
45
Welcome to HCOMP 2019: https://www.humancomputation.com/.
46
AAAI-18 Emerging Topic Human-AI Collaboration:
http://www.aaai.org/Conferences/AAAI/2018/aaai18emergingcall.php.
47
Where is the Human? Bridging the Gap Between AI and HCI: CHI 2019 Workshop: https://michae.lv/ai-hci-workshop/.
48
Call: “Unifying Human Computer Interaction and Artificial Intelligence” issue of Human-Computer Interaction:
https://ispr.info/2019/02/20/call-unifying-human-computer-interaction-and-artificial-intelligence-issue-of-humancomputer-interaction/.
43
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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEIn the context of work, conferences have emerged exploring the role of the human, the machine, andtheir partnership, such as MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and theInitiative on the Digital Economy that launched the Annual AI and the Future of Work Congress. 49,50
As part of A 20-Year Community Roadmap for Artificial Intelligence Research in the U.S., 51 in 2019 theComputing Community Consortium (CCC) held a workshop focused on meaningful interaction
between humans and AI systems. 52 Additionally, the CCC operated the Human Technology Frontier
task force in 2017-2018 to focus on the potential of technology to augment human performance in,including but not limited to, the workplace, the classroom, and the healthcare system. 53
The cross-strategy principle in the 2016 National AI R&D Strategic Plan, “appropriate trust of AI
systems requires explainability, especially as the AI grows in scale and complexity,” has seen an R&D
call to action in the context of human-AI collaborations. This principle has been identified by anumber of professional societies and agencies as a priority area (see sidebar). This research area
reflects the intersection of Strategies 2 and 3, as explainability, fairness, and transparency are key
principles for AI systems to effectively collaborate with humans. Likewise, the challenge ofunderstanding and designing human-AI ethics and value alignment into systems remains an open
research area. In parallel, the private sector has responded with principles for effective human-AI
collaboration. 54,55
As Federal agencies have increased AI investments in the past three years along mission objectives,
they have shared a common emphasis on human-machine cognition, autonomy, and agency, suchas in decision support, risk modeling, situational awareness, and trusted machine intelligence (seesidebar). Through such R&D investments, research partnerships are growing across a number ofaxes, bringing together computational scientists; behavioral, cognitive, and psychologicalscientists; and scientists and engineers from other domains. New collaborations have formed
between academic researchers and users of AI systems inside and outside the workplace.
Moving forward, it is critical that Federal agencies continue to foster AI R&D in the open world topromote the design of AI systems that incorporate and accommodate the situations and goals of users
so that AI systems and users can work collaboratively in both anticipated and unanticipatedcircumstances.
While completely autonomous AI systems will be important in some application domains (e.g.,underwater or deep space exploration), many other application areas (e.g., disaster recovery andmedical diagnostics) are most effectively addressed by a combination of humans and AI systems
working together to achieve application goals. This collaborative interaction takes advantage of thecomplementary nature of humans and AI systems. While effective approaches for human-AI
collaboration already exist, most of these are “point solutions” that only work in specific environments
using specific platforms toward specific goals. Generating point solutions for every possible application
instance does not scale; more work is thus needed to go beyond these point solutions toward morehttps://futureofwork.csail.mit.edu/.
AI and Future of Work Innovation Summit 2019: https://analyticsevent.com/.
51
https://cra.org/ccc/wp-content/uploads/sites/2/2019/03/AI_Roadmap_Exec_Summary-FINAL-.pdf
52
Artificial Intelligence Roadmap Workshop 2 – Interaction: https://cra.org/ccc/events/artificial-intelligenceroadmap-workshop-2-interaction/.
53
https://cra.org/ccc/human-technology-frontier/54
https://www.microsoft.com/en-us/research/uploads/prod/2019/01/Guidelines-for-Human-AI-Interaction-camera-ready.pdf
55
https://www.partnershiponai.org/about/#our-work49
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general methods of human-AI collaboration. The tradeoffs must be explored between designing
general systems that work in all types of problems, requiring less human effort to build and greater
facility for switching between applications, versus building a large number of problem-specific systems
that may work more effectively for each problem.
Future applications will vary considerably in the functional role divisions between humans and AI
systems, the nature of the interactions between humans and AI systems, the number of humans andother AI systems working together, and how humans and AI systems will communicate and sharesituational awareness. Functional role divisions between humans and AI systems typically fall into oneof the following categories:
1. AI performs functions alongside the human: AI systems perform peripheral tasks that support thehuman decision maker. For example, AI can assist humans with working memory, short or longterm memory retrieval, and prediction tasks.
2. AI performs functions when the human encounters high cognitive overload: AI systems perform
complex monitoring functions (such as ground proximity warning systems in aircraft), decision
making, and automated medical diagnoses when humans need assistance.
3. AI performs functions in lieu of a human: AI systems perform tasks for which humans have very
limited capabilities, such as for complex mathematical operations, control guidance for dynamic
systems in contested operational environments, aspects of control for automated systems in
harmful or toxic environments, and in situations where a system should respond very rapidly (e.g.,
in nuclear reactor control rooms).
Achieving effective interactions between humans and AI systems requires additional R&D to ensure thatthe system design does not lead to excessive complexity, undertrust, or overtrust. The familiarity ofhumans with AI systems can be increased through training and experience, to ensure that the human
has a good understanding of the AI system’s capabilities and what the AI system can and cannot do. To
address these concerns, certain human-centered automation principles should be used in the designand development of these systems: 56
1. Employ intuitive, user-friendly design of human-AI system interfaces, controls, and displays.
2. Keep the operator informed. Display critical information, states of the AI system, and changes tothese states.
3. Keep the operator trained. Engage in recurrent training for general knowledge, skills, and abilities(KSAs), as well as training in algorithms and logic employed by AI systems and the expected failuremodes of the system.
4. Make automation flexible. Deploying AI systems should be considered as a design option for
operators who wish to decide whether they want to use them or not. Also important is the designand deployment of adaptive AI systems that can be used to support human operators during
periods of excessive workload or fatigue. 57,58
Many fundamental challenges arise for researchers when creating systems that work effectively withhumans. Several of these important challenges are outlined in the following subsections.

C. Wickens and J. G. Hollands, “Attention, time-sharing, and workload.” In Engineering, Psychology and Human
Performance (London: Pearson PLC, 1999), 439-479.
57
https://www.nasa.gov/mission_pages/SOFIA/index.html
58
https://cloud1.arc.nasa.gov/intex-na/
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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

Seeking new algorithms for human-aware AI
Over the years, AI algorithms have become able to solve problems of increasing complexity. However,there is a gap between the capabilities of these algorithms and the usability of these systems byhumans. Human-aware intelligent systems are needed that can interact intuitively with users andenable seamless machine-human collaborations. Intuitive interactions include shallow interactions,such as when a user discards an option recommended by the system; model-based approaches thattake into account the users’ past actions; or even deep models of user intent that are based uponaccurate human cognitive models. Interruption models must be developed that allow an intelligent
system to interrupt the human only when necessary and appropriate. Intelligent systems should also
have the ability to augment human cognition, knowing which information to retrieve when the userneeds it, even when they have not prompted the system explicitly for that information. Future
intelligent systems must be able to account for human social norms and act accordingly. Intelligent
systems can more effectively work with humans if they possess some degree of emotional intelligence, sothat they can recognize their users’ emotions and respond appropriately. An additional research goal
is to go beyond interactions of one human and one machine, toward a “systems-of-systems”, that is,teams composed of multiple machines interacting with multiple humans.
Human-AI system interactions have a wide range of objectives. AI systems need the ability to represent
a multitude of goals, actions that they can take to reach those goals, constraints on those actions, andother factors, as well as easily adapt to modifications in the goals. In addition, humans and AI systems
must share common goals and have a mutual understanding of them and relevant aspects of theircurrent states. Further investigation is needed to generalize these facets of human-AI systems todevelop systems that require less human engineering.

Developing AI techniques for human augmentation
While much of the prior focus of AI research has been on algorithms that match or outperform people
performing narrow tasks, more work is needed to develop systems that augment human capabilities
across many domains. Human augmentation research includes algorithms that work on a stationary
device (such as a computer); wearable devices (such as smart glasses); implanted devices (such as brain
interfaces); and in specific user environments (such as specially tailored operating rooms). For example,
augmented human awareness could enable a medical assistant to point out a mistake in a medical
procedure, based on data readings combined from multiple devices. Other systems could augmenthuman cognition by helping the user recall past experiences applicable to the user’s current situation.
Another type of collaboration between humans and AI systems involves active learning for intelligent
data understanding. In active learning, input is sought from a domain expert and learning is only
performed on data when the learning algorithm is uncertain. This is an important technique to reduce
the amount of training data that needs to be generated in the first place, or the amount that needs tobe learned. Active learning is also a key way to obtain domain expert input and increase trust in thelearning algorithm. Active learning has so far only been used within supervised learning; further researchis needed to incorporate active learning into unsupervised learning (e.g., clustering, anomaly detection)and reinforcement learning. 59 Probabilistic networks allow domain knowledge to be included in the formof prior probability distributions. General ways of allowing machine learning algorithms to incorporate
domain knowledge must be sought, whether in the form of mathematical models, text, or others.

59

While supervised learning requires humans to provide the ground-truth answers, reinforcement learning andunsupervised learning do not.

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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEDeveloping techniques for visualization and human-AI interfaces
Better visualization and user interfaces are additional areas that need much greater development tohelp humans understand large-volume modern datasets and information coming from a variety ofsources. Visualization and user interfaces must clearly present increasingly complex data andinformation derived from them in a human-understandable way. Providing real-time results isimportant in safety-critical operations and may be achieved with increasing computational power andconnected systems. In these types of situations, users need visualization and user interfaces that canquickly convey the correct information for real-time response.
Human-AI collaboration can be applied in a wide variety of environments, and where there areconstraints on communication. In some domains, human-AI communication latencies are low andcommunication is rapid and reliable. In other domains (e.g., NASA’s deployment of the rovers Spirit andOpportunity to Mars), remote communication between humans and the AI system has a very highlatency (e.g., round trip times of 5-20 minutes between Earth and Mars), thus requiring the deployedplatform(s) to operate largely autonomously, with only high-level strategic goals communicated to theplatform. These communications requirements and constraints are important considerations for the
R&D of user interfaces.

