Trump’s executive order to prioritize federal research in AI / by Admin

The trajectories of neural ordinary differential equations

The trajectories of neural ordinary differential equations

How to navigate the executive order and find funding…from a startup perspective



The president signed an executive order this week defining a national plan to boost government investment into research and development in AI (artificial intelligence) technology. 

Trump’s executive order states that the “US must drive technological breakthroughs in AI”, and highlights the need to “drive development of appropriate technical standards and reduce barriers to the safe testing and deployment of AI technologies”. 

Continued American leadership in AI is of paramount importance to maintaining the economic and national security of the United States and to shaping the global evolution of AI in a manner consistent with our Nation’s values, policies, and priorities”

The executive order comes at a pivotal time in the history of AI development, with many nations competing for the technological edge in AL and Machine Learning; an edge that has far-reaching implications for new, disruptive technology to drive innovation in sectors ranging from Healthcare IT to the military.  The executive order acknowledges that funding prioritization and interagency focus on artificial intelligence technology is needed for the US to take stronger leadership in an increasingly competitive global playing field for AI. 

 This focus on funding the development of new technology is vitally important for the US right now-as both a prioritization of national security and a catalyst for economic growth. US government R&D funding waters the seeds of new technologies under development by startups all over the United States, and encourages teaming with experienced federal system integrators. This fresh wave of US investment into artificial intelligence translates directly to increased investment dollars flowing into existing programs, and the creation of new sources of R&D funding. 


Interagency Select Committee on Artificial Intelligence

 Under the guidance of the newly formed Interagency Select Committee on Artificial Intelligence new opportunities to patriciate in research programs will flourish. The committee formed inside the National Science and Technology Council, and will advise the White House on government-wide AI research and development priorities. Working to establish partnerships between government, private sector and research organizations, the work of this committee should be a focus for startups looking for funding and technology alignment. 

The committee will be chaired by the Office of Science and Technology, along with the National Science Foundation  and DARPA, the Pentagon’s research arm. Initial committee membership will include:

  • Walter Copan: Commerce Department Undersecretary for Standards and Technology and Director of the National Institute of Standards and Technology

 

In order to foster a whole-of-government discussion, on June 27, 2018, the newly formed Select Committee on AI approved the formation of a new Networking and Information Technology Research and Development Program (NITRD) Artificial Intelligence Interagency Working Group. The Charter of the NITRD AI R&D IWG has members from subcommittees representing AI, Big Data, and Intelligent Robotics and Autonomous Systems.  Working group leadership is provided by the Co-chairs: Jeff Alstott, PH.D., Program Manager, Intelligence Advanced Research Projects Activity (IARPA), and Henry Kautz, Division Director, CISE/IIS, National Science Foundation.


Startup Opportunities 

“ Artificial Intelligence will affect the missions of nearly all executive departments and agencies”. 


The agency resource section of the NITRD Interagency Working Group websiteis a springboard for startups looking for R&D funding opportunities, with consolidated access to opportunities at multiple agencies. Among that list is program from DARPA that stands out as well funded and flexible, the  DARPA “AI Next” campaign.  A powerhouse of AI focused R&D funding with over 20 actively funded initiatives, over 60 existing DARPA programs are looking for an infusion of fresh,innovative startup technology. DARPA announced in September 2018 a multi-year investment of more than $2 billion in new and existing programs with “AI Next”, and with the latest executive order, I see the possibility of an expansion into more programs. 

 

In addition to new and existing DARPA research, a key component of the AI Next campaign is DARPA’s Artificial Intelligence Exploration (AIE) program, which was first announced in July 2018. AIE constitutes a series of high-risk, high payoff projects where researchers will work to establish the feasibility of new AI concepts within 18 months of award. Leveraging streamlined contracting procedures and funding mechanisms will enable these efforts to move from proposal to project kick-off within three months of an opportunity announcement. Forthcoming AIE Opportunities will be published on the FedBizOpps website under Program Announcement DARPA-PA-18-02.

Article Notes and Further Reading

The graphic I used in this blog post was generated from a fascinating article written by by Karen Hao on December 12, 2018, titled “A radical new neural network design could overcome big challenges in AI” from www.technologyreview.com. Read about it here: https://www.technologyreview.com/s/612561/a-radical-new-neural-network-design-could-overcome-big-challenges-in-ai/

Karen Hao is the artificial intelligence reporter for MIT Technology Review. In particular she covers the ethics and social impact of the technology as well as its applications for social good. She also writes the AI newsletter, the Algorithm, which thoughtfully examines the field’s latest news and research. Prior to joining the publication, she was a reporter and data scientist at Quartz and an application engineer at the first startup to spin out of Google X.

The graphic itself represents the plotted trajectories of neural ordinary differential equations, based on work done by David Duvenaud, AI researcher at the University of Toronto. The paper that generated this fascinating research is here.