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Executive Summary Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI.
On May 3, 2016, the Administration announced the formation of a new NSTC Subcommittee on Machine Learning and Artificial intelligence, to help coordinate Federal activity in AI.1 This Subcommittee, on June 15, 2016, directed the Subcommittee on Networking and Information Technology Research and Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic priorities for AI R&D, with particular attention on areas that industry is unlikely to address.
This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federally-funded AI research, both research occurring within the government as well as Federally-funded research occurring outside of government, such as in academia. The ultimate goal of this research is to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts. To achieve this goal, this AI R&D Strategic Plan identifies the following priorities for Federally-funded AI research:
Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI.
Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems.
Strategy 3: Understand and address the ethical, legal, and societal implications of AI. We expect AI technologies to behave according to the formal and informal norms to which we hold our fellow humans. Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals.
Strategy 4: Ensure the safety and security of AI systems. Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely, in a controlled, well-defined, and well-understood manner. Further progress in research is needed to address this challenge of creating AI systems that are reliable, dependable, and trustworthy.
Strategy 5: Develop shared public datasets and environments for AI training and testing. The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources.
Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and
1 E. Felten, “Preparing for the Future of Artificial Intelligence,” White House Office of Science and Technology Policy blog, May 5, 2016, https://www.whitehouse.gov/blog/2016/05/03/preparing-future-artificial-intelligence.
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evaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques.
Strategy 7: Better understand the national AI R&D workforce needs. Advances in AI will require a strong community of AI researchers. An improved understanding of current and future R&D workforce demands in AI is needed to help ensure that sufficient AI experts are available to address the strategic R&D areas outlined in this plan.
The AI R&D Strategic Plan closes with two recommendations:
Recommendation 1: Develop an AI R&D implementation framework to identify S&T opportunities and support effective coordination of AI R&D investments, consistent with Strategies 1-6 of this plan.
Recommendation 2: Study the national landscape for creating and sustaining a healthy AI R&D workforce, consistent with Strategy 7 of this plan.
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Introduction
Purpose of the National AI R&D Strategic Plan
In 1956, researchers in computer science from across the United States met at Dartmouth College in New Hampshire to discuss seminal ideas on an emerging branch of computing called artificial intelligence or AI. They imagined a world in which “machines use language, form abstractions and concepts, solve the kinds of problems now reserved for humans, and improve themselves”.2 This historic meeting set the stage for decades of government and industry research in AI, including advances in perception, automated reasoning/planning, cognitive systems, machine learning, natural language processing, robotics, and related fields. Today, these research advances have resulted in new sectors of the economy that are impacting our everyday lives, from mapping technologies to voice-assisted smart phones, to handwriting recognition for mail delivery, to financial trading, to smart logistics, to spam filtering, to language translation, and more. AI advances are also providing great benefits to our social wellbeing in areas such as precision medicine, environmental sustainability, education, and public welfare.3
The increased prominence of AI approaches over the past 25 years has been boosted in large part by the adoption of statistical and probabilistic methods, the availability of large amounts of data, and increased computer processing power. Over the past decade, the AI subfield of machine learning, which enables computers to learn from experience or examples, has demonstrated increasingly accurate results, causing much excitement about the near-term prospects of AI. While recent attention has been paid to the importance of statistical approaches such as deep learning,4 impactful AI advances have also been made in a wide variety of other areas, such as perception, natural language processing, formal logics, knowledge representations, robotics, control theory, cognitive system architectures, search and optimization techniques, and many others.
The recent accomplishments of AI have generated important questions on the ultimate direction and implications of these technologies: What are the important scientific and technological gaps in current AI technologies? What new AI advances would provide positive, needed economic and societal impacts? How can AI technologies continue to be used safely and beneficially? How can AI systems be designed to align with ethical, legal, and societal principles? What are the implications of these advancements for the AI R&D workforce?
The landscape for AI R&D is becoming increasingly complex. While past and present investments by the U.S. Government have led to groundbreaking approaches to AI, other sectors have also become significant contributors to AI, including a wide range of industries and non-profit organizations. This investment landscape raises major questions about the appropriate role of Federal investments in the development of AI technologies. What are the right priorities for Federal investments in AI, especially regarding areas and timeframes where industry is unlikely to invest? Are there opportunities for industrial and international R&D collaborations that advance U.S. priorities?
2 J. McCarthy, M. L. Minsky, N. Rochester, C. E. Shannon, “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence,” August 31, 1955, http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html. 3 See presentations from subject matter experts at Artificial Intelligence for Social Good workshop, June 7, 2016, http://cra.org/ccc/events/ai-social-good/. 4 Deep learning refers to a general family of methods that use multi-layered neural networks; these methods have supported rapid progress on tasks once believed to be incapable of automation.
