Under the agreement, federal customers using Palantir’s Foundry system — a software-based data analytics platform that leverages AI and machine learning to automate decision-making — will be able to request data labeling services from Enabled Intelligence. The goal of the partnership is to improve the accuracy of custom AI models built by users by providing them with higher-quality datasets to create and test them with.

“By bringing the Palantir Platform and Enabled Intelligence’s labeling services together in highly secured environments, we believe this will streamline the full cycle of AI model creation and deployment, ensuring that our clients can leverage more precise and actionable insights from their data,” Josh Zavilla, head of Palantir’s national security arm, told DefenseScoop in a statement.

Enabled Intelligence employs a cadre of experts dedicated to annotating multiple data types — including satellite imagery, video, audio, text and more — at a much faster rate than other players in the market, the company’s CEO Peter Kant told DefenseScoop. The impetus for starting Enabled Intelligence came from a gap in the government’s access to accurately labeled data that it needs to train AI models, he said.

“We focus a lot on the quality and the accuracy of the data,” Kant said in an interview. “The better quality of the labeled data, the better and more reliable the AI model is going to be.”

Through the new partnership, government customers are now able to send specific datasets that may need additional labeling directly to Enabled Intelligence’s analysts, Kant explained. Once the data is annotated, the company can push it back to the original users through Foundry so that it can be used to build more accurate artificial intelligence models.

“It’s fully integrated into our labeling pipeline, so we automatically create labeling campaigns to the right people — our employees who know that ontology and know how to do that work with that phenomenology — [and] label it there within Foundry,” Kant said.

The company’s services would be particularly beneficial if a U.S. adversary or rogue actor begins deploying new capabilities that aren’t already included on a training dataset. For example, if American sensors capture imagery indicating that Houthi fighters are using a new small commercial drone as an attack vector, AI models developed for the Maven Smart System or other similar programs might not initially have the right data to support an appropriate response, Kant explained.

While improving the quality of AI has clear advantages for users, Kant emphasized that it can also reduce the overall power needed to run those models. He pointed to the open-source large language model (LLM) developed in China, known as DeepSeek, and claims by its developers that the platform’s performance is comparable to Open AI’s ChatGPT or Google’s Gemini with only a fraction of compute — partly because its developers focused on training data that was well labeled.

“Our customers — especially on the defense and intelligence side — say, ‘Hey, we’re trying to do AI at the edge, or we’re trying to do analysis at the edge.’ You can’t put 1600 GPUs on a [MQ-1 Predator drone], so how do we do this?” Kant said. “One of the ways of doing that has been to really focus on making sure that the data going in is of high quality and can be moved around easily.”

The ability to run AI models with less compute would be particularly beneficial for operators located in remote environments, where it can be difficult to build the necessary infrastructure needed to power them, he added. 

“Now we want to use [LLMs] for some real critical systems activities for these missions, and the recognition that the data that goes in and how it’s used to train [AI] and how good it is, it’s been critical — not just in terms of reliability, but also how much compute we need,” Kant said.

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The Department of Defense has used bug bounty programs to find cyber weaknesses by incentivizing white-hat hackers to hunt for them. Now, lawmakers want a similar concept to be applied to AI models.

“Not later than 180 days after the date of the enactment of this Act and subject to the availability of appropriations, the Chief Digital and Artificial Intelligence Officer of the Department of Defense shall develop a bug bounty program for foundational artificial intelligence models being integrated into the missions and operations of the Department of Defense,” states the conference report on the fiscal 2024 National Defense Authorization Act that was released this week.

For the bill, lawmakers define a foundational AI model as “an adaptive generative model that is trained on a broad set of unlabeled data sets that may be used for different tasks with minimal fine-tuning.”

The CDAO would be able to collaborate with leaders of other federal departments and agencies that have cybersecurity and AI expertise on the effort.

No later than one year after the enactment of the legislation, the head of that office would be required to brief congressional committees on the development and implementation of the program and long-term plans for these types of initiatives.

An amendment to the Senate’s version of the NDAA included a provision for an AI bug bounty program, but the House version did not. The mandate for such a program made it into the compromise version.

The CDAO is already exploring bounty concepts for its missions. In July, it issued a call to industry to set up and administer a “bias bounty” program to tackle bias in artificial intelligence systems.

Meanwhile, the Pentagon is exploring use cases for generative artificial intelligence through Task Force Lima and other efforts. However, there are also concerns that adversaries could use generative AI to harm the United States.

The Senate version of the NDAA included an amendment that would require the Defense Department to create and execute a prize competition to evaluate technology for the detection and watermarking of generative AI. The House version did not include such a provision, but the compromise version does.

