“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 are progressing 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.”

So says the recently updated National Artificial Intelligence Research And Development Strategic Plan. Released jointly by the White House's Office of Science and Technology Policy, the National Science Foundation, and and an array of federal agencies, the updated plan refreshes the 2016 National AI R&D Strategic Plan, reevaluating federal priorities for AI R&D investments and identifying eight strategic priority areas for federal investment in AI research and innovation, seven of which remain unchanged from the strategic plan released in 2016. New to the 2019 plan is an imperative to expand public and private partnerships to enhance federal investments and activities in support of AI.

The eight strategies are: 

    •    Make long-term investments in AI research, prioritizing next-generation applications that can help "drive discovery and insight and enable the United States to remain a world leader in AI."

    •    Develop more effective strategies for human-AI collaboration, with a focus on AI systems that "effectively complement and augment human capabilities."

    •    Understand and address the "ethical, legal, and societal implications of AI" and how they can be addressed through the technology.

    •    Work to ensure AI systems' safety and security, and spread knowledge of "how to design AI systems that are reliable, dependable, safe, and trustworthy."

    •    Create high-quality, shared public datasets and environments for AI training and testing.

    •    Measure and evaluate AI with standards and benchmarks, eventually arriving at a broad set of evaluative techniques, including technical standards and benchmarks.

    •    Better understand the workforce needs of AI researchers and developers nationwide, and work strategically to foster an AI-ready workforce.

    •    Expand existing public-private partnerships, and create new ones to speed advances in AI, promoting opportunities for sustained investment R&D and for "transitioning advances into practical capabilities, in collaboration with academia, industry, international partners, and other non-Federal entities.”

In addition, the plan emphasizes the need to ensure that data used to power AI is trustworthy and that the algorithms used to process it are understandable – not least in healthcare.

"A key research challenge is increasing the 'explainability' or ''transparency' of AI," according to the report. "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 as decision-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 their results to users.”