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In 2015, the U.S. Government’s investment in unclassified R&D in AI-related technologies was approximately $1.1 billion. Although these investments have led to important new science and technologies, there is opportunity for further coordination across the Federal government so that these investments can achieve their full potential.5
Recognizing the transformative effects of AI, in May 2016, the White House Office of Science and Technology Policy (OSTP) announced a new interagency working group to explore the benefits and risks of AI.6 OSTP also announced a series of four workshops, held in the May-July 2016 time frame, aimed at spurring public dialogue on AI, and identifying the challenges and opportunities it entails. The outcomes of the workshops are part of a companion public report, Preparing for the Future of Artificial Intelligence, released in conjunction with this plan.
In June 2016, the new NSTC Subcommittee on Machine Learning and Artificial Intelligence—which is chartered to stay abreast of advances in AI within the Federal government, the private sector, and internationally, and to help coordinate Federal activities in AI—tasked the NITRD National Coordination Office (NCO) to create the National Artificial Intelligence Research and Development Strategic Plan. The Subcommittee directed that this plan should convey a clear set of R&D priorities that address strategic research goals, focus Federal investments on those areas in which industry is unlikely to invest, and address the need to expand and sustain the pipeline of AI R&D talent.
Input to this AI R&D Strategic Plan has come from a wide range of sources, including Federal agencies, public discussions at AI-related meetings, an OMB data call across all Federal agencies who invest in IT-related R&D, the OSTP Request for Information (RFI) that solicited public input about how America can best prepare for an AI future,7 and information from open publications on AI.
This plan makes several assumptions about the future of AI.8 First, it assumes that AI technologies will continue to grow in sophistication and ubiquity, thanks to AI R&D investments by government and industry. Second, this plan assumes that the impact of AI on society will continue to increase, including on employment, education, public safety, and national security, as well as the impact on U.S. economic growth. Third, it assumes that industry investment in AI will continue to grow, as recent commercial successes have increased the perceived returns on investment in R&D. At the same time, this plan assumes that some important areas of research are unlikely to receive sufficient investment by industry, as they are subject to the typical underinvestment problem surrounding public goods. Lastly, this plan assumes that the demand for AI expertise will continue to grow within industry, academia, and government, leading to public and private workforce pressures.
Other R&D strategic plans and initiatives of relevance to this AI R&D Strategic Plan include the Federal Big Data Research and Development Strategic Plan,9 the Federal Cybersecurity Research and Development Strategic Plan,10 the National Privacy Research Strategy,11 the National Nanotechnology
5 While NITRD has several working groups that touch on aspects of AI, there is no current NITRD working group focused specifically on coordinating inter-agency AI R&D investments and activities. 6 E. Felten, “Preparing for the Future of Artificial Intelligence,” White House Office of Science and Technology Policy blog, May 5, 2016, https://www.whitehouse.gov/blog/2016/05/03/preparing-future-artificial-intelligence. 7 WH/OSTP RFI blog post: https://www.whitehouse.gov/blog/2016/06/27/how-prepare-future-artificial-intelligence. 8 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. 9 Federal Big Data Research and Development Strategic Plan, May 2016, https://www.nitrd.gov/PUBS/bigdatardstrategicplan.pdf. 10 Federal Cybersecurity Research and Development Strategic Plan, February 2016, https://www.nitrd.gov/cybersecurity/publications/2016_Federal_Cybersecurity_Research_and_Development_Strategic_Plan.pdf.
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Initiative Strategic Plan,12 the National Strategic Computing Initiative,13 the Brain Research through Advancing Innovative Neurotechnologies Initiative,14 and the National Robotics Initiative.15 Additional strategic R&D plans and strategic frameworks are in the developmental stages, addressing certain sub-fields of AI, including video and image analytics, health information technology, and robotics and intelligent systems. These additional plans and frameworks will provide synergistic recommendations that complement and expand upon this AI R&D Strategic Plan.
Desired Outcome
This AI R&D Strategic Plan looks beyond near-term AI capabilities toward longer-term transformational impacts of AI on society and the world. Recent advances in AI have led to significant optimism about the potential for AI, resulting in strong industry growth and commercialization of AI approaches. However, while the Federal government can leverage industrial investments in AI, many application areas and long-term research challenges will not have clear near-term profit drivers, and thus may not be significantly addressed by industry. The Federal government is the primary source of funding for long-term, high-risk research initiatives, as well as near-term developmental work to achieve department- or agency-specific requirements or to address important societal issues that private industry does not pursue. The Federal government should therefore emphasize AI investments in areas of strong societal importance that are not aimed at consumer markets—areas such as AI for public health, urban systems and smart communities, social welfare, criminal justice, environmental sustainability, and national security, as well as long-term research that accelerates the production of AI knowledge and technologies.
A coordinated R&D effort in AI across the Federal government will increase the positive impact of these technologies, and provide policymakers with the knowledge needed to address complex policy challenges related to the use of AI. A coordinated approach, moreover, will help the United States capitalize on the full potential of AI technologies for the betterment of society.