“Not later than 270 days after the date of the enactment of this Act, under the authority of section 4025 of title 10, United States Code, the Secretary of Defense shall establish a prize competition designed to evaluate technology (including applications, tools, and models) for generative artificial intelligence detection and generative artificial intelligence watermarking,” the NDAA conference report states.

The objective would be to facilitate the research, development, testing and evaluation of these types of technologies to support the secretaries of the military departments and combatant commanders “in warfighting requirements,” as well as transitioning such technologies from prototyping to production.

For the bill, lawmakers define generative AI detection as “the positive identification of the use of generative artificial intelligence in the generation of” digital content. Generative AI watermarking is defined as “embedding within such content data conveying attribution of the generation of such content to generative artificial intelligence.”

Private sector entities, defense contractors, academia, federally funded R&D centers, and federal departments and agencies would be eligible to participate in the prize competition, which would be administered by the undersecretary of defense for research and engineering.

Congress hasn’t voted yet on the compromise NDAA.

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The 2023 Department of Defense Data, Analytics, and AI Adoption Strategy was produced by the Chief Digital and AI Office (CDAO) and unveiled by Deputy Defense Secretary Kathleen Hicks at a Pentagon press briefing on Thursday.

“Increasingly over the last dozen years, advances in machine learning have heralded and accelerated new generations of AI breakthroughs — with much of the innovation happening outside of the DOD and government. And so our task in DOD is to adopt these innovations wherever they can add the most military value,” she told reporters.

Senior CDAO officials, including its chief Craig Martell, have been teasing this newly launched strategy — which was slated for publication by the end of the summer — repeatedly in recent months. 

It follows two foundational guiding documents meant to drive AI use across the DOD. The first enterprise-wide AI strategy was launched in 2018, and Pentagon officials disseminated the first revised version in 2021.

This new 26-page AI adoption strategy “not only builds on DOD’s prior AI and data strategies — but also includes updates to account for recent industry advances in federated environments, decentralized data management, generative AI and more,” Hicks said.  

She confirmed the department’s hope that this updated guidance will help all components and services speedily realize critical unfolding AI-aligned efforts, including those related to Joint All-Domain Command and Control (JADC2) and the new Replicator initiative, which aims to counter China by fielding thousands of autonomous systems in multiple domains within the next 2 years.

The strategy includes sections spotlighting new key outcomes the department is eyeing to enable with AI, the associated goals officials will pursue and the high-priority areas where they plan to fully embrace these rapidly evolving technologies. Specific goals listed involve removing policy barriers, investing in interoperable and federated infrastructure, improving data management and growing AI talent, among others. 

“The state of AI in DOD is not a short story, nor is it static. We must keep doing more — safely and swiftly — given the nature of strategic competition with the [People’s Republic of China], our pacing challenge. At the same time, we benefit from a national position of strength. And our own uses grow stronger every day, and we will be keeping up the momentum ensuring we make the best possible use of AI technology responsibly and at speed,” Hicks said.

During a call with reporters following Hicks’ briefing, Martell also echoed this intention of Pentagon leadership to keep a flexible and fluid approach when it comes to unleashing AI and machine learning for the military in the coming years. 

“Technologies evolve. Things are going to change next week, next year, next decade — and what wins today might not win tomorrow. Rather than identify a handful of AI-enabled warfighting capabilities that will beat our adversaries, our strategy outlines the approach to strengthening the organizational environment within which people can continuously deploy data analytics capabilities for enduring decision advantage,” Martell explained.

He and other officials on the call told reporters that the CDAO team is crafting new “iterative” implementation guidance that will accompany the strategy. They expect to release it in the next few months. 

That plan will look a bit different from traditional implementation guides, Martell said, because “each of the services have wildly different needs, they’re at wildly different points in their journey and they have wildly different infrastructure.” 

Notably, this refreshed strategy also comes as DOD leadership is heeding warnings from top technology and national security experts about the dire need for the U.S. military to be “AI-ready” by 2025.

During the Pentagon press briefing, Hicks told DefenseScoop that the 2023 AI adoption strategy marks “a key piece of how we get there.”

Another major contributing element to DOD becoming AI-ready to match its adversaries in that time frame, in her view, is on-time congressional appropriations and predictable resourcing to advance ongoing work.

“Absent any predictability in our funding streams, it’s very hard for us to be able to project [DOD’s AI readiness] with accuracy,” Hicks told DefenseScoop.

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“I would say that the way that we’re trying to overcome paralysis is by analysis of the risk — and by being an organization that is setting the culture to experiment,” Jinyoung Englund, CDAO’s chief strategy officer for algorithmic warfare, said at the Google Public Sector Forum hosted by Scoop News Group. 