Developing more effective language processing systems
Enabling people to interact with AI systems through spoken and written language has long been a goalof AI researchers. While significant advances have been made, considerable open research challenges
must be addressed in language processing before humans can communicate as effectively with AI
systems as they do with other humans. Much recent progress in language processing has been credited
to the use of data-driven machine learning approaches, which have resulted in successful systems that,for example, successfully recognize fluent English speech in quiet surroundings in real time. These
achievements, however, are only first steps toward reaching longer-term goals. Current systems cannot
deal with real-world challenges such as speech in noisy surroundings, heavily accented speech,
children’s speech, impaired speech, and speech for sign languages. The development of language
processing systems capable of engaging in real-time dialogue with humans is also needed. Suchsystems will need to infer the goals and intentions of its human interlocutors, use the appropriate
register, style and rhetoric for the situation, and employ repair strategies in case of dialogue
misunderstandings. Further research is needed on developing systems that more easily generalize
across different languages. Additionally, more study is required on acquiring useful structured domain
knowledge in a form readily accessible by language processing systems.
Language processing advances in many other areas are also needed to make interactions between
humans and AI systems more natural and intuitive. Robust computational models must be built for
patterns in both spoken and written language that provide evidence for emotional state, affect, andstance, and for determining the information that is implicit in speech and text. New language
processing techniques are needed for grounding language in the environmental context for AI systems
that operate in the physical world, such as in robotics. Finally, since the manner in which people
communicate in online interactions can be quite different from voice interactions, models of languages
used in these contexts must be perfected so that social AI systems can interact more effectively withpeople.

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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEStrategy 3: Understand and Address the Ethical, Legal, and Societal
Implications of AI
2019
UpdateAddressing ethical, legal, and societal considerations in AI

Since the release of the 2016 National AI
R&D Strategic Plan, R&D activities
addressing the ethical, legal, and societal
implications of AI system development
and deployment have increased. There is a
growing realization that AI systems must
be “trustworthy,” and that AI could
transform many sectors of social andeconomic life, including employment,
healthcare,andmanufacturing.
International organizations such as theOrganisation for Economic Co-operation
and Development (OECD) 63 and the G7
Innovation Ministers 64 have encouraged
R&D to increase trust in and adoption of AI.
The 2016 National AI R&D Strategic Plan
was prescient in identifying research
themes in privacy; improving fairness,transparency, and accountability of AI
systems by design; and designingarchitectures for ethical AI. Research
conferences dedicated to fairness,accountability, and transparency in ML
and AI systems have flourished. 65 Federal
agencies have responded with a variety of
new research programs and meetings
focused on these critical areas (seesidebar).

Explainability, fairness, and transparency:
Recent agency R&D programs
Since the release of the 2016 National AI R&D StrategicPlan, a number of agencies have initiated AI R&D
programs for Strategy 3:
 DARPA’s Explainable AI (XAI) program 60 aims tocreate a suite of ML techniques that produce more
explainable AI systems while maintaining a highlevel of learning performance (prediction
accuracy). XAI will also enable human users tounderstand, appropriately trust, and effectively
manage the emerging generation of AI systems.
More generally, DoD is committed to “leading inmilitary ethics and AI safety” as one of five key
actions outlined in the strategic approach thatguides its efforts to accelerate the adoption of AI
systems. 61
 NSF and Amazon are collaborating 62 to jointly
support research focused on AI fairness with thegoal of contributing to trustworthy AI systems thatare readily accepted and deployed to tackle grandchallenges facing society. Specific topics ofinterest include, but are not limited to,transparency, explainability, accountability,
potential adverse biases and effects, mitigation
strategies, validation of fairness, andconsiderations of inclusivity.

https://www.darpa.mil/program/explainable-artificial-intelligence“Summary of the 2018 Department of Defense Artificial Intelligence Strategy”:
https://media.defense.gov/2019/Feb/12/2002088963/-1/-1/1/SUMMARY-OF-DOD-AI-STRATEGY.PDF.
62
https://www.nsf.gov/pubs/2019/nsf19571/nsf19571.htm
63“OECD Initiatives on AI”: http://www.oecd.org/going-digital/ai/oecd-initiatives-on-ai.htm.
64“G7 Innovation Ministers' Statement on AI”: http://www.g8.utoronto.ca/employment/2018-labour-annex-b-en.html.
65
http://www.fatml.org/; https://fatconference.org/; http://www.aies-conference.com/
60
61– 19 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEThe 2019 Executive Order on Maintaining American Leadership in Artificial Intelligence emphasizes
that maintaining American leadership in AI requires a concerted effort to promote advancements intechnology and innovation, while protecting civil liberties, privacy, and American values: 1
The United States must foster public trust and confidence in AI technologies and protect civil
liberties, privacy, and American values in their application in order to fully realize thepotential of AI technologies for the American people.
More R&D is needed to develop AI architectures that incorporate ethical, legal, and societal concerns
through technical mechanisms such as transparency and explainability. This R&D will require
intensive collaboration among technical experts as well as stakeholders and specialists in otherfields including the social and behavioral sciences, law, ethics, and philosophy. Since ethicaldecisions may also be heavily context- or application-dependent, collaboration with domain
experts could be required as well. This interdisciplinary approach could be incorporated in the
training, design, testing, evaluation, and implementation of AI in the interests of understanding andaccounting for AI-induced decisions and actions and mitigating unintended consequences.
Federal agencies should therefore continue to foster the growing community of interest in further
R&D of these issues by sponsoring research and convening experts and stakeholders.
When AI agents act autonomously, we expect them to behave according to the formal and informal
norms to which we hold our fellow humans. As fundamental social ordering forces, law and ethics
therefore both inform and adjudge the behavior of AI systems. The dominant research needs involve
both understanding the ethical, legal, and social implications of AI, as well as developing methods for
AI design that align with ethical, legal, and social principles. Privacy concerns must also be taken into
account; further information on this issue can be found in the National Privacy Research Strategy. 66
As with any technology, the acceptable uses of AI will be informed by the tenets of law and ethics; thechallenge is how to apply those tenets to this new technology, particularly those involving autonomy,
agency, and control.
As illuminated in “Research Priorities for Robust and Beneficial Artificial Intelligence,” 67
In order to build systems that robustly behave well, we of course need to decide what good
behavior means in each application domain. This ethical dimension is tied intimately toquestions of what engineering techniques are available, how reliable these techniques are, andwhat trade-offs are made—all areas where computer science, machine learning, and broader AI
expertise is valuable.
Research in this area can benefit from multidisciplinary perspectives that involve experts fromcomputer science, social and behavioral sciences, ethics, biomedical science, psychology, economics,
law, and policy research. Further investigation is needed in areas both inside and outside of the NITRDrelevant IT domain (i.e., in information technology, as well as in the disciplines mentioned previously)
to inform the R&D and use of AI systems and their impacts on society.
The following subsections explore key information technology research challenges in this area.

66
67

https://www.nitrd.gov/pubs/NationalPrivacyResearchStrategy.pdf“An Open Letter: Research Priorities for Robust and Beneficial Artificial Intelligence” (Future of Life Institute):
http://futureoflife.org/ai-open-letter/.

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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

Improving fairness, transparency, and accountability by design
Many concerns have been voiced about the susceptibility of data-intensive AI algorithms to error andmisuse, and the possible ramifications for gender, age, racial, or economic classes. The propercollection and use of data for AI systems, in this regard, represent an important challenge. Beyond
purely data-related issues, however, larger questions arise about the design of AI to be inherently just,fair, transparent, and accountable. Researchers must learn how to design these systems so that theiractions and decision-making are transparent and easily interpretable by humans, and thus can be
examined for any bias they may contain, rather than just learning and repeating these biases. There areserious intellectual issues about how to represent and “encode” value and belief systems. Scientists
must also study to what extent justice and fairness considerations can be designed into the system,and how to accomplish this within the bounds of current engineering techniques.

Building ethical AI
Beyond fundamental assumptions of justice and fairness are other concerns about whether AI systems
can exhibit behavior that abides by general ethical principles. How might advances in AI frame new“machine-relevant” questions in ethics, or what uses of AI might be considered unethical? Ethics isinherently a philosophical question while AI technology depends on, and is limited by, engineering.
Within the limits of what is technologically feasible, therefore, researchers must strive to develop
algorithms and architectures that are verifiably consistent with, or conform to, existing laws, social
norms and ethics—clearly a very challenging task. Ethical principles are typically stated with varying
degrees of vagueness and are hard to translate into precise system and algorithm design. There arealso complications when AI systems, particularly with new kinds of autonomous decision-making
algorithms, face moral dilemmas based on independent and possibly conflicting value systems. Ethicalissues vary according to culture, religion, and beliefs. However, acceptable ethics reference frameworks
can be developed to guide AI system reasoning and decision-making in order to explain and justify its
conclusions and actions. A multidisciplinary approach is needed to generate datasets for training thatreflect an appropriate value system, including examples that indicate preferred behavior whenpresented with difficult moral issues or with conflicting values. These examples can include legal or
ethical “corner cases,” labeled by an outcome or judgment that is transparent to the user. 68 AI needs
adequate methods for values-based conflict resolution, where the system incorporates principles that
can address the realities of complex situations where strict rules are impracticable.

Designing architectures for ethical AI
Additional progress in fundamental research must be made to determine how to best design
architectures for AI systems that incorporate ethical reasoning. A variety of approaches have beensuggested, such as a two-tier monitor architecture that separates the operational AI from a monitor
agent that is responsible for the ethical or legal assessment of any operational action.68 An alternative
view is that safety engineering is preferred, in which a precise conceptual framework for the AI agent
architecture is used to ensure that AI behavior is safe and not harmful to humans. 69 A third method is
to formulate an ethical architecture using set theoretic principles, combined with logical constraints

A. Etzioni and O. Etzioni, “Designing AI Systems that Obey Our Laws and Values,” Communications of the ACM 59(9)(2016):29-31.
69
R. Y. Yampolsky, “Artificial Intelligence Safety Engineering: Why Machine Ethics is a Wrong Approach.” In Philosophy
and Theory of Artificial Intelligence, ed. V.C. Muller (Heidelberg: Springer Verlag, 2013), 389-396.
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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

on AI system behavior that restrict action to conform to ethical doctrine. 70 As AI systems become moregeneral, their architectures will likely include subsystems that can take on ethical issues at multiple
levels of judgment, including: 71 rapid response pattern matching rules, deliberative reasoning for
slower responses for describing and justifying actions, social signaling to indicate trustworthiness forthe user, and social processes that operate over even longer time scales to enable the system to abide
by cultural norms. Researchers will need to focus on how to best address the overall design of AI
systems that align with ethical, legal, and societal goals.