This AI R&D Strategic Plan defines a high-level framework that can be used to identify scientific and technological gaps in AI and track the Federal R&D investments that are designed to fill those gaps. The AI R&D Strategic Plan identifies strategic priorities for both near-term and long-term support of AI that address important technical and societal challenges. The AI R&D Strategic Plan, however, does not define specific research agendas for individual Federal agencies. Instead, it sets objectives for the Executive Branch, within which agencies may pursue priorities consistent with their missions, capabilities, authorities, and budgets, so that the overall research portfolio is consistent with the AI R&D Strategic Plan.
The AI R&D Strategic Plan also does not set policy on the research or use of AI technologies nor does it explore the broader concerns about the potential influence of AI on jobs and the economy. While these topics are critically important to the Nation, they are discussed in the Council of Economic Advisors report entitled “Is This Time Different? The Opportunities and Challenges of Artificial Intelligence.”8 The
11 National Privacy Research Strategy, June 2016, https://www.nitrd.gov/PUBS/NationalPrivacyResearchStrategy.pdf. 12 National Nanotechnology Initiative Strategic Plan, February 2014, http://www.nano.gov/sites/default/files/pub_resource/2014_nni_strategic_plan.pdf. 13 National Strategic Computing Initiative Strategic Plan, July 2016, https://www.whitehouse.gov/sites/whitehouse.gov/files/images/NSCI%20Strategic%20Plan.pdf 14 Brain Research through Advancing Innovative Neurotechnologies (BRAIN), April 2013, https://www.whitehouse.gov/BRAIN. 15 National Robotics Initiative, June 2011, https://www.whitehouse.gov/blog/2011/06/24/developing-next-generation-robots.
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AI R&D Strategic Plan focuses on the R&D investments needed to help define and advance policies that ensure the responsible, safe, and beneficial use of AI.
A Vision for Advancing our National Priorities with AI Driving this AI R&D Strategic Plan is a hopeful vision of a future world in which AI is safely used for significant benefit to all members of society. Further progress in AI could enhance wellbeing in nearly all sectors of society,16 potentially leading to advancements in national priorities, including increased economic prosperity, improved quality of life, and strengthened national security. Examples of such potential benefits include:
Increased economic prosperity: New products and services can create new markets, and improve the
quality and efficiency of existing goods and services across multiple industries. More efficient logistics and supply chains are being created through expert decision systems.17 Products can be transported more effectively through vision-based driver-assist and automated/robotic systems.18 Manufacturing can be improved through new methods for controlling fabrication processes and scheduling work flows.19
How is this increased economic prosperity achieved?
Manufacturing: Technological advances can lead to a new industrial revolution in manufacturing, including the entire engineering product life cycle. Increased used of robotics could enable manufacturing to move back onshore.20 AI can accelerate production capabilities through more reliable demand forecasting, increased flexibility in operations and the supply chain, and better prediction of the impacts of change to manufacturing operations. AI can create smarter, faster, cheaper, and more environmentally-friendly production processes that can increase worker productivity, improve product quality, lower costs, and improve worker health and safety.21 Machine learning algorithms can improve the scheduling of manufacturing processes and reduce inventory requirements.22 Consumers can benefit from access to what is now commercial-grade 3-D printing.23
Logistics: Private-sector manufacturers and shippers can use AI to improve supply-chain management through adaptive scheduling and routing.24 Supply chains can become more robust
16 See the “2016 Report of the One Hundred Year Study on Artificial Intelligence”, which focuses on the anticipated uses and impacts of AI in the year 2030, https://ai100.stanford.edu/2016-report. 17 E. W. T. Ngai, S. Peng, P. Alexander, and K. K. L. Moon, “Decision support and intelligent systems in the textile and apparel supply chain: An academic review of research articles,” Expert Systems with Applications, 41(2014): 81-91. 18 J. Fishelson, D. Freckleton, and K. Heaslip,”Evaluation of automated electric transportation deployment strategies: integrated against isolated,” IET Intelligent Transport Systems, 7 (2013): 337-344. 19 C. H. Dagli, ed., Artificial neural networks for intelligent manufacturing, Springer Science & Business Media, 2012. 20 D. W. Brin, “Robotics on the Rise”, MHI Solutions, Q3, 2013, https://dinahwbrin.files.wordpress.com/2013/07/mhi-solutions-robotics.pdf. 21 “Robotics Challenge Aims to Enhance Worker Safety, Improve EM Cleanup”, DOE Office of Environmental Management, August 31, 2016, http://energy.gov/em/articles/robotics-challenge-aims-enhance-worker-safety-improve-em-cleanup-other-em-events-set. 22 M. J. Shaw, S. Park, and N. Raman, “Intelligent scheduling with machine learning capabilities: the induction of scheduling knowledge,” IIE transactions, 24.2 (1992): 156-168. 23 H. Lipson and M. Kurman, Fabricated: The new world of 3D printing, John Wiley & Sons, 2013. 24 M. S. Fox, M. Barbuceanu, and R. Teigen, “Agent-oriented supply-chain management,” International Journal of Flexible Manufacturing Systems, 12 (2000): 165-188.
