Artificial Intelligence & Machine Learning at NPS

Artificial Intelligence (AI) is widely recognized as a critical and decisive capability in future warfare and national defense. It is featured prominently as a technology that must be mastered by high-level strategic groups in DOD and by the National Security Commission on AI. It creates far-reaching possibilities for disruptive innovation.

The Naval Postgraduate School has deep faculty expertise in AI, data science and geopolitical strategy, plus more than 2,000 resident students with operational frontline experience dedicated to addressing the challenges of Great Power Competition. AI/ML connects in a multidisciplinary fashion across NPS with engineering, robotics, operations research and more, and is part of multiple educational certificates and degrees at NPS. With NPS faculty and students currently teaching and researching varied AI concepts and applications, and translating them into future naval capabilities, the university is deeply embedded in advancing the technology and the DOD’s AI workforce. 

 

AI Initiatives at NPS

Consortium for Intelligent Systems Education and Research (CISER)

The consortium includes about 70 faculty from across the NPS campus who are interested in artificial intelligence, machine learning and data science. CISER promotes the advancement ofintelligent systems and analytics through education, research and innovation adoption, and provides the DOD with relevant answers to difficult strategic problems involving AI. The consortium leverages the university’s multidisciplinary expertise to provide innovative solutions and relevant insights into the strategic challenges posed by artificial intelligence.

CISER assisted in the development of the Harnessing AI course (now in its second edition) and the design of training materials for the JAIC. It also supported an effort to establish a Naval AI Accelerator at NPS and is currently supporting a faculty initiative to establish a center for Trustworthy AI on the NPS campus.

Collaboration with the Joint AI Center (JAIC)

The JAIC is the DOD’s lead organization for accelerating the adoption of artificial intelligence across the services. In 2020, NPS conducted two workshops for program managers and key technical personnel of AI projects in the DOD as part of the JAIC’s pilot courses for Drive AI and Create AI.

“What we need are warfighters. We need people who understand how decisions are made and how our decisions are structured. ... People touch AI in different ways, there are builders, users and employers, and each one of those skill sets are necessary for us to have a comprehensive understanding of not only bringing AI to the table, but what table to bring it to and what it is supposed to do when it gets there. That’s where having uniformed experts who are really good at the craft can extend their knowledge to AI realm to do data science,” said JAIC Director Marine Corps Lt. Gen. Michael Groen, MS in Electrical Engineering and Applied Physics '95, during a Secretary of the Navy Guest Lecture, Oct. 13, 2020.

 

Wade Huntley, PhD
Sr. Lecturer, National Security Affairs
Sr. Lecturer, Cyber Academic Group

Dr. Wade L. Huntley is senior lecturer in the National Security Affairs department and Cyber Academic Group at the Naval Postgraduate School in Monterey, California, and an independent consultant on international security issues. He is also Program Director for DSCU Regional Education Courses. Huntley has worked extensively engaging government officials and non-governmental experts on a range of contemporary international policy challenges. Previously Huntley was Director for Disarmament and Non-Proliferation Research in the Liu Institute for Global Issues at the University of British Columbia in Vancouver, Canada; Associate Professor at the Hiroshima City University Peace Institute in Hiroshima, Japan; and Director of the Global Peace and Security Program at the Nautilus Institute in Berkeley, California. He has also held research and teaching positions at several universities and colleges and has worked in consultative roles engaging officials and experts on a range of global security issues, specializing in scenarios generation for future-oriented examinations of near-term policy challenges. Huntley’s publications include four edited volumes and over fifty peer-reviewed articles, book chapters and scholarly essays.

As an expert in national security, you recognize the strategic implications of emerging technologies like AI. How important is it for NPS students and faculty with technical expertise in AI to consider the geopolitical implications?

For NPS students and faculty with technical expertise in artificial intelligence, being mindful of the geopolitical implications of AI development is essential. This reflects a broader reality: modern scientists have typically paid close attention to the social and political consequences of their work. The atomic scientists of the early 20th century were keenly aware of the revolutionary impact that nuclear explosive technologies would create. Biologists have long appreciated the careful choices required for conducting responsible research using genetics technologies. Research and development in artificial intelligence is no different. 