R. C. Arkin, “Governing Legal Behavior: Embedding Ethics in a Hybrid Deliberative/Reactive Robot Architecture,”Georgia Institute of Technology Technical Report, GIT-GVU-07-11, 2007.
71
B. Kuipers, “Human-like Morality and Ethics for Robots,” AAAI-16 Workshop on AI, Ethics and Society, 2016;
https://web.eecs.umich.edu/~kuipers/papers/Kuipers-aaaiws-16.pdf
70– 22 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEStrategy 4: Ensure the Safety and Security of AI Systems
2019
UpdateCreating robust and trustworthy AI systems

Since the 2016 release of the National AI
R&D Strategic Plan, there has been rapid
growth in scientific and societal
understanding of AI security and safety.
Much of this new knowledge has helped
identify new problems: it is more evident
now how AI systems can be made to do thewrong thing, learn the wrong thing, or
reveal the wrong thing, for example,
through adversarial examples, datapoisoning,
and
model
inversion,respectively. Unfortunately, technicalsolutions for these AI safety and security
problems remain elusive.
To address all of these problems, the safety
and security of AI systems must be
considered in all stages of the AI system
lifecycle, from the initial design anddata/model building, to verification andvalidation, deployment, operation, andmonitoring. Indeed, the notion of “safety(or security) by design” might impart anincorrect notion that these are only
concerns of system designers; instead, theymust be considered throughout the system
lifecycle, not just at the design stage, andso must be an important part of the AI R&D
portfolio.
When AI components are connected toother systems or information that must be
safe or secure, the AI vulnerabilities andperformance requirements (e.g., very low
false-positive and false-negative rates,when operating over high volumes of data)

AI safety and security:
Recent agency R&D programs
Since the release of the 2016 National AI R&D StrategicPlan, a number of agencies have initiated efforts
supporting Strategy 4:
 DOT published new Federal guidance for
automated vehicles in October 2018 supporting
the safe integration of automation into the broad
multimodal surface transportation system.
Preparing for the Future of Transportation:
Automated Vehicles 3.0 72 advances DOT’s
principles for safe integration of automatedvehicles. The document also reiterates priorsafety guidance, provides new multimodal safety
guidance, and outlines a process for working with
DOT as this new technology evolves. As of May
2019, fourteen companies had released
Voluntary Safety Self-Assessments detailing how
they will incorporate safety into their design andtesting of automated driving systems. 73
 In December 2018, IARPA announced two
programs on AI security: Secure, Assured,Intelligent Learning Systems (SAILS) 74 andTrojans in Artificial Intelligence (TrojAI). 75 DARPA
announced another program in February 2019,
Guaranteeing AI Robustness against Deception(GARD). 76 Together, these programs aim tocombat a range of attacks on AI systems.
 As noted in Strategy 3, DoD is committed to“leading in military ethics and AI safety” as one offive key actions outlined in the strategic
approach that guides its efforts to accelerate theadoption of AI systems. 77

https://www.transportation.gov/av/3
https://www.nhtsa.gov/automated-driving-systems/voluntary-safety-self-assessment
74
https://www.iarpa.gov/index.php/research-programs/sails
75
https://www.iarpa.gov/index.php/research-programs/trojai
76
https://www.darpa.mil/news-events/2019-02-06
77“Summary of the 2018 Department of Defense Artificial Intelligence Strategy”:
https://media.defense.gov/2019/Feb/12/2002088963/-1/-1/1/SUMMARY-OF-DOD-AI-STRATEGY.PDF.
72
73– 23 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

are inherited by the larger systems. These challenges are not static; as AI systems continue to growin capabilities, they will likely grow in complexity, making it ever harder for correct performance or
privacy of information to be verified and validated. This complexity may also make it increasingly
difficult to explain decisions in ways that justify high levels of trust from human users (see Strategy 3).
Making AI trustworthy—now and into the future—is a critical issue that requires Federal Government
R&D investments (see sidebar), along with collaborative efforts among government, industry,
academia, and civil society. Engineering trustworthy AI systems may benefit from borrowing existing
practices in safety engineering in other fields that have learned how to account for potential
misbehavior of non-AI autonomous or semi-autonomous systems. However, AI-specific problems
mean that novel techniques for program analysis, testing, formal verification, and synthesis will be
critical to establish that an AI-based system meets its specifications—that is, that the system does
exactly what it is supposed to do and no more. These problems are exacerbated in AI-based systems
that can be easily fooled, evaded, and misled in ways that can have profound security implications.
An emerging research area is adversarial ML, which explores both the analysis of vulnerabilities in
ML algorithms as well as algorithmic techniques that yield more robust learning. Well-known attacks
on ML include adversarial classifier evasion attacks, where the attacker changes behavior to escapebeing detected, and poisoning attacks, where training data itself is corrupted. There is growing needfor research that systematically explores the space of adversaries that attack ML and other AI-based
systems and to design algorithms that provide provable robustness guarantees against classes ofadversaries.
Methods must be developed to make safe and secure the creation, evaluation, deployment, andcontainment of AI, and these methods must scale to match the capability and complexity of AI.
Evaluating these methods will require new metrics, control frameworks, and benchmarks for testing
and assessing the safety of increasingly powerful systems. Both methods and metrics must
incorporate human factors, with safe AI objectives defined by human designers’ goals, safe AI
operations defined by human users’ habits, and safe AI metrics defined by human evaluators’
understanding. Producing human-driven and human-understandable methods and metrics for thesafety of AI systems will enable policymakers, the private sector, and the public to accurately judge
the evolving AI safety landscape and appropriately proceed within it.
Before an AI system is put into widespread use, assurance is needed that the system will operate safely
and securely, in a controlled manner. Research is needed to address this challenge of creating AI
systems that are reliable, dependable, and trustworthy. As with other complex systems, AI systems face
important safety and security challenges due to: 78• Complex and uncertain environments: In many cases, AI systems are designed to operate in complex
environments, with a large number of potential states that cannot be exhaustively examined ortested. A system may confront conditions that were never considered during its design.
• Emergent behavior: For AI systems that learn after deployment, a system's behavior may be
determined largely by periods of learning under unsupervised conditions. Under such conditions,
it may be difficult to predict a system’s behavior.
• Goal misspecification: Due to the difficulty of translating human goals into computer instructions,the goals that are programmed for an AI system may not match the goals that were intended by
the programmer.

78

J. Bornstein, “DoD Autonomy Roadmap – Autonomy Community of Interest,” Presentation at NDIA 16th Annual
Science & Engineering Technology Conference, March 2015.

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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE• Human-machine interactions: In many cases, the performance of an AI system is substantiallyaffected by human interactions. In these cases, variation in human responses may affect thesafety of the system. 79

To address these issues and others, additional investments are needed to advance AI safety andsecurity, 80 including explainability and transparency, trust, verification and validation, security against
attacks, and long-term AI safety and value-alignment.

Improving explainability and transparency
A key research challenge is increasing the “explainability” or “transparency” of AI. Many algorithms,
including those based on deep learning, are opaque to users, with few existing mechanisms for
explaining their results. This is especially problematic for domains such as healthcare, where doctors
need explanations to justify a particular diagnosis or a course of treatment. AI techniques such asdecision-tree induction provide built-in explanations but are generally less accurate. Thus, researchers
must develop systems that are transparent, and intrinsically capable of explaining the reasons for theirresults to users.

Building trust
To achieve trust, AI system designers need to create accurate, reliable systems with informative, userfriendly interfaces, while the operators must take the time for adequate training to understand system
operation and limits of performance. Complex systems that are widely trusted by users, such as manualcontrols for vehicles, tend to be transparent (the system operates in a manner that is visible to the user),credible (the system’s outputs are accepted by the user), auditable (the system can be evaluated),reliable (the system acts as the user intended), and recoverable (the user can recover control whendesired). A significant challenge to current and future AI systems remains the inconsistent quality ofsoftware production technology. As advances bring greater linkages between humans and AI systems,
the challenge in the area of trust is to keep pace with changing and increasing capabilities, anticipate
technological advances in adoption and long-term use, and establish governing principles and policies
for the study of best practices for design, construction, and use, including proper operator training forsafe operation.

Enhancing verification and validation
New methods are needed for verification and validation of AI systems. “Verification” establishes that asystem meets formal specifications, while “validation” establishes that a system meets the user’s
operational needs. Safe AI systems may require new means of assessment (determining if the system ismalfunctioning, perhaps when operating outside expected parameters), diagnosis (determining thecauses for the malfunction), and repair (adjusting the system to address the malfunction). For systems
operating autonomously over extended periods of time, system designers may not have consideredevery condition the system will encounter. Such systems may need to possess capabilities for selfassessment, self-diagnosis, and self-repair in order to be robust and reliable.

