The Department of Defense is funding projects leveraging artificial intelligence (AI) and machine learning (ML), computer vision at record levels for FY2020. . Analysis of the DOD’s Research, Development, Test, and Enhancement (RDT&E) and Procurement budget requests for FY 2020 reveals that agencies have requested nearly $2.4B for programs with an artificial intelligence or machine learning component. Increasingly, funding for AI is spread across a wider array of agencies at the DOD.
New Funding Requests
Neither the Defense Security Service nor the Chemical and Biological Defense Program, requested funding related to AI in fiscal 2018 or 2019. For fiscal 2020, however, they requested $2.4M and $3.5M, respectively, illustrating the growing viability of the technology and the interest in it developing across the DOD.
Total Proposed Funding FY2020
Comparing FY2018 and FY202, we see an increase in funding for existing projects, combined with new funding requests, increasing the overall funding 30% over FY2018.
Top 5 Defense Agencies by AI/ML in the FY 2020 Budget Request
DARPA leads the way for federal funding for research, development, and Enhancement (RDT&E) programs with an artificial intelligence or machine learning component. For more than five decades, DARPA has been a leader in generating groundbreaking research and development (R&D) that facilitated the advancement and application of rule-based and statistical-learning based AI technologies. Today, DARPA continues to lead innovation in AI research as it funds a broad portfolio of R&D programs, ranging from basic research to advanced technology development. DARPA believes this future, where systems are capable of acquiring new knowledge through generative contextual and explanatory models, will be realized upon the development and application of “Third Wave” AI technologies.
DARPA announced in September 2018 a multi-year investment of more than $2 billion in new and existing programs called the “AI Next” campaign. Key areas of the campaign include automating critical DoD business processes, such as security clearance vetting or accrediting software systems for operational deployment; improving the robustness and reliability of AI systems; enhancing the security and resiliency of machine learning and AI technologies; reducing power, data, and performance inefficiencies; and pioneering the next generation of AI algorithms and applications, such as “explainability” and common sense reasoning.
Source: FY2020 DOD Budget Request
Funding Analysis
Defense Advanced Research Projects Agency (DARPA) and the Office of the Secretary of Defense (OSD) come in at the top of the requested budgets. Much of DOD’s AI-related work is focused on R&D, it comes as no surprise to see DARPA at the top of the funding requests. OSD funds research under the Under Secretary of Defense for Research and Engineering. In turn, the USD R&E seeds programs across the defense research establishment, including the Defense Innovation Unit (DIU). The Defense Information Systems Agency’s requested funding for AI/ML grew the most in FY 2020, rising more than 500% over the total requested in FY 2018 thanks to the addition of the Joint AI Center.
Top Programs
DARPA Artificial Intelligence and Human-Machine Symbiosis ($161M): This project develops technologies that enable machines to function as trusted partners for human operators. Of particular interest are systems that can understand human speech and extract information contained in diverse media.
OSD Algorithmic Warfare Cross Functional Teams ($221.2): This program funds Project Maven, DOD’s rapid AI fielding program to augment and automate Processing, Exploitation and Dissemination (PED) for Full Motion Video (FMV), Tactical Unmanned Aerial Vehicles (TUAVs), Medium Altitude, High Altitude, and Wide Area Motion Imagery (WAMI) Intelligence, Surveillance and Reconnaissance (ISR). Project Maven is DOD’s pathfinder AI initiative.
DTRA Counter Improvised-Threat Demonstration, Prototype Development, and Testing ($104M): This project delivers counter-threat solutions to U.S. Joint Forces supporting contingency operations and deployed forces. FY 2020 funding supports the development of autonomous unmanned vehicles countering adversary improvised threats.
DISA Joint Artificial Intelligence Center (JAIC) ($209M): Established to preserve and expand U.S. military advantage in support of the 2018 National Defense Strategy, the JAIC is intended to accelerate the delivery of AI enabled capabilities, to scale-up the department-wide impact of AI, and to synchronize DOD’s AI activities for the Joint Force.
US SOCOM AC/MC-130J/Integrated Tactical Mission Systems (ITMS) ($29M): The ITMS lightens aircrew workloads by merging Special Operation Forces mission system data with aircraft flight information controls. Capabilities include, but are not limited to, automated route re-planning, tactical flight management, integrated aircraft defensive systems, defensive countermeasures, and embedded training. FY 2020 funding will continue the automation of the TMS, including the application of machine learning and artificial intelligence.
Key Findings
Numerous AI use cases and pilot projects are underway in most federal agencies, and several agencies have AI projects in production or with Authority-to-Operate.
Agency applications of AI based on machine learning technology is accelerating, focusing on cyber offense and defense, robotics and autonomy, border security and health care.
Adjacent technology advancements will have significant AI adoption implications. IT infrastructure modernization will pave the way for AI deployments, while cloud applications and high performance computing are seen as critical enablers.
Much of DOD’s AI and machine learning work continues to occur within defense laboratories, giving contractors with R&D experience a competitive advantage for contract awards. Industry should expect DOD to lean heavily on the use of Other Transaction Authority to award contracts for emerging AI and machine learning prototypes.
Contract obligations in AI-related investments grew 75% from FY 2016 to 2018. Expect continued increases in contract spending as agencies expand knowledge and use of AI capabilities.
More information and citations
My budget figures and graphs were taken from the Deltek FY2020 Report on Federal Artificial Intelligence Landscape.
The GIF at the top of the article is a depiction of a Graph Neural Network (GNN) , a connectionist model particularly suited for problems whose domain can be represented by a set of patterns and relationships between them. Go here to learn more. In those problems, a prediction about a given pattern can be carried out exploiting all the related information, which includes the pattern features, the pattern relationships and, in general, the whole graph that represents the domain. GNN peculiarity consists in its capability of computing the output prediction processing directly the input domain graph, without any preprocessing into a vectorial representation. GNNs have been proved to be a universal approximator for a class of functions on graphs and have been applied to several tasks, including spam detection, object localization in images, molecule classification.