In fact, several factors make this imperative particularly strong. One factor is the expanding range of applications of AI technology in economies and societies throughout the world. We cannot predict all effects of these applications, so we have a responsibility to be attentive to unanticipated outcomes. Another factor involves the potent ethical conundrums that artificial intelligence applications can present with respect to maintaining awareness of and accountability for the automated decision-making capabilities that we create. And, of course, applications available to governments and militaries raise questions concerning domestic political values and international security. 

Many AI applications have been and will continue to be unambiguously positive -- for the American people, U.S. national security interests, and the world overall. But this is only because we are paying continuous attention to those kinds of implications while undertaking technical development.

The National Security Commission on Artificial Intelligence suggested: "We can still defend America and our allies without widespread AI adoption today, but in the future, we will almost certainly lose without it." What could be at stake if the Navy and Marine Corps do not commit to these new technologies rapidly enough?

There is a lot at stake if the U.S. military and government do not incorporate AI technologies rapidly enough to benefit from the opportunities they present. There is also a lot at stake if we commit to incorporating technologies before we sufficiently appreciate their impacts on tactics, operations and strategies. Skewing too far in either of these directions poses risks. The challenge is to find the right balance. The context of high uncertainty makes that hard.

In terms of military applications alone, the wide range of potentially beneficial applications of AI technology creates real impediments to anticipating longer-term and interactive effects. Those uncertainties exacerbate the challenges of balancing the risks of untested technologies against the risks of being left behind.

History tells us that weapons technologies need to be tested on the battlefield before their full operational and strategic implications can be known. However, Artificial Intelligence is not a new weapon technology. Rather, AI represents a set of revolutionary tools that can dramatically enhance the effectiveness of many weapon systems, create the possibilities of new weapons, and fundamentally reshape how we think about warfare at all levels. That's a lot to think through ahead of time. Fortunately, balancing risk and opportunity is a lot easier in some instances than others. So part of the formula for committing to AI technologies rapidly enough involves making selective choices about which areas to push forward firmly and which areas to think about more carefully.

Perhaps the biggest challenge is to elevate the decision-making about AI incorporation as much as possible out of the bureaucratic and competitive funding pressures with which we are all familiar. On the one hand, we sometimes see an over-infatuation with new technologies for their own sake. On the other hand, organizational inertia and resistance to changing established ways of doing things are all too common. Rivalries among military services and governmental agencies, including competition for funding, can also warp decision-making. The ubiquity of the opportunities for AI applications makes AI decision-making potentially porous to such influences. We will be well served by enhancing our capacity to develop holistic and integrated perspectives to guide the incorporation of AI technologies into military capacities.

China and other countries are investing heavily in AI and some have declared that whoever masters AI masters the world. Do you agree that mastering AI is the most important priority for the US in terms of global competition? How is this affected by the practice of civil-military integration and Military-Civil Fusion?

A decade ago, it was possible for those thinking about the incorporation of AI into U.S. capabilities and strategy to imagine the U.S. would dominate this technology domain for a while. That's not the case anymore. AI-driven AlphaGo's defeat of the world's top Go player in 2017 is often termed China's "Sputnik Moment." The analogy refers to the U.S. government being caught off guard by Russia's launch of the Sputnik satellite, catalyzing a national surge in development of U.S. space launch capabilities. It's an interesting analogy because the U.S. government was more prepared for that moment than is commonly realized. Similarly, I suspect that China's top leaders may not have been so surprised by this rather dramatic demonstration of AI possibilities.

Shortly thereafter, China's State Council issued an AI Development Plan calling for China to be a world competitor in several AI technology fields by 2020, a world leader in AI applications by 2025, and a world leader in AI "innovation" by 2030, including advanced implementation of "intelligent economy and intelligent society." This AI Development Plan also emphasizes "two-way" civil-military fusion. We also know that China has subsequently made great strides in these directions, incorporating AI technologies ubiquitously across its economy and society, as well as in military development. Some ofthese applications have deeply enhanced China's domestic surveillance capacities in ways antithetical to U.S. values. But China's civil-military fusion strategy has enabled the country to apply dramatic advances in civil AI implementation to national security and military purposes.  