J. M. Bradshaw, R. R. Hoffman, M. Johnson, and D. D. Woods, “The Seven Deadly Myths of Autonomous Systems,”
IEEE Intelligent Systems 28(3)(2013):54-61.
80See, for instance: D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mane, “Concrete Problems in
AI Safety,” 2016, arXiv: 1606.06565v2; S. Russell, D. Dewey, and M. Tegmark, “Research Priorities for Robust andBeneficial Artificial Intelligence,” 2016, arXiv: 1602.03506; T. G. Dietterich and E. J. Horvitz, “Rise of Concerns about AI:
Reflections and Directions,” Communications of the ACM, 58(10)(2015); and K. Sotala and R. Yampolskiy, “Responses
to catastrophic AGI risk: A survey,” Physica Scripta, 90(1), 19 December 2014.
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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

Securing against attacks
AI embedded in critical systems must be robust in order to handle accidents but should also be secure
to a wide range of intentional cyber attacks. Security engineering involves understanding thevulnerabilities of a system and the actions of actors who may be interested in attacking it. Whilecybersecurity R&D needs are addressed in greater detail in the NITRD 2016 Federal Cybersecurity R&D
Strategic Plan, 81 some cybersecurity risks are specific to AI systems. For example, one key research area
is “adversarial machine learning” that explores the degree to which AI systems can be compromised by“contaminating” training data, by modifying algorithms, or by making subtle changes to an object thatprevent it from being correctly identified (e.g., prosthetics that spoof facial recognition systems). The
implementation of AI in cybersecurity systems that require a high degree of autonomy is also an areafor further study. One recent example of work in this area is DARPA’s Cyber Grand Challenge thatinvolved AI agents autonomously analyzing and countering cyber attacks. 82

Achieving long-term AI safety and value-alignment
AI systems may eventually become capable of “recursive self-improvement,” in which substantial
software modifications are made by the software itself, rather than by human programmers. To ensure
the safety of self-modifying systems, additional research is called for to develop: self-monitoringarchitectures that check systems for behavioral consistency with the original goals of human designers;
confinement strategies for preventing the release of systems while they are being evaluated; valuelearning, in which the values, goals, or intentions of users can be inferred by a system; and value
frameworks that are provably resistant to self-modification.

https://www.nitrd.gov/pubs/2016-Federal-Cybersecurity-Research-and-Development-Strategic-Plan.pdf; this isbeing updated in 2019.
82
https://archive.darpa.mil/CyberGrandChallenge_CompetitorSite/81– 26 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEStrategy 5: Develop Shared Public Datasets and Environments for AI Training
and Testing2019
UpdateIncreasing access to datasets and associated challenges

At the time of the 2016 National AI R&D
Strategic Plan’s release, publicly available
datasets and environments were alreadyplaying a critical role in pushing forward AI
R&D, particularly in areas such as
computer vision, natural language
processing, and speech recognition.
ImageNet, 84 with more than 14 million
labeled objects, along with associatedcomputer vision community challenges(e.g., the ImageNet Large Scale Visual
Recognition Challenge 85 that evaluatesalgorithms for object detection and image
classification), have played an especiallyvital role in the community. As translational
applications for ML are being found in
myriad application areas such as
healthcare, medicine, and smart andconnected communities, the need has
grown for publicly available datasets indomain-specific areas.
The importance of datasets and models –in particular, those of the Federal
Government – is explicitly called out in the2019 Executive Order on Maintaining
AmericanLeadershipin
Artificial
Intelligence:1
Heads of all agencies shall review theirFederal data and models to identify
opportunities to increase access anduse by the greater non-Federal AI
research community in a manner that
benefits that community, while
protecting safety, security, privacy, andconfidentiality. Specifically, agencies
shall improve data and model
inventory documentation to enablediscovery and usability, and shallShared Public Datasets and Environments for AI
Training and Testing: Recent agency R&D programs
Since the release of the 2016 National AI R&D StrategicPlan, a number of agencies have initiated efforts
supporting Strategy 5:
 DOT sponsored the Second Strategic Highway
Research Program (SHRP2) Naturalistic Driving
Study (NDS), 83 which recorded more than
5.4 million trips taken by more than 3,400 drivers
and vehicles. An in-vehicle data acquisition system(DAS) unit gathered and stored data from forward
radar, four video cameras, accelerometers, vehicle
network information, a geographic positioning
system, and an onboard lane tracker. Data from the
DAS were recorded continuously while participants’vehicles were operating. Whereas summaries of
the NDS data are public, access to the detailed
datasets requires qualified research ethics training.
 The VA Data Commons is creating the largest linked
medical–genomics dataset in the world with tools
for enabling ML and AI, and guided by veterans’
preferences. This effort is leveraging applicable
NIST standards, laws, and executive orders.
 GSA (General Services Administration) is working toenable the use of cloud computing resources forfederally funded AI R&D. Data.gov and code.gov,
housed at GSA, contain over 246,000 datasets andcode from across agencies and automaticallyharvest datasets released by agencies.
 The NIH Science and Technology Research
Infrastructure for Discovery, Experimentation, andSustainability (STRIDES) initiative, a partnershipwith industry-leading cloud service providers, isenabling researcher access to major data assetsthat are funded across NIH and that are stored incloud environments.

https://insight.shrp2nds.us/
http://www.image-net.org/
85
http://www.image-net.org/challenges/LSVRC/
83
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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

prioritize improvements to access and quality of AI data and models based on the AI researchcommunity’s feedback.
A new NSTC Subcommittee on Open Science was created in 2018 to coordinate Federal efforts on
open and FAIR (findable, accessible, interoperable, and reusable) data. R&D investments will beneeded to develop tools and resources that make it easier to identify, use, and manipulate relevant
datasets (including Federal datasets), verify data provenance, and respect appropriate use policy.
Many of these datasets themselves may be of limited use in an AI context without an investment inlabeling and curation. Federal agencies should engage and work with AI stakeholders to ensure thatappropriately vetted datasets and models that are released for sharing are ready and fit for use andthat they are maintained as standards and norms evolve. Ultimately, development and adoption ofbest practices and standards in documenting dataset and model provenance will enhancetrustworthiness and responsible use of AI technologies.
Since 2016, there have also been increased concerns about data content, such as potential bias (seeStrategy 3) 86,87 or private information leakage. The 2016 National AI R&D Strategic Plan noted that“dataset development and sharing must … follow applicable laws and regulations and be carriedout in an ethical manner.” The DOT-supported InSight project provides such carefully structured
access to data collected during the Naturalistic Driving Study (see sidebar). The 2016 National AI R&D
Strategic Plan also noted that new “technologies are needed to ensure safe sharing of data, sincedata owners take on risk when sharing their data with the research community.” For example,
CryptoNets 88 allows neural networks to operate over encrypted data, ensuring that data remain
confidential, because decryption keys are not needed in neural networks. Researchers have also
begun developing new ML techniques that use a differential privacy framework to provide
quantifiable privacy guarantees over the used data. 89 At the same time, privacy methods mustremain sufficiently explainable and transparent to help researchers correct them and make themsafe, efficient, and accurate. Furthermore, AI could reveal discoveries beyond the original or
intended scope; therefore, researchers must remain cognizant of the potential dangers in access todata or discoveries by adversaries.
Data alone are of little use without the ability to bring computational resources to bear on large-scale
public datasets. The importance of computational resources to AI R&D is called out in the 2019
Executive Order on Maintaining American Leadership in Artificial Intelligence:1
The Secretaries of Defense, Commerce, Health and Human Services, and Energy, theAdministrator of the National Aeronautics and Space Administration, and the Director of the
National Science Foundation shall, to the extent appropriate and consistent with applicablelaw, prioritize the allocation of high-performance computing resources for AI-related
applications through: (i) increased assignment of discretionary allocation of resources andresource reserves; or (ii) any other appropriate mechanisms.

Emily M. Bender and Batya Friedman, “Data Statements for NLP: Toward Mitigating System Bias and Enabling
Better Science,” Transactions of the Association for Computational Linguistics 6 (2018):587-604.
87
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan, “Semantics derived automatically from language corpora
contain human-like biases,” Science 356(6334):183-186, 14 Apr 2017.
88
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, John Wernsing, “CryptoNets:
Applying neural networks to encrypted data with high throughput and accuracy,” 2016 International Conference on
Machine Learning 48:201-210; http://proceedings.mlr.press/v48/.
89
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang, “DeepLearning with Differential Privacy,” 23rd ACM Conference on Computer and Communications Security,” 2016: 308-318.
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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

and:…the Select Committee, in coordination with the General Services Administration (GSA), shallsubmit a report to the President making recommendations on better enabling the use ofcloud computing resources for federally funded AI R&D.
The need for computational capacity for many AI challenges has been increasing rapidly.32 Federal
funding may provide computational capabilities for Federally-funded research. Some companies
and universities, however, may have additional computational demands. Overall, there is a national
need to study and invest in shared computational resources to promote AI R&D.
The benefits of AI will continue to accrue, but only to the extent that training and testing resources for
AI are developed and made available. The variety, depth, quality, and accuracy of training datasets andother resources significantly affects AI performance. Many different AI technologies require high-quality
data for training and testing, as well as dynamic, interactive testbeds and simulation environments.
More than just a technical question, this is a significant “public good” challenge, as progress would
suffer if AI training and testing is limited to only a few entities that already hold valuable datasets andresources, yet we must simultaneously respect commercial and individual rights and interests in thedata. Research is needed to develop high-quality datasets and environments for a wide variety of AI
applications and to enable responsible access to good datasets and testing and training resources.
Additional open-source software libraries and toolkits are also needed to accelerate the advancementof AI R&D. The following subsections outline these key areas of importance.

Developing and making accessible a wide variety of datasets to meet the needs of a diverse
spectrum of AI interests and applications
The integrity and availability of AI training and testing datasets is crucial to ensuring scientificallyreliable results. The technical as well as the socio-technical infrastructure necessary to support
reproducible research in the digital area has been recognized as an important challenge—and isessential to AI technologies as well. The lack of vetted and openly available datasets with identified
provenance to enable reproducibility is a critical factor to confident advancement in AI. 90 As in otherdata-intensive sciences, capturing data provenance is critical. Researchers must be able to reproduce
results with the same as well as different datasets. Datasets must be representative of challenging realworld applications, and not just simplified versions. To make progress quickly, emphasis should beplaced on making available already existing datasets held by government, those that can be developedwith Federal funding, and, to the extent possible, those held by industry.
The machine learning aspect of the AI challenge is often linked with “big data” analysis. Considering
the wide variety of relevant datasets, it remains a growing challenge to have appropriate
representation, access, and analysis of unstructured or semi-structured data. How can the data be
represented—in absolute as well as relative (context-dependent) terms? Current real-world databases
can be highly susceptible to inconsistent, incomplete, and noisy data. Therefore, a number of data
preprocessing techniques (e.g., data cleaning, integration, transformation, reduction, andrepresentation) are important to establishing useful datasets for AI applications. How does the data
preprocessing impact data quality, especially when additional analysis is performed?

90

Toward this end, in 2016 the Intelligence Advanced Research Projects Activity issued a Request for Information on
novel training datasets and environments to advance AI. See https://iarpa.gov/index.php/working-with-iarpa/requests-for-information/novel-training-datasets-and-environments-to-advance-artificial-intelligence.

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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

Encouraging the sharing of AI datasets—especially for government-funded research—would likely
stimulate innovative AI approaches and solutions. However, technologies are needed to ensure safe
sharing of data, since data owners take on risk when sharing their data with the research community.
Dataset development and sharing must also follow applicable laws and regulations and be carried out
in an ethical manner. Risks can arise in various ways: inappropriate use of datasets, inaccurate or
inappropriate disclosure, and limitations in data de-identification techniques to ensure privacy andconfidentiality protections.