Personnel from four legacy Defense Department components — the Joint Artificial Intelligence Center (JAIC), Defense Digital Service (DDS), Office of the Chief Data Officer, and the Advana program — were realigned to form the CDAO in late 2021. Englund previously held leadership roles across DDS, including as its acting director, before she was tapped to serve as the CDAO’s first-ever chief strategy officer for algorithmic warfare.

That office reached full operating capability in 2022, the same year that Lyft’s former head of machine learning Craig Martell departed from his post at the ride-sharing service to lead it.

“[Martell] served in industry — and he’s not afraid to take risks. Why? Because as a scientist, he knows that risk can be measured and managed. And so the way that we as an organization within DOD are trying to accelerate adoption is by spreading a culture of learning by doing,” Englund said at the conference.

Offering a few examples, she pointed to Task Force Lima, which Deputy Defense Secretary Kathleen Hicks recently formed under the CDAO to expedite the U.S. military’s grasp and deployment of emerging generative AI capabilities. That field essentially involves large language models that generate software code and media content based on human prompts. 

“Task Force Lima is led by mostly active-duty military, whose sole focus is to take the user-centered design and a human-centric approach to identifying what are the ways that generative AI can best assist our service members and our military counterparts in terms of how we implement that new technology within our bureaucracy,” Englund said.

“Already we’ve collected over 200 use cases” associated with the effort, she added, noting that the unit recently released a request for information to gain feedback regarding novel ways in which generative AI could be hacked, and the ways in which products can be built to deliver services to free service members from rote work.

The CDAO’s unfolding Global Information Dominance Experiments (GIDE) mark another area where the office is “moving forward and spreading this culture of learning by doing,” she noted.

Englund explained that through this series of exercises — the fourth of which is currently being conducted — the CDAO partners with the Joint Chiefs of Staff to get nascent technologies in the hands of warfighters to see “how they actually work in theater”— and determine if associated policies or requirements need to be rewritten to enable their use. 

In her view, the CDAO is in a unique position where it has the capacity to prototype new technologies, as well as rulemaking and acquisition authorities. 

“Let’s just be frank: there’s risk everywhere. Whether you’re a private sector business or a government agency, there’s risk. And risk can be and should be measured and managed. And if you can figure out a framework for doing that, then it really should not be so scary adopting the technology,” Englund said.

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The CDAO delegation visited those partners between Aug. 20 and 25 — notably in the week after President Biden hosted leaders from Japan and South Korea at Camp David for a summit where the countries agreed to work together on missile defense and military technology development.

In responses to questions via email on Thursday, a CDAO spokesperson would not comment on whether Martell and his team met with some of the same officials that attended the president’s summit. Still, the office did share new details regarding who the CDAO officials met with abroad, and some of the key topics they deliberated on in those engagements. 

“These were high-level introductory meetings intended to strengthen the bilateral relationship between CDAO and Dr. Martell’s counterparts in each country,” the spokesperson wrote. 

No formal agreements were signed as a result of the meetings, and the official did not confirm any next steps between the international partners. 

“They discussed responsible AI, data management, and organizational culture, and talent management supporting data, analytics, and AI in defense. Dialogue also included emerging data and analytics needs, and ways to strengthen ties and collaboration bilaterally and multilaterally,” the spokesperson said.

In Singapore, Martell met with various senior leaders from the Ministry of Defence, according to the CDAO official — “including Permanent Secretary (Defence Development) Mr. Melvyn Ong, Deputy Secretary (Technology) Mr. Chad-Son Ng, Chief Defence Scientist Mr. Peng Yam Tan, and Chief Executive of the Defence Science and Technology Agency Mr. Mervyn Tan.”

Martell also engaged with experts from Singapore’s Defence Technology Community, the Defense Science and Technology Agency, the Counter-Terrorism Information Facility, and Smart Nation Digital Government Office.

In Korea, Martell and his team attended a “Defense Data AI Cooperation Meeting” and connected directly with leadership and broader teams from the ROK Ministry of National Defense Planning and Coordination Office, the Data Policy Division, the Defense Data Analysis Center, and the Defense AI Center Promotion Team. 

“While in Seoul, he also visited the United States Forces Korea,” the CDAO spokesperson told DefenseScoop.

In Tokyo, Martell met with “key interlocutors focusing on data and AI in the Japanese Ministry of Defense Internal Bureau,” they further noted. 

With those officials — including Japan’s Chief Information Officer, Deputy CIO and Joint Staff — the CDAO discussed areas of future data and AI-related collaboration. 