There are many ways to assess the relative AI capacity of U.S. & China today. When reviewing the range of data available, the general picture that emerges is that the U.S. is still in a leadership position. China is a primary competitor and gaining. Mastering AI is a critical priority for the U.S. in terms of global competition, military security, and broader national position. But it cannot be the only priority, not least because AI capabilities touch on so many other elements of US social, economic, and military power. AI priorities need to be fulsomely incorporated into a core national strategy ofgreat power competition that is dynamic and responsive to evolving conditions and new developments.  

Taking a step back, it is clear that U.S.-China competition in general, and the priority on both sides to develop and incorporate AI technologies, is creating arms race dynamics. National competition plus the perception of military applications drives research and development. The uncertainty over potential advantages fuels this racing – neither country wants to become technologically ambushed. The dual civilian and military applications, and the positively reinforcing impact of civil-military symmetrical development, accelerates technology diffusion and hastens adoption. There is debate over whether arms racing increases prospects of military conflict or provides an outlet for a competition short of conflict. But it is generally the case that arms races increase political tensions and destabilize relations. For AI, there is also another particular consequence: competitive pressures can drive states to deploy and use AI-enabled systems at earlier stages of development than they would otherwise. That is perfectly rational behavior because the risk of an adversarial advantage balances the risk of immature technologies. The consequence is that both sides may end up deploying AI systems that could be more prone to unanticipated behavior, bias, and failure -- and more susceptible to subversion.

What can/should we do to improve civil-military relations in the U.S.?

Department of Defense strategies already recognize that, with respect to the development of artificialintelligence technologies, the relationship of the military and government to civilian and corporate sectors is going to have to operate differently than it has in the past. Current strategies anticipate decentralized development of the technologies, emphasize partnerships with industry and academia, and envision the DOD helping develop non-defense applications. The reason for this approach is clear: the private sector is leading, and will continue to lead, AI innovation and development. Private funding for AI R&D dwarfs governmental resources.

This is a reversal of the Cold War relationship, in which the U.S. government led technology development for national security needs. The U.S. nuclear deterrent infrastructure was developed fundamentally on the basis of requirements defined by the U.S. government. The development ofmost major conventional capabilities followed the same pattern. Now, however, in artificialintelligence and in a number of other areas of information technologies, the DOD is incorporating existing commercial technologies for military uses. The U.S. military is drawing on, rather than driving, private sector innovation. This reversed relationship means that direct DOD resourcing is only part ofthe effort. This new relationship also highlights the importance of public support, and industry support, for DOD objectives. That last point should not alarm us. We have known since Clausewitz that broad national support for governmental objectives is an essential element of success for any military strategy.  

At NPS you educate future military leaders, some of whom will become decision-makers in national security strategy. How do you approach educating these students knowing that future challenges are ever-evolving and unpredictable? 

Some folks miss the simplicity of the bipolar nuclear balance that defined the strategic competition ofthe Cold War. I do not miss the prospect of large-scale nuclear conflict that was an ever-present condition of that period. 21st-century geopolitical conditions are certainly more complex, dynamic and unpredictable. We are still struggling to adapt our strategic acumen to this uncertain era. Therefore, preparing younger officers to be nimble & farsighted in their decision-making in a world defined by rapid and unpredictable change is a paramount task. 

I am an advocate of developing strategies that are self-reflective and self-adaptive – that is, strategies that enable decision makers to react flexibly to dramatic surprises and changing conditions, rather than strategies that create bounded bureaucratic momentum destined to be increasingly inconsistent with national security needs. 

If you were to predict, what challenges do you anticipate being the most pressing in 10-20 years when current NPS students are in those leadership roles?

The best prediction I can make is that prediction will be a failing strategy. The most pressing need for current NPS students who will be in leadership roles 10-20 years from now is to prepare themselves to cope with challenges that they may not anticipate, or even imagine, today. That will work if the world turns out more linear as well.

 

Dr. Sam Buttrey on Operations Research and use of ArtificialIntelligence within the DOD

Champion of Jeopardy's Inaugural Professor's Tournament
Associate Professor, Operations Research

 

Ying Zhao, PhD
Research Professor, Department of Information Sciences

Dr. Ying Zhao is a research professor at the Naval Postgraduate School in Monterey, California. Zhao's research focuses on data sciences, machine learning, and artificial intelligence methods, including lexical link analysis (LLA), collaborative learning agents (CLA), and reinforcement learning for search, visualization, and analysis, for defense military applications in the areas of semantic and social networks, common tactical air pictures, combat identification, logistics, wargaming, and mission planning.