Making training and testing resources responsive to commercial and public interests
With the continuing explosion of data, data sources, and information technology worldwide, both thenumber and size of datasets are increasing. The techniques and technologies to analyze data are notkeeping up with the high volume of raw information sources. Data capture, curation, analysis, andvisualization are all key research challenges, and the science needed to extract valuable knowledge
from enormous amounts of data is lagging behind. While data repositories exist, they are often unable
to deal with the scaling up of datasets, have limited data provenance information, and do not support
semantically rich data searches. Dynamic, agile repositories are needed.
One example of the kind of open/sharing infrastructure program that is needed to support the needs of
AI research is the IMPACT program (Information Marketplace for Policy and Analysis of Cyber-risk & Trust)developed by the Department of Homeland Security (DHS). 91 This program supports the global cybersecurity risk research effort by coordinating and developing real-world data and information sharing
capabilities, including tools, models, and methodologies. IMPACT also supports empirical data sharing
between the international cybersecurity R&D community, critical infrastructure providers, and theirgovernment supporters. AI R&D would benefit from comparable programs across all AI applications.

Developing open-source software libraries and toolkits
The increased availability of open-source software libraries and toolkits provides access to cuttingedge AI technologies for any developer with an Internet connection. Resources such as the Weka
toolkit, 92 MALLET, 93 and OpenNLP, 94 among many others, have accelerated the development andapplication of AI. Development tools, including free or low-cost code repository and version control
systems, as well as free or low-cost development languages (e.g., R, Octave, and Python) provide low
barriers to using and extending these libraries. In addition, for those who may not want to integratethese libraries directly, any number of cloud-based machine learning services exist that can performtasks such as image classification on demand through low-latency web protocols that require little orno programming for use. Finally, many of these web services also offer the use of specialized hardware,
including GPU-based systems. It is reasonable to assume that specialized hardware for AI algorithms,
including neuromorphic processors, will also become widely available through these services.
Together, these resources provide an AI technology infrastructure that encourages marketplaceinnovation by allowing entrepreneurs to develop solutions that solve narrow domain problems without
requiring expensive hardware or software, without requiring a high level of AI expertise, and permitting
rapid scaling-up of systems on demand. For narrow AI domains, barriers to marketplace innovation are
extremely low relative to many other technology areas.

https://www.dhs.gov/csd-impact
https://sourceforge.net/projects/weka/
93
http://mallet.cs.umass.edu
94
https://opennlp.apache.org
91
92– 30 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATETo help support a continued high level of innovation in this area, the U.S. Government can boost efforts
in the development, support, and use of open AI technologies. Particularly beneficial would be open
resources that use standardized or open formats and open standards for representing semantic
information, including domain ontologies when available.
Government may also encourage greater adoption of open AI resources by accelerating the use of open
AI technologies within the government itself, and thus help to maintain a low barrier to entry forinnovators. Whenever possible, government should contribute algorithms and software to open source
projects. Because government has specific concerns, such as a greater emphasis on data privacy andsecurity, it may be necessary for the government to develop mechanisms to ease government adoptionof AI systems. For example, it may be useful to create a task force that can perform a “horizon scan”
across government agencies to find particular AI application areas within departments, and thendetermine specific concerns that would need to be addressed to permit adoption of such techniquesby these agencies.

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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEStrategy 6: Measure and Evaluate AI Technologies through Standards andBenchmarks
2019
UpdateSupporting development of AI technical standards and related tools

The 2016 National AI R&D Strategic Planstates that “Standards, benchmarks,
testbeds, and their adoption by the AI
community are essential for guiding andpromoting R&D of AI technologies.” In theintervening three years, emphasis on
standards and benchmarks has continued
to rise in the U.S. and globally. The 2019
Executive Order on Maintaining American
Leadership in Artificial Intelligence
explicitly calls out the importance of suchstandards:1…[T]he Secretary of Commerce,
through the Director of [NIST], shallissue a plan for Federal engagement inthedevelopmentoftechnical
standards and related tools in supportof reliable, robust, and trustworthysystems that use AI technologies.

Standards, benchmarks, and related tools:
Recent agency R&D programs
Since the release of the 2016 National AI R&D StrategicPlan, NIST in particular has initiated efforts for Strategy 6:
 NIST is engaged in the standardization program of
ISO/IEC JTC 1 SC 42 on Artificial Intelligence. 95 A
NIST expert is the convener for the Big Data work
effort in SC 42. The U.S. delegation to SC 42
includes NIST and other Federal agency experts,
as well as representatives from industry andacademia. U.S. input to SC 42 is facilitated by theInternational Committee for Information
Technology Standards (INCITS).
 NIST staff participate in additional AI standards
activities through standards organizations, suchas the American Society of Mechanical Engineers,
IEEE, and ISO/IEC. Their activities cover such topics
as computational modeling for advanced
manufacturing, ontologies for robotics andautomation, personal data privacy, andalgorithmic bias.

With AI innovation potentially impacting
all sectors and domains of society, many
standards development organizations
 NIST experts are raising awareness about thehave new AI-related considerations andimportance of consensus standards for AI inwork items underway, including activities
multilateral fora, including bodies such as G20 and
related to AI ethics and trustworthy AI
G7.96 NIST brings unique Federal Government
systems (see Strategy 3). The International
expertise that grounds policy discussions in
Organization for Standardization (ISO) andpractice, in particular, through close collaboration
theInternational
Electrotechnicalwith the private sector. Similarly, NIST lends its
Commission (IEC) have convened a joint
standards
andrelatedexperience
totechnical subcommittee on AI (ISO/IEC
intergovernmental bilateral discussions.
Joint Technical Committee 1, Subcommittee
42 on Artificial Intelligence95) to develop standards for AI systems and associated considerations. It
is critical that Federal, industry, and academic researchers continue to inform these activities,
particularly as AI advances and systems reach into areas such as transportation, health care, andfood that align with the missions of government agencies.
Since 2016, the surge in AI-related standards activities has outpaced the launch of new AI-focusedbenchmarks and evaluations, particularly as related to trustworthiness of AI systems. In the95
96

https://www.iso.org/committee/6794475.html
https://home.treasury.gov/policy-issues/international/g-7-and-g-20– 32 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

intervening time, however, considerations of fairness and bias in benchmark datasets have become
increasingly important, with researchers pursuing new facial recognition datasets that seek tominimize bias. Much more plentiful are benchmarks that test the application-level performance ofAI algorithms (e.g., false-positive or false-negative rates for classification algorithms) andbenchmarks that quantify the compute-level performance of AI software and hardware systems.
Two such recent activities are MLPerf 97 and DAWNbench. 98
Assessing, promoting, and assuring all aspects of AI trustworthiness requires measuring andevaluating AI technology performance through benchmarks and standards. Beyond being safe,
secure, reliable, resilient, explainable, and transparent, trustworthy AI must preserve privacy while
detecting and avoiding inappropriate bias. As AI technologies evolve, so will the need to develop
new metrics and testing requirements for validation of these essential characteristics.
Standards, benchmarks, testbeds, and their adoption by the AI community are essential for guiding andpromoting R&D of AI technologies. The following subsections outline areas where additional progress
must be made.

Developing a broad spectrum of AI standards
The development of standards must be hastened to keep pace with the rapidly evolving capabilities
and expanding domains of AI applications. Standards provide requirements, specifications, guidelines,
or characteristics that can be used consistently to ensure that AI technologies meet critical objectives
for functionality and interoperability, and that they perform reliably and safely. Adoption of standards
brings credibility to technology advancements and facilitates an expanded interoperable marketplace.
One example of an AI-relevant standard that has been developed is P1872-2015 (Standard Ontologies
for Robotics and Automation), developed by the Institute of Electrical and Electronics Engineers. This
standard provides a systematic way of representing knowledge and a common set of terms anddefinitions. These allow for unambiguous knowledge transfer among humans, robots, and otherartificial systems, as well as provide a foundational basis for the application of AI technologies torobotics. Additional work in AI standards development is needed across all subdomains of AI.
Standards are needed to address:• Software engineering: to manage system complexity, sustainment, security, and to monitor and
•
•
•
•
•
•
•

97
98

control emergent behaviors;
Performance: to ensure accuracy, reliability, robustness, accessibility, and scalability;
Metrics: to quantify factors impacting performance and compliance to standards;
Safety: to evaluate risk management and hazard analysis of systems, human computerinteractions, control systems, and regulatory compliance;
Usability: to ensure that interfaces and controls are effective, efficient, and intuitive;
Interoperability: to define interchangeable components, data, and transaction models via
standard and compatible interfaces;
Security: to address the confidentiality, integrity, and availability of information, as well as
cybersecurity;
Privacy: to control for the protection of information while being processed, when in transit, or
being stored;

https://mlperf.org/
https://dawn.cs.stanford.edu/benchmark/– 33 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE• Traceability: to provide a record of events (their implementation, testing, and completion), andfor the curation of data; and• Domains: to define domain-specific standard lexicons and corresponding frameworks.
Establishing AI technology benchmarks
Benchmarks, made up of tests and evaluations, provide quantitative measures for developing
standards and assessing compliance to standards. Benchmarks drive innovation by promoting
advancements aimed at addressing strategically selected scenarios; they additionally provide
objective data to track the evolution of AI science and technologies. To effectively evaluate AI
technologies, relevant and effective testing methodologies and metrics must be developed andstandardized. Standard testing methods will prescribe protocols and procedures for assessing,comparing, and managing the performance of AI technologies. Standard metrics are needed to define
quantifiable measures in order to characterize AI technologies, including but not limited to: accuracy,complexity, trust and competency, risk and uncertainty, explainability, unintended bias, comparison
to human performance, and economic impact. It is important to note that benchmarks are data driven.
Strategy 5 discusses the importance of datasets for training and testing.
As a successful example of AI-relevant benchmarks, the National Institute of Standards and Technology
has developed a comprehensive set of standard test methods and associated performance metrics toassess key capabilities of emergency response robots. The objective is to facilitate quantitative
comparisons of different robot models by making use of statistically significant data on robot
capabilities that was captured using the standard test methods. These comparisons can guidepurchasing decisions and help developers to understand deployment capabilities. The resulting test
methods are being standardized though the ASTM International Standards Committee on Homeland
Security Applications for robotic operational equipment (referred to as standard E54.08.01). 99 Versionsof the test methods are used to challenge the research community through the RoboCup Rescue Robot
League competitions, 100 which emphasize autonomous capabilities. Another example is the IEEE Agile
Robotics for Industrial Automation Competition (ARIAC), 101 a joint effort between IEEE and NIST,102
which promotes robot agility by utilizing the latest advances in artificial intelligence and robot
planning. A core focus of this competition is to test the agility of industrial robot systems, with the goalof enabling those on the shop floors to be more productive, more autonomous, and requiring less timefrom shop floor workers.
While these efforts provide a strong foundation for driving AI benchmarking forward, they are limited
by being domain-specific. Additional standards, testbeds, and benchmarks are needed across a
broader range of domains to ensure that AI solutions are broadly applicable and widely adopted.