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Broadly, DOD sets military housing areas (MHAs) — or batches of zip codes around military installations — and assigns basic allowance for housing (BAH) rates for each of those areas. The department’s BAH program is a fundamental component of its pay packages for service members, and it’s ultimately designed to compensate for local median rental costs and average utilities costs for civilians with comparable incomes to each military pay grade in their specific duty locations via monthly payments. 

Officials look to private sector rental housing costs and associated data to compute BAH rates, which are updated annually.

Bacon’s new bill (the text of which hasn’t been released publicly but was shared with DefenseScoop by the lawmaker’s team this week) is titled the “Basic Allowance for Housing Calculation Improvement Act of 2023.”

The three-page legislation would mandate the secretary of defense — no later than Sept. 30, 2024 — to “seek to enter into an agreement with a covered entity pursuant to which the covered entity shall calculate, using industry standard machine learning and artificial intelligence algorithms, the monthly rates of BAH for not fewer than 15 MHAs,” through the proposed pilot program. 

A “covered entity” is defined in the bill as a “nationally recognized entity in the field of single-family housing that has data on local rental rates in real estate markets across” the U.S. 

DOD would have to submit a report evaluating that work to the House and Senate Armed Services Committees (HASC and SASC) within two years of the act’s passage. Beyond that time frame, the bill language does not include further details or directions for the pilot.

The legislation was referred to HASC, where Bacon serves as a member, following its introduction last week.

If the bill does pass, this wouldn’t be the first time AI would be examined by Pentagon officials as a tool to augment the BAH calculation process. A cohort of Air and Space Forces members completed a capstone project that assessed AI and machine learning models to optimize BAH determination procedures, last year, through the Department of the Air Force-Massachusetts Institute of Technology Artificial Intelligence Accelerator’s Phantom Fellowship Program. 

Members of Bacon’s team did not provide further information regarding what prompted the lawmaker to introduce the bill now. Congress is in recess until after Labor Day.

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“When we look at our strategic plan…the first two lines of effort we are focused on is prioritizing command and control and driving force readiness through innovation,” said Greenwell, director of the Enterprise Integration and Innovation Center and CIO at the DefenseTalks conference in Washington, D.C. on September 19.  

Greenwell cited DISA’s recent reorganization to “formalize data as one of our key lines of effort” to leverage “data as the center of gravity” to enable speed of capability to the warfighter. He also highlighted moves to promote joint warfighting cloud capabilities and modernizing DevSecOps as additional areas where DISA is positioning itself to add cloud capabilities across the DOD.

Speaking alongside Greenwell, Josh Marcuse, head of strategy and innovation for Google Cloud, Public Sector advocated for an approach that first looks at mission outcomes and the warfighter’s needs when considering cloud capabilities. Once those outcomes are determined, Marcuse said it would be more valuable to work backwards and answer questions around how to integrate AI and ML capabilities to solve those challenges.

Marcuse noted that in his time working alongside DOD partners he has seen how a majority of their budget and work is improving their data hygiene. However, “not all problems can be solved with AI,” he said.

“AI [and ML] is not like salt and pepper that you sprinkle on any program. You have to be really good at understanding what problem lends itself to a machine learning approach,” he explained. “We look for where there is a data set and a mission outcome, where we can use mature techniques that are secure and proven, to use that data to achieve a result that the warfighter needs.”

Greenwell added how data is key to DISA’s and the DOD’s mission. He shared that last October, DISA reorganized its leadership structure to establish its first chief data officer as part of its modernized data strategy goals. One of the agency’s key roles in cyber-defense and cybersecurity requires them to understand the mountains of data they are gathering from their wide variety of capabilities.

“Data is central to what we are doing,” he said, “and we can’t do it without AI and machine learning. We have to evolve.”

Finally, Greenwell and Marcuse both stressed the importance of public-private partnerships to move forward some of these big ideas.

“From my perspective it is a lot about partnerships,” said Greenwell, “…and DISA is one entity within the department and can’t do it alone.”

Marcuse noted that there exist pockets of innovation everywhere in DOD and DISA, however they are not evenly distributed across the organizations because of cultural barriers that lead to isolation. Innovation in DOD becomes more about overcoming those cultural barriers.

“There is no organization on the planet that has both mission and [technology] capabilities at such scale. So, when the DOD finds these gems, and then is able to mass produce them, that is when you see historic contributions to the frontiers of technology,” explained Marcuse.

Watch the full discussion on the DefenseTalks On-Demand page.

You can also register for the upcoming Google Government Summit on November 15 in Washington D.C. to hear more from government leaders on how they are using cloud to achieve their mission outcomes.

This article was produced by Scoop News Group for DefenseScoop and underwritten by Google Cloud.

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