Since joining NPS, Zhao has been a principal investigator of many awarded DOD research projects. She is a co-author of four U.S. patents in knowledge pattern search from networked agents, data fusion, and visualization for multiple anomaly detection systems. She received her doctorate in mathematics from MIT and is the co-founder of Quantum Intelligence, Inc. Zhao is currently an ESEP scholar (Engineers and Scientists Exchange Program of DOD) at the Defence Science and Technology Lab, UK, through Nov. 30, 2022.

The recently released OUSD(R&E) Science and Technology Vision lists "Trusted AI and Autonomy" as one of the 14 critical tech areas vital to maintaining the US' national security. How does NPS' AI curriculum and the Consortium for Intelligent Systems Education and Research (CISER) support the DOD's priorities within the innovation ecosystem?

NPS has the best military experts, strategists, and policy influencers. NPS connects to war colleges and DOD research labs. NPS also has secure facilities. Our students are experienced warfighters and know the requirements; we can contribute a lot to U.S. national security priorities. The students are eager to learn the technologies and apply them to military applications. They are future leaders and can deliver strategies to guide and influence future warfare.

NPS Faculty are very motivated and well-funded for research. The DOD engineers and scientists have always been at the cutting edge of innovation. They have been developing and delivering military applications and beyond for generations, and nothing is entirely new. However, due to globalization, open society, new threats in addition to technological breakthroughs, a cultural shift may require us to adapt now. 

According to Rev Lebaredian, VP of simulation technology at Nvidia, "The rate of innovation in AI has been accelerating for the better part of decade, but AI cannot advance without large amounts of high quality and diverse data." He also predicts we will see an explosion in synthetic data. What kind of role do you think synthetic data has in military operations? Do you think synthetic data is accurate enough for AI to be successfully implemented in military applications?

Defense applications are uniquely challenging for data sciences, AI/ML technologies, and digital modernization because sometimes there is overwhelming big data such as in the logistics, sensor, and system engineering areas. Sometimes there is little data or no data such as emerging behavior and intention of red forces or unknown situations. To generate synthetic data, simulating the environment and adversaries is important. This is related to AI's cutting-edge AI GANS (generative adversarial networks), GPT3 (Generative Pre-trained Transformer 3), and AGI (Artificial General Intelligence) assistant models – "a knowledge system that is always around us, learns and help us learn" (Eric Schmidt). This is because the AI breakthroughs (e.g., AlphaGo, AlphaZero, AlphaFold among others) show that data sciences, AI/ML and tremendous computing power can create AI assistants to discover and "see" things that human cognition and eyes cannot see. For example, the computer found new moves in the strategy board game "Go" that Asian people who played "Go" for thousands of years did not discover. An equipped virtual agent should also aid human warfighters in a very revolutionary and profound way. This aligns with Eric Schmidt's, who chaired the U.S. National Security Commission on AI, view on how AGI would be used in the future.

For military applications, such as AGI, should include sets of AI/ML tools and simulation models, which are consistent, explainable, no black boxes, and can be used to test theories for a range of users in a wide range of applications such as campaign/mission planning, future warfighting concepts designing and simulation, warfighter training, etc., allow different questions to be asked easily. The proposed end user areas are very human-machine interactive. Therefore, there is a lot of room to compare machine intelligence and human cognition, which in my view should be complementary. For example, synthetic data and AI recommendations may not be accurate, valid, or feasible, therefore, need human collaboration to check the feasibility and validity of the synthetic data.

We need to invest in the AGI type of AI assistant for warfighters because a demonstration of a defense AI product in a specific area is difficult to implement in a short period with respect to the strict DOD data and application security. The need is to develop an AGI that does not need to re-program heavily toward plug-and-play with data models for specific processes and areas. 

As the DOD's leading graduate research university, NPS is able to conduct research that directly addresses DOD challenges and priorities and also to connect with industry, who in many ways are advancing faster than the DOD in areas like AI. Why is it important for the DOD to collaborate with academia and industry? What role is NPS playing, or should be playing, in facilitating that collaboration?