Increasing the availability of AI testbeds
The importance of testbeds was stated in the Cyber Experimentation of the Future report: “Testbeds are
essential so that researchers can use actual operational data to model and run experiments on
real-world system[s] … and scenarios in good test environments.” 103 Having adequate testbeds is a2019 update: The resulting test methods are now standards issued by ASTM International Standards Committee on
Homeland Secruity Applications for Response Robots (referred to as E54.09).
100
http://www.robocup2016.org/en/101
http://robotagility.wixsite.com/competition
102
2019 update: IEEE is no longer a partner of ARIAC, which is now in its third year.
103
SRI International and USC Information Sciences Institute, “Cybersecurity Experimentation of the Future (CEF):
Catalyzing a New Generation of Experimental Cybersecurity Research,” Final Report, July 31, 2015.
99– 34 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

need across all areas of AI. The government has massive amounts of mission-sensitive data unique togovernment, but much of this data cannot be distributed to the outside research community.
Appropriate programs could be established for academic and industrial researchers to conduct
research within secured and curated testbed environments established by specific agencies. AI models
and experimental methods could be shared and validated by the research community by having accessto these test environments, affording AI scientists, engineers, and students unique research
opportunities not otherwise available.

Engaging the AI community in standards and benchmarks
Government leadership and coordination is needed to drive standardization and encourage its
widespread use in government, academia, and industry. The AI community—made up of users,
industry, academia, and government—must be energized to participate in developing standards andbenchmark programs. As each government agency engages the community in different ways based on
its role and mission, community interactions can be leveraged through coordination in order tostrengthen their impact. This coordination is needed to collectively gather user-driven requirements,
anticipate developer-driven standards, and promote educational opportunities. User-driven
requirements shape the objectives and design of challenge problems and enable technology
evaluation. Having community benchmarks focuses R&D to define progress, close gaps, and drive
innovative solutions for specific problems. These benchmarks must include methods for defining andassigning ground truth. The creation of benchmark simulation and analysis tools will also accelerate AI
developments. The results of these benchmarks also help match the right technology to the user’sneed, forming objective criteria for standards compliance, qualified product lists, and potential source
selection.
Industry and academia are the primary sources for emerging AI technologies. Promoting andcoordinating their participation in standards and benchmarking activities are critical. As solutions
emerge, opportunities abound for anticipating developer- and user-driven standards through sharing
common visions for technical architectures, developing reference implementations of emerging
standards to show feasibility, and conducting precompetitive testing to ensure high-quality andinteroperable solutions, as well as to develop best practices for technology applications.
One successful example of a high-impact, community-based, AI-relevant benchmark program is the
Text Retrieval Conference (TREC), 104 which was started by NIST in 1992 to provide the infrastructurenecessary for large-scale evaluation of information retrieval methodologies. More than 250 groups haveparticipated in TREC, including academic and commercial organizations both large and small. The
standard, widely available, and carefully constructed set of data put forth by TREC has been creditedwith revitalizing research on information retrieval. 105,106 A second example is the NIST periodic
benchmark program in the area of machine vision applied to biometrics, 107 particularly facerecognition. 108 This began with the Face Recognition Technology (FERET) evaluation in 1993, which
provided a standard dataset of face photos designed to support face recognition algorithm
development as well as an evaluation protocol. This effort has evolved over the years into the Face

http://trec.nist.gov
E. M. Voorhees and D. K. Harman, TREC Experiment and Evaluation in Information Retrieval (Cambridge: MIT Press, 2005).
106
http://googleblog.blogspot.com/2008/03/why-data-matters.html
107
http://biometrics.nist.gov
108
http://face.nist.gov
104
105– 35 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

Recognition Vendor Test (FRVT), 109 involving the distribution of datasets, hosting of challenge
problems, and conducting of sequestered technology evaluations. This benchmark program has
contributed greatly to the improvement of facial recognition technology. Both TREC and FRVT can serveas examples of effective AI-relevant community benchmarking activities, but similar efforts are neededin other areas of AI.
It is important to note that developing and adopting standards, as well as participating in benchmark
activities, comes with a cost. R&D organizations are incentivized when they see significant benefit.
Updating acquisition processes across agencies to include specific requirements for AI standards inrequests for proposals will encourage the community to further engage in standards development andadoption. Community-based benchmarks, such as TREC and FRVT, also lower barriers and strengthen
incentives by providing types of training and testing data otherwise inaccessible, fostering healthy
competition between technology developers to drive best-of-breed algorithms, and providing
objective and comparative performance metrics for relevant source selections.

109

P. J. Phillips, “Improving Face Recognition Technology,” Computer 44(3)(2011): 84-96.

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THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEStrategy 7: Better Understand the National AI R&D Workforce Needs
2019
UpdateAdvancing the AI R&D workforce, including those working on AI systems andthose working alongside them, to sustain U.S. leadership

Since the release of the 2016 National AI
R&D Strategic Plan, the demand for AI
researchers and practitioners has grown
rapidly. Studies have shown that the
number of hiring opportunities is expected
to rise into the millions over the next
decade. As one data point, the U.S. Bureauof Labor Statistics projects that the numberof positions for computer and information
scientists and engineers will grow by 19%
from 2016 to 2026, almost three times fasterthan the average for all occupations. 111
Moreover, through 2028, AI researchers are
expected contribute to as much as $11.5
trillion of cumulative growth promised byintelligent technologies in the G20
countries alone. 112
U.S. academic institutions are struggling tokeep pace with the explosive growth instudent interest and enrollment in
AI. 113,114,115 At the same time, industry, with
its sustained financial support and accessto advanced computing facilities anddatasets, exerts a strong pull on academic
research and teaching talent. 116
It is critical to maintain a robust academic
research ecosystem in AI that, in
collaboration with industry R&D, cancontinue
todelivertremendous
dividends 117 by advancing national health,prosperity, and welfare, and securing thenational defense.

National AI R&D workforce:
Recent agency activities
Since the release of the 2016 National AI R&D StrategicPlan, a number of agencies have initiated efforts
supporting Strategy 7:
 Apart from supporting undergraduate andgraduate students through standard AI research
grants, agencies are prioritizing computationaland data-enabled science and engineering intheir graduate fellowship programs. For
example, in 2018, DOE added a new track to its
Computational Science Graduate Fellowship
program. This track supports students pursuing
advanced degrees in applied mathematics,
statistics, or computer science, and promotes
more effective use of high-performance systems,
including in the areas of AI, ML, and deeplearning.44,110 Also in 2018, NSF began prioritizing
computational and data-enabled science andengineering in a subset of awardees of its
Graduate Research Fellowships Program.
 The Census Bureau has created the Statistical
Data Modernization (SDM) project to bring its
workforce, operations, and technologies up to
the current state of the art and set the standard
for statistical agencies in today’s data-driven
society. SDM’s workforce transformation
component will enable the hiring of new data
scientists with expertise in new methods andanalytics, including the use of AI methods andtools to process and analyze big data. The
workforce transformation will also address theupskilling of the current data science workforce.

https://www.krellinst.org/csgf/math-cs
https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm
112
https://www.accenture.com/t20180920T094705Z__w__/us-en/_acnmedia/Thought-LeadershipAssets/PDF/Accenture-Education-and-Technology-Skills-Research.pdf
113
https://cra.org/data/generation-cs/
114
https://cra.org/wp-content/uploads/2018/05/2017-Taulbee-Survey-Report.pdf
115
http://web.cs.wpi.edu/~cew/papers/CSareas19.pdf
116
https://www.nitrd.gov/rfi/ai/2018/AI-RFI-Response-2018-Yolanda-Gil-AAAI.pdf
117
https://www.nap.edu/catalog/13427/continuing-innovation-in-information-technology
110
111– 37 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEIn the three years since the release of the 2016 National AI R&D Strategic Plan, various reports havecalled for continued support for the development of instructional materials and teacher
professional development in computer science at all levels. Emphasis is needed at the K–12 levels
to feed the Nation’s pipeline of AI researchers over many decades. 118 At the undergraduate level,
there is a need to focus on integrating advanced computational skills and methods with domainspecific knowledge from other disciplines, given the growing role of computing across disciplines. 119
Sustained support is also needed at the graduate level, where students are conducting fundamental
research in ML and AI. Indeed, the 2019 Executive Order on Maintaining American Leadership in
Artificial Intelligence requires that:1
Heads of implementing agencies that also provide educational grants shall, to the extent
consistent with applicable law, consider AI as a priority area within existing Federal
fellowship and service programs … [including] … (A) high school, undergraduate, andgraduate fellowship; alternative education; and training programs; (B) programs torecognize and fund early-career university faculty who conduct AI R&D, including throughPresidential awards and recognitions; (C) scholarship for service programs; (D) directcommissioning programs of the United States Armed Forces; and (E) programs that support
the development of instructional programs and curricula that encourage the integration of
AI technologies into courses in order to facilitate personalized and adaptive learning
experiences for formal and informal education and training.
More broadly, the need for a firm grounding in computational thinking, including through computerscience education, is also noted prominently in the Federal Government’s December 2018 five-year
strategic plan for science, technology, engineering, and mathematics (STEM) education. 120
In addition, it is imperative to broaden the participation among groups traditionally
underrepresented in computing and related fields.
Finally, the AI R&D workforce will consist of multidisciplinary teams comprising not just computerand information scientists and engineers, but also experts from other fields key to AI and ML
innovation and its application, including cognitive science and psychology, economics and gametheory, engineering and control theory, ethics, linguistics, mathematics, philosophy, and the many
domains in which AI may be applied.
Federal agencies are giving priority to training and fellowship programs at all levels to prepare theworkforce with requisite AI R&D skills through apprenticeships, skills programs, fellowships, andcourse work in relevant disciplines (see sidebar). Such training opportunities target both scientists
and engineers who contribute to AI R&D innovations and users of AI R&D who may possess relevant
domain knowledge. In the case of the former, long-term Federal investment in AI R&D, as describedin Strategy 1, further supports the growth of this workforce, both through training the next
generation of researchers and by making faculty positions more attractive to current graduate andpostdoctoral students. In the case of the latter, new programs are bringing AI-relevant skills tocurrent and future users of AI systems (see sidebar). Federal agencies must therefore continue tostrategically foster expertise in the AI R&D workforce that spans multiple disciplines and skill
categories to ensure sustained national leadership.

