The National Science and Technology Council (NSTC) and the Subcommittee on Networking and Information Technology Research and Development (NITRD) issued the report, The National Artificial Intelligence Research and Development Strategic Plan to establish a set of objectives for federally-funded AI research conducted within and outside the government, including academic research. The goal of this research is to produce new AI knowledge and technologies that benefit society while minimizing negative impacts. This report fulfills these goals by outlining seven strategies and two recommendations for federal AI research and development (R&D) in the context of fourteen different industry applications.
This report prioritizes strategies for future federal investment in research areas where private investment is unlikely. Benefits are described in terms of increased economic prosperity, improved quality of life, and strengthened national security as applied to particular industry applications expected to benefit from advances in AI. The report states that increased economic prosperity can be realized through AI developments in applications that include manufacturing, logistics, finance, transportation, agriculture, marketing, communication, and science and technology; improved educational opportunities and quality of life will come from AI contributions to education, medicine, law and personal services; and enhanced national and homeland security will be achieved through AI advances applied to security and law enforcement and safety and prediction.
The R&D strategies included in the report are organized according to Basic R&D areas of AI (identified in strategies 1 and 2) and Cross-Cutting Foundations of AI (strategies 3 through 7). Cross-cutting foundations include areas of research whose discoveries are applicable throughout the field of AI, while basic R&D areas of AI are more narrowly focused and are intended to build on the cross-cutting strategies.
The strategies proposed for federal AI R&D that apply to basic R&D include the following:
Strategy 1: Make Long-Term Investments in AI Research
While the report notes that an important component of long-term research is incremental research with predictable outcomes, it argues that long-term sustained investments in high-risk research can lead to high-reward payoffs. Areas with potential long-term payoffs include:
- Development of more-advanced machine learning algorithms that can identify the useful information hidden in big data;
- Enhancements in how AI systems detect, classify, identify, and recognize objects;
- Improved understanding of the theoretical capabilities and limitations for AI and the extent to which human-like solutions are possible with AI algorithms;
- Research on “general-purpose” AI that exhibits the flexibility and versatility of human intelligence in a broad range of cognitive domains including learning, language, perception, reasoning, creativity, and planning;
- Scalable AI systems that collaborate effectively with each other and with humans to achieve results not possible with a single system;
- Fostering research on how AI can communicate and operate in a more humanlike fashion;
- Robots that are more capable, reliable, and easier to use;
- Advanced hardware for improved and faster AI operations; and
- AI software that advances hardware performance.
Strategy 2: Develop Effective Methods for Human-AI Collaboration
As AI is a supplement to human activity, the Report indicates that best practices for AI-human interaction must be designed to avoid excessive complexity and to address the recognized effects of using automated systems, such as undertrust (not fully using the automation), or overtrust (over-utilizing automation, leading to complacency). These aims can be achieved specifically by:
- Seeking new algorithms for AI that enable intuitive interaction with users and seamless machine-human collaborations;
- Developing techniques for AI that can improve the thinking and functioning of humans;
- Developing techniques for how AI effectively presents information to users in real-time in formats that are easy to interpret; and
- Developing more effective language processing systems to allow AI machines to interpret written or verbal commands regardless of the clarity of the commands.
The strategies for Cross-Cutting Foundations of AI R&D include:
Strategy 3: Understand and Address the Ethical, Legal, and Societal Implications of AI
The Report points out that research needs to account for the ethical, legal, and social implications of AI, as well as developing methods for AI that align with ethical, legal, and social principles. These aims can be achieved specifically by:
- Improving fairness, transparency, and accountability in AI design to avoid bias;
- Building ethical AI functions that reflect an appropriate value system, developed through examples that indicate preferred behavior when presented with difficult moral issues or with conflicting values; and
- Designing computer architectures that incorporate ethical reasoning.
Strategy 4: Ensure the Safety and Security of AI Systems
The Report states that further research is needed to create AI that is reliable, transparent, and secure. This aim can be achieved specifically by:
- Improving how systems that include AI will explain their reasoning and decisions to users;
- Building trust with users by creating accurate, reliable systems with informative, user-friendly interfaces;
- Improving methods for AI systems’ verification (establishing that a system meets formal specifications) and validation (establishing that a system meets the users’ operational needs);
- Increasing security against cyber-attacks on or by AI systems; and
- Building long-term AI safety by maintaining alignment with human values.
Strategy 5: Develop Shared Public Datasets and Environments for AI Training and Testing
The Report points out that additional 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/training resources. According to the Report, these aims can be achieved specifically by:
- Developing and making available a wide variety of datasets to meet the needs of AI interests and applications;
- Making training and testing resources responsive to commercial and public interests; and
- Developing and distributing software libraries and toolkits.
Strategy 6: Measure and Evaluate AI Technologies through Standards and Benchmarks
The Report states that establishment and adoption of standards, benchmarks, and testing methods are essential for guiding and promoting R&D of AI technologies. These aims can be achieved specifically by:
- Developing requirements, specifications, guidelines, or characteristics that can be used consistently to ensure that AI technologies meet critical objectives for functionality and interoperability, and that the technologies perform reliably and safely;
- Establishing AI technology quantitative benchmarks to objectively measure AI accuracy, complexity, operator trust and competency, risk, uncertainty, transparency, unintended bias, performance, and economic impact;
- Increasing the availability of AI testbeds across all aspects of AI, including providing limited access to sensitive information for improving AI systems designed to protect confidential data; and
- Engaging the AI community (e.g., governments, industry, and academia) in developing standards and benchmarks.
Strategy 7: Better Understand the National AI R&D Workforce Needs
The Report’s list of strategies concludes by reporting that AI experts are in short supply, with demand for these people expected to continually escalate. Data is needed to characterize the current state of the AI R&D workforce, including the needs of academia, government, and industry.
NITRD clarifies its intent in issuing this report by stating these priorities as a supplement, not a replacement, for any pre-existing federal research agendas. Furthermore, while this report does not explicitly address the appropriate scope or application of AI technologies, NITRD recognizes the necessity of addressing these issues and identifies relevant reports to that effect.
The report concludes by offering two recommendations to the Federal government for strengthening and promoting the success of this strategic plan:
- Develop an AI R&D implementation framework to identify science and technology opportunities and support effective coordination of AI R&D investments; and
- Study the national landscape for creating and sustaining a healthy AI R&D workforce.