The ML/AI community is driven heavily by open-source software and algorithms. Data and business are private and unique to the industries and applications. Competitors may have been leveraging the open sources very much, and we should do that too. For example, the book "AI Superpowers" by Kai-Fu Lee discussed how China used open sources to speed up applied AI technologies dramatically.

In the past, I've been funded by the NAVAIR China Lake & ONR FNC, NPS NRP, and ONR NEPTUNE programs with various projects related to the framework I called "leverage AI to learn, optimize, and wargame (LAILOW) for defense applications." I have collaborated with the MIT AF AI Accelerator and CSAIL to apply LAILOW to cross-domain use cases. The NPS students are very experienced warfighters and know the requirements. Our students may not code like AI professionals and computer scientists, but they should be able to map the requirements to potential solutions and tools developed by the AI/ML community. NPS is also close to Silicon Valley and first-class AI universities like Stanford and Berkeley. There are tremendous opportunities and needs for the NPS to build the MIT AF AI accelerator-like platforms to communicate the defense application requirements to the academic communities, connect requirements with the advanced research results and tools, and build DOD AI application pipelines. The related funding platforms NavalX, DIU and Tech Bridges have been focusing on the startups, which is good. However, we can do more about education and students' thesis projects to reach out and leverage their resources.  

You've championed the adoption of AI/ML techniques for the USMC supply chain. Can you summarize the Leverage Artificial Intelligence to Learn, Optimize, and Win (LAILOW) framework and why it is so important to DOD logistic planning?

The logistics and supply chain enterprise has a tremendous amount of data and probably is the unique place that can have a digital twin; therefore, "learn" refers to applying AI/ML to learn and discover patterns from historical data and then use the patterns for prediction and forecast. The logistics and supply chain enterprise is a very complicated business enterprise and needs constant decision and action optimization, which the operation research has traditionally addressed. "Win" refers to the wargame perspective that I have introduced in this project to divide the business process into the problem areas (attackers) and solutions areas (defenders); the wargame is between the problems and solutions. The wargame serves as a what-if analysis to analyze and discover risk areas where the enterprise can (resilience) or cannot (vulnerability) handle in a simulation and synthetic environment. LAILOW is an AGI framework and the USMC supply chain provides a good use case for the framework, the framework is important not for just logistic planning, but all planning areas.

We'd love to hear more about your personal research. Can you share a little bit about a current or recent research project you've worked on that you find/found particularly interesting?

NPS has planned to send students to the MIT AF AI Accelerator, I hope to continue working with the NPS students and the Accelerator to advance the LAILOW framework. The other proof-of-the-concept projects of LAILOW are shown below:

  • Cognitive and agile radio, sponsored by ONR NEPTUNE (code 33). LAILOW is used to learn and optimize high radio frequency for communicate and replace traditional automatic link establishment (ALE).
  • Leverage AI to Learn, Optimize, and Wargame (LAILOW) for Strategic Laydown and Dispersal (SLD) of the Operating Forces of the US Navy (funded by N3/N5): this is to standardize and digitize the current SLD decision making process, make an electronic SLD model, and reduce manual workload for the current method.
  • Leverage AI to Learn, Optimize, and Wargame (LAILOW) for a Complex Enterprise: Application to the Sustainment in a Contested Environment: Navy Battle Damage Assessment and Repair (BDAR) (funded by N4): this is to reconstruct an actual wargame and simulate more.
  • Threat and Capability Coevolutionary Wargame (TCCW) Applied to Advanced Persistent Threats (funded by OUSD(R&E), NSA, NIWC Pacific), use LAILOW to learn cyber decoy and detection models
  • Structured and Unstructured Data Sciences and Business Intelligence for Analyzing Requirements Post Mortem, N8 - Integration of Capabilities & Resources, LAILOW will be used to perform distributed what-if analysis and simulation.

I am working on a Defence Science and Technology Lab project to design a virtual red AI agent for the integrated air missile defence, IADS. The vision of the red AI agent will be beyond IADS. I also work with the Dstl Porton group in the areas of AI for Radio Frequency, patterns of life used for modeling civilian populations, and influence operations using open source data and tools.  

My personal interests also include quantum intelligence game and machine learning, quasicrystal theory as language models related to the origin of consciousness, causality, mathematical models, and human cognition.