https://github.com/touretzkyds/ai4k12/wiki
https://www.nap.edu/catalog/24926/assessing-and-responding-to-the-growth-of-computer-scienceundergraduate-enrollments
120
https://www.whitehouse.gov/wp-content/uploads/2018/12/STEM-Education-Strategic-Plan-2018.pdf
118
119– 38 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

Attaining the needed AI R&D advances outlined in this strategy will require a sufficient AI R&D
workforce. Nations with the strongest presence in AI R&D will establish leading positions in theautomation of the future. They will become the frontrunners in competencies like algorithm creationand development; capability demonstration; and commercialization. Developing technical expertise
will provide the basis for these advancements.
While no official AI workforce data currently exist, numerous recent reports from the commercial andacademic sectors are indicating an increased shortage of available experts in AI. AI experts arereportedly in short supply, 121 with demand expected to continue to escalate. 122 High-tech companies
are reportedly investing significant resources into recruiting faculty members and students with AI
expertise. 123 Universities and industries are reportedly in a battle to recruit and retain AI talent. 124
Additional studies are needed to better understand the current and future national workforce needs
for AI R&D. Data is needed to characterize the current state of the AI R&D workforce, including the needsof academia, government, and industry. Studies should explore the supply and demand forces in the AI
workplace, to help predict future workforce needs. An understanding is needed of the projected AI R&D
workforce pipeline. Considerations of educational pathways and potential retraining opportunities
should be included. Diversity issues should also be explored, since studies have shown that a diverse
information technology workforce can lead to improved outcomes. 125 Once the current and future AI
R&D workforce needs are better understood, then appropriate plans and actions can be considered toaddress any existing or anticipated workforce challenges.

“Startups Aim to Exploit a Deep-Learning Skills Gap,” MIT Technology Review, January 6, 2016.
“AI talent grab sparks excitement and concern,” Nature, April 26, 2016.
123“Artificial Intelligence Experts are in High Demand,” The Wall Street Journal, May 1, 2015.
124“Million dollar babies: As Silicon Valley fights for talent, universities struggle to hold on to their stars,” The
Economist, April 2, 2016.
125
J. W. Moody, C. M. Beise, A. B. Woszczynski, and M. E. Myers, “Diversity and the information technology workforce:
Barriers and opportunities,” Journal of Computer Information Systems 43 (2003): 63-71.
121
122– 39 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATEStrategy 8: Expand Public–Private Partnerships to Accelerate Advances in AI
Strategy 8 is new in 2019 and reflects thegrowing importance of public-private
partnerships enabling AI R&D.
American leadership in science andengineering research and innovation isrooted in the Nation’s unique governmentuniversity-industry R&D ecosystem. As theAmerican Association of Arts and Sciences
has written, “America’s standing as aninnovation leader” relies upon “establishinga more robust national GovernmentUniversity-Industry
research
126
partnership.” Since the release of the2016 National AI R&D Strategic Plan, theAdministration has amplified this vision ofpromoting “sustained investment in AI R&D
in collaboration with academia, industry,
international partners and allies, and other
non-Federal
entitiestogenerate
technological breakthroughs in AI andrelated technologies and to rapidlytransition those breakthroughs into
capabilities that contribute to U.S.
economic and national security.”1

Advancing the Nation’s AI innovation ecosystem,
spanning government, universities, and industry
 Deep convolutional neural networks have proven to
be a key innovation rooted in AI research. Although
this modeling approach emerged from early Federal
investments in the late 1980s, there were notenough data nor enough computational capabilities
available at the time for neural networks to makeaccurate predictions. Through the combination of arise in big data, today’s data-intensive scientificmethods, and conceptual advances in how to
structure and optimize the networks, neuralnetworks have re-emerged as a useful way to
improve accuracy in AI models. Interactions
between academia and the private sector, including
government funding, in recent years have helped
reduce the error rate in speech recognition systems,
enabling
innovations
suchasreal-timetranslation.126
 Similarly, Federal investments in reinforcement
learning in the 1980s and 1990s—an approachrooted in behavioral psychology that involves
learning to associate behaviors with desired
outcomes—have led to today’s deep learning
systems. Through interactions across sectors,
computers are increasingly learning like humans,
without explicit instruction, and reinforcement
learning is driving progress in self-driving cars andother forms of automation where machines can honeskills through experience. Reinforcement learning
was the key technology underlying AlphaGo, theprogram that defeated the world’s best Go players,which has seen a growing number of victories overprofessional players since 2016.126

Over the last several decades, fundamental
research in information technology
conducted at universities with Federal
funding, as well as in industry, has led tonew, multi-billion-dollar sectors of the
Nation’s economy. 127 Concurrent advancesacross government, universities, andindustry have been mutually reinforcing andhave led to an innovative, vibrant AI sector.
Many of today’s AI systems have beenenabled by the American government-university-industry R&D ecosystem (see sidebar).

Since the release of the 2016 National AI R&D Strategic Plan, additional emphasis has been placed on
the benefits of public-private partnerships. These benefits include strategically leveraging resources,
including facilities, datasets, and expertise, to advance science and engineering innovations;
Restoring the Foundation: The Vital Role of Research in Preserving the American Dream (American Academy of Arts andSciences, Cambridge, MA, 2014); https://www.amacad.org/multimedia/pdfs/publications/researchpapersmonographs/AmericanAcad_RestoringtheFoundation_Brief.pdf .
127
National Research Council Computer Science Telecommunications Board, Continuing Innovation in Information
Technology (The National Academies Press, Washington, D.C., 2012); https://doi.org/10.17226/13427.
126– 40 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

accelerating the transition of these innovations to practice; and enhancing education and training fornext-generation researchers, technicians, and leaders. Government-university-industry R&D
partnerships bring pressing, real-world challenges faced by industry to university researchers, enabling“use-inspired research”; leverage industry expertise to accelerate the transition of open and publishedresearch results into viable products and services in the marketplace for economic growth; and growresearch and workforce capacity by linking university faculty and students with industry
representatives, industry settings, and industry jobs (see sidebar).126,128,129,130 These partnerships build
upon joint engagements among Federal agencies that enable synergies in areas where agencies’
missions intersect. The Nation also benefits from relationships between Federal agencies andinternational funders who can work together to address key challenges of mutual interest across a
range of disciplines.
While there are many structures and mechanisms for public-private partnerships, some common
categories for engagement include:
1. Individual project-based collaborations. These efforts enable engagement by university
researchers with those in other sectors, including Federal agencies, industry, and internationalorganizations, to identify and leverage synergies in areas of mutual interest.
2. Joint programs to advance open, precompetitive, fundamental research. Direct partnerships
among organizations across sectors enable funding and support for open, precompetitive,
fundamental research in areas of mutual interest to the partners. In general, non-Federal partners
contributing research resources receive the same intellectual property rights afforded to the U.S.
Government by the Bayh-Dole Act. 131
3. Collaborations to deploy and enhance research infrastructure. Collaborations among Federal
agencies, industry, and international organizations significantly enhance the potential fordeveloping new and enhancing existing research infrastructure that in turn enables
groundbreaking experimentation by researchers. Partners may offer financial and/or in-kind
support to develop and/or enhance research infrastructure.
4. Collaborations to enhance workforce development including broadening participation. Multisector
partnerships set the foundation for rigorous, engaging, and inspiring instructional materials thatenhance workforce development and diversity in STEM professions.
In each of these cases, leveraging each partner’s strengths for the benefit of all is vitally important toachieving success.

Mathematical Sciences Research Institute report, “Partnerships: A Workshop on Collaborations between the NSF/MPS& Private Foundations,” 2015; http://library.msri.org/msri/Partnerships.pdf.
129Computing Community Consortium, “The Future of Computing Research: Industry-Academic Collaborations,” 2016;
http://cra.org/ccc/wp-content/uploads/sites/2/2016/06/15125-CCC-Industry-Whitepaper-v4-1.pdf.
130Computing Community Consortium, “Evolving Academia/Industry Relations in Computing Research: Interim Report
released by the CCC,” 2019; https://www.cccblog.org/wp-content/uploads/2019/03/Industry-Interim-Report-wfootnotes.pdf.
131
https://history.nih.gov/research/downloads/PL96-517.pdf
128– 41 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

Advances in AI R&D stand to benefit
from all of these types of publicprivate partnerships. Partnerships canpromoteopen,
precompetitive,
fundamental AI R&D; enhance accessto research resources such as
datasets, models, and advanced
computational capabilities; and foster
researcher exchanges and/or joint
appointments between government,
universities, and industry to share AI
R&D expertise. Partnerships can also
promulgate
theinherently
interdisciplinary nature of AI R&D,
which requires convergence between
computer and information science,cognitive science and psychology,
economics
andgame
theory,engineering and control theory, ethics,
linguistics, mathematics and statistics,
andphilosophy to
drive
the
development and evaluation of future
AI systems that are fair, transparent,
and accountable, as well as safe andsecure. Federal agencies are actively
pursuing public-private partnerships
to achieve these goals (see sidebar).
Federal agencies must therefore
continue to pursue and strengthen
public-private partnerships in AI R&D
to drive technology development andeconomic growth by leveraging
investments and expertise in areas ofmutual interest to government,
industry, and academia. In doing so,
the U.S. Government will capitalize ona uniquely American innovation
ecosystem that has transformed
nearly every aspect of the Nation’s
economy and society over the last two
decades through novel information
technologies.127