 
Dr. Jae Jun Kim

Jae Jun Kim, PhD
Research Associate Professor, Mechanical and Aerospace Engineering Department

Dr. Jae Jun Kim is a Research Assistant Professor in the Mechanical and Aerospace Engineering (MAE) Department and an Associate Director of the Spacecraft Research and Design Center at the Naval Postgraduate School. His research interests include dynamics and control of flexible spacecraft, active and adaptive optics for large aperture telescopes and imaging spacecraft, and high-energy laser beam control. His teaching interests include Mechanical Vibration, Space Systems Laboratory, Dynamics and Control of Flexible Spacecraft, and Acquisition, Tracking and Pointing of Military Spacecraft. Kim received his degree from SUNY - Buffalo in 2004 and joined NPS in 2006.

Can you tell us more about the AI techniques being implemented into the High-Energy Laser Beam Control Research Testbed at NPS? If implemented effectively, how will these AI techniques change high-energy laser beam operations for the military? 

High-energy laser system operators perform various tasks such as acquiring and tracking a target, identifying target type/class, selecting aimpoint on a target, engaging and maintaining aimpoint, and providing kill assessment. Decision making is an integral part in performing these tasks. AI techniques can help make timely and accurate decisions by operators. AI techniques are currently used to develop a decision-aid tool by automating some of the tasks traditionally performed by human operators. The High-Energy Laser Beam Control Research Testbed (HBCRT) at NPS provides an experimental platform to develop and evaluate AI techniques for target detection, target classification, aimpoint selection, and aimpoint maintenance.

NPS Researchers Use High-Tech Optics, Artificial Intelligence to Advance Laser Weapons Systems

The HBCRT project involves researchers from across defense, industry and academia. How does shared collaboration on defense-related research drive competitive advantage for the United States? What role is NPS playing, or should be playing, in facilitating that collaboration? 

The Department of Defense, industry and academia may have different missions, but we have a common goal of advancing the technology. From the technology concept to the industry product, technology maturation cannot be achieved without direct/indirect collaborations of many relevant research efforts. I think shared collaboration efforts can accelerate the pace of technology maturation, which can lead to competitive advantage. We always learn new things when we work on collaborative research such as the HBCRT project. We share the knowledge gained with our students who are end users of these technologies.

Successful collaboration is often achieved when we are working on research projects that are ofinterest to both NPS, DOD and industry. Those research problems typically involve emerging technologies that have a potential to meet the need for warfighters. I think NPS is better positioned in understanding the need for warfighters and identifying technologies that can meet the need. NPS plays a role in pursuing technologies that are of interest to NPS, DOD and industry to facilitate productive collaboration.

As we incorporate AI into existing technologies, how can we ensure that those technologies are protected from adversarial attack? What measures are taken to mitigate vulnerabilities? 

For many military applications, such as high-energy laser systems, AI technologies are currently used to develop a decision-aid tool, not a decision-making tool. I think we still need to improve the technology until we have enough confidence about the AI models to produce accurate and protected output. Another question is how much confidence is enough.

There are few places where warfighters, engineers, researchers and national security experts can work together to proactively address DOD priorities and emerging threats. How does NPS’ mostly military student body and cross-department collaboration impact the applied solutions emerging from NPS? How does it impact future military operations?   

Military students have good understanding of current capabilities and future needs in their respective areas. I often come across motivated students who have ideas on improving capabilities in their respective fields. Many NPS faculty and staff provide research opportunities in various DOD-relevant problems to stimulate student research interest as well. This provides overall positive impact in applying new technologies and solving relevant DOD problems at NPS. I think there is much room to improve in terms of cross-departmental collaboration and leveraging resources available at NPS.  

The global space economy is nearing $500 billion, as of 2021, and some predict it will be $1 trillion by 2040. With this increased focus and investment in space, what emerging aerospace engineering technology trends, other than AI, should the US focus on? What technologies will have the greatest impact on the defense aerospace industry?

I believe space surveillance is very important for the defense aerospace industry. We have been eyeing technologies that can provide high resolution and persistent surveillance capabilities from space, such as segmented mirror telescopes and sparse aperture imaging. With the successful launch of the James Webb Space Telescope in December 2021, large aperture space imaging platforms became a reality. Cost-effective large aperture imaging from space is one of the technology areas that we can focus on. I am sure there are other important technical areas that can impact the defense aerospace industry as well.

 
 

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