132
133

Public-private partnerships:
Recent agency R&D programs
A number of agencies have already initiated public-private
partnerships in support of AI R&D: The Defense Innovation Unit (DIU) 132 is a DoD organizationthat solicits commercial solutions capable of addressing
DoD needs. The DIU in turn provides pilot contracts, which
can include hardware, software, or other unique services.
If successful, pilot contracts lead to follow-on contracts
between companies and any DoD entity. A key DIU featureis the rapid pace of the pilot and subsequent contracts.
 NSF and the Partnership on AI, a diverse,
multistakeholder organization working to better
understand AI’s impacts, are partnering to jointly support
high-risk, high-reward research at the intersection of thesocial and technical dimensions of AI.15
 The DHS Science and Technology Directorate’s Silicon
Valley Innovation Program (SVIP) 133 looks to harness
commercial R&D innovation ecosystems across the Nation
and around the world for technologies with government
applications. SVIP employs a streamlined application andpitch process; brings government, entrepreneurs, andindustry together to find cutting-edge solutions; and coinvests in and accelerates transition to market.
 The Department of Health and Human Services (HHS)
piloted the Health Tech Sprint initiative, also known in its
first iteration as “Top Health,” modeled in part after the
Census Bureau's Opportunity Project. This effort created animble framework to public-private collaborations
around bidirectional data links. It piloted new models for
iterating on data release for AI training and testing, and it
developed a voluntary incentivization framework for a
public–private AI ecosystem.
 The HHS Division of Research, Innovation, and Ventures ispart of the Biomedical Advanced Research and Development
Authority at the Office of the Assistant Secretary for
Preparedness and Response. It oversees an accelerator
network and is recruiting a nonprofit partner that can workwith private investors to fund innovative technologies andproducts to solve systemic health security challenges,
with AI applications being one area of interest.
Accelerators will connect startups and other businesses
with product development and business support services.

https://www.diu.mil/
https://www.dhs.gov/science-and-technology/svip– 42 –

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE

Abbreviations
NASA

AFOSR

Air Force Office of Scientific ResearchAI

artificial intelligence

DARPA

Defense Advanced Research
Projects Agency

DHS

Department of Homeland Security

DoD

Department of Defense

DOE

Department of Energy

DOT

Department of Transportation

FDA

Food and Drug Administration

FRVT

Face Recognition Vendor Test

GPS

Global Positioning System

GPUgraphics processing unit

GSA

General Services Administration

HHS

Department of Health and Human
Services

HPC

high-performance computing

IARPAIntelligence Advanced Research
Projects Activity

IEC

International ElectrotechnicalCommission

IEEE

Institute of Electrical andElectronics Engineers

IMPACT

Information Marketplace for Policy
and Analysis of Cyber-risk & Trust
(DHS)

R&D
RFI
STEM

ISO

International Organization forStandardization

SVIP

IT

information technology

IWG

interagency working group

ML
MLAI

machine learning
Machine Learning and Artificial
Intelligence (Subcommittee of the
NSTC)

NCO
NDS
NIFA
NIH
NIST

National Institutes of HealthNational Institute of Standards andTechnology

NITRD

TREC

Networking and Information
Technology Research andDevelopment program
National Library of Medicine (NIH)
National Science Foundation
National Science and Technology
CouncilNational Telecommunications andInformation Administration
Office of the Director of National
Intelligence
Office of Science and Technology
Policyresearch and development
Request for Information
science, technology, engineering,and mathematics
Silicon Valley Innovation Program(DHS)
Text Retrieval Conference

USDA
VA
XAI

U.S. Department of Agriculture
U.S. Department of Veterans Affairs
explainable AI

NLM
NSF
NSTC
NTIA
ODNI
OSTP

– 43 –

National Aeronautics and Space
Administration
National Coordination Office for
NITRD
Naturalistic Driving Study (DOT)
National Institute of Food andAgriculture (USDA)

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATENational Science & Technology Council
Chair

Staff

Kelvin Droegemeier, Director, OSTP

Chloé Kontos, Executive Director, NSTC

Select Committee on Artificial Intelligence
Co-Chairs

Michael Kratsios, Deputy Assistant to the
President for Technology Policy (The WhiteHouse)

France A. Córdova, Director, NSF
Steven Walker, Director, DARPA

Subcommittee on Machine Learning and Artificial Intelligence
Co-Chairs

Lynne Parker, Assistant Director for Artificial
Intelligence, OSTP
James Kurose, Assistant Director, Directorate
for Computer Information Science andEngineering (CISE), NSF

Charles Romine, Director, Information
Technology Laboratory, NIST
Stephen Binkley, Deputy Director, Science
Programs, Office of Science, DOE

Executive Secretary

Faisal D’Souza, NITRD NCO
Subcommittee on Networking & Information Technology Research & Development
Co-Chairs

Kamie Roberts, Director, NITRD NCO

James Kurose, Assistant Director, CISE, NSF
Executive Secretary

Nekeia Butler, NITRD NCO
Artificial Intelligence Research & Development Interagency Working Group
Co-Chairs

Jeff Alstott, Program Manager, IARPA Office of
the Director of National Intelligence (ODNI)Henry Kautz, Division Director, CISE Divisionof Information and Intelligent Systems, NSF
Staff

Faisal D’Souza, Technical Coordinator, NITRD NCO
Strategic Plan Writing Team

Jeff Alstott, IARPA
Gil Alterovitz, VA
Sameer Antani, NIHCharlotte Baer, NIFA, USDA
Daniel Clouse, ODNI
Faisal D’Souza, NITRD NCO
Kimberly Ferguson-Walter,
U.S. Navy

Michael Garris, NIST
Erwin Gianchandani, NSF
Ross Gillfillan, OSTP
Travis Hall, NTIA
Meghan Houghton, NSF
Henry Kautz, NSF
Erin Kenneally, DHS
David Kuehn, DOT– 44 –

James Kurose, NSF
James Lawton, AFOSR
Steven Lee, DOE
Aaron Mannes, DHS
Lynne Parker, OSTP
Dinesh Patwardhan, FDA
Elham Tabassi, NIST

THE NATIONAL ARTIFICIAL INTELLIGENCE R&D STRATEGIC PLAN: 2019 UPDATE
About the National Science and Technology Council
The National Science and Technology Council (NSTC) is the principal means by which the Executive Branch coordinates science andtechnology policy across the diverse entities that make up the Federal research and development enterprise. A primary objectiveof the NSTC is to ensure that science and technology policy decisions and programs are consistent with the President’s stated goals.
The NSTC prepares research and development strategies that are coordinated across Federal agencies aimed at accomplishingmultiple national goals. The work of the NSTC is organized under committees that oversee subcommittees and working groups
focused on different aspects of science and technology. More information is available at https://www.whitehouse.gov/ostp/nstc.
About the Office of Science and Technology Policy
The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology Policy, Organization,
and Priorities Act of 1976 to provide the President and others within the Executive Office of the President with advice on thescientific, engineering, and technological aspects of the economy, national security, homeland security, health, foreign relations,the environment, and the technological recovery and use of resources, among other topics. OSTP leads interagency science andtechnology policy coordination efforts, assists the Office of Management and Budget with an annual review and analysis of Federal
research and development (R&D) in budgets, and serves as a source of scientific and technological analysis and judgment for the
President with respect to major policies, plans, and programs of the Federal Government. More information is available athttps://www.whitehouse.gov/ostp.
About the Select Committee on Artificial Intelligence
The Select Committee on Artificial Intelligence (AI) advises and assists the NSTC to improve the overall effectiveness and productivityof Federal R&D efforts related to AI to ensure continued U.S. leadership in this field. It addresses national and international policy
matters that cut across agency boundaries, and it provides formal mechanisms for interagency policy coordination and development
for Federal AI R&D activities, including those related to autonomous systems, biometric identification, computer vision, humancomputer interactions, machine learning, natural language processing, and robotics. It also advises the Executive Office of the President
on interagency AI R&D priorities; works to create balanced and comprehensive AI R&D programs and partnerships; leverages Federal
data and computational resources across department and agency missions; and supports a technical, national AI workforce.
About the Subcommittee on Machine Learning and Artificial Intelligence
The Machine Learning and Artificial Intelligence (MLAI) Subcommittee monitors the state of the art in machine learning (ML) andartificial intelligence within the Federal Government, in the private sector, and internationally to watch for the arrival of important
technology milestones in the development of AI, to coordinate the use of and foster the sharing of knowledge and best practices
about ML and AI by the Federal Government, and to consult in the development of Federal MLAI R&D priorities. The MLAI
Subcommittee reports to the Committee on Technology and the Select Committee on AI. The MLAI Subcommittee also coordinates
AI taskings with the Artificial Intelligence Research & Development Interagency Working Group (see below).
About the Subcommittee on Networking & Information Technology Research & Development
The Networking and Information Technology Research and Development (NITRD) Program is the Nation’s primary source ofFederally funded work on pioneering information technologies (IT) in computing, networking, and software. The NITRD
Subcommittee guides the multiagency NITRD Program in its work to provide the R&D foundations for assuring continued U.S.
technological leadership and meeting the needs of the Nation for advanced IT. It reports to the NSTC Committee on Science andTechnology Enterprise. The Subcommittee is supported by the interagency working groups that report to it and by its National
Coordination Office. More information is available at https://www.nitrd.gov/about/.
About the Artificial Intelligence Research & Development Interagency Working GroupThe NITRD AI R&D Interagency Working Group (IWG) coordinates Federal R&D in AI; it also supports and coordinates activities
tasked by the Select Committee on AI and the NSTC Subcommittee on Machine Learning and Artificial Intelligence. This vital work
promotes U.S. leadership and global competitiveness in AI R&D. The NITRD AI R&D IWG spearheaded the update of this National
Artificial Intelligence Research and Development Strategic Plan. More information is available at https://www.nitrd.gov/groups/AI.
About this Document
This document includes the original text from the 2016 National AI R&D Strategic Plan with updates prepared in 2019 following
Administration and interagency evaluation of the 2016 Plan and of community responses to a Request for Information on updating
the Plan. The 2016 strategies were broadly determined to be valid going forward with some reemphases and with a call for a new
strategy on Private-Public Partnerships in AI. A shaded call-out box has been inserted at the top of each strategy to highlight
updated imperatives and/or new focus areas. The 2019 update adds an entirely new Strategy 8 on Private-Public Partnerships in AI.
Copyright Information
This document is a work of the United States Government and is in the public domain (see 17 U.S.C. §105). It may be freely
distributed, copied, and translated, with acknowledgment to OSTP; requests to use any images must be made to OSTP.
Published in the United States of America, 2019.