AI Enters the Front Lines of National Defense and Security

July 29, 2019

Artificial intelligence is now an essential component of defense and security strategies — from combat systems to operational processes.

When it comes to protecting the nation and its allies, the United States is armed with some of the world’s most sophisticated defense and security systems. Increasingly, artificial intelligence is at the heart of these systems.

For decades, the United States Department of Defense (DoD) has pioneered innovative uses for AI in defense and security, from assessing the readiness of military vehicles to identifying insurgent targets. Today, these efforts have shifted into high gear under a U.S. strategic initiative focused on harnessing AI to advance the security and prosperity of the nation.

As for that initiative, a 2018 DoD summary of the nation’s AI strategy gets right to the point: “We will harness the potential of AI to transform all functions of the Department positively, thereby supporting and protecting U.S. servicemembers, safeguarding U.S. citizens, defending allies and partners, and improving the affordability, effectiveness, and speed of our operations.”[1]

With those goals in mind, the use cases for AI in defense and national security are virtually unlimited. AI can be embedded into weapons and surveillance systems to enhance performance. It can be used to improve target recognition, combat simulation and training, and threat monitoring. It can be used in logistics and transportation systems, to help the military get the right equipment and people to the right places at the right time.

Let’s look at some of the specific use cases for AI in defense and security.

Combat systems

Military systems equipped with AI are capable of handling larger volumes of data and to do so more efficiently than conventional systems. AI also enables advanced computing and decision‑making capabilities that help military commanders improve the control, regulation and actuation of combat systems.

In an example of the importance of AI on the battlefield, the research firm KPMG notes that a defense agency could have just 8–10 minutes to decide whether a missile launch represents a threat, share the findings with allies, and decide what to do in response. It takes AI to rapidly integrate real-time data from satellites and sensors and to present findings immediately to help commanders decide what actions to take.[2]

Video surveillance and image analysis

When it comes to analyzing video and images captured by surveillance systems and aerial vehicles, AI is a huge advantage. For example, algorithms can be trained to recognize terrorist activity evident in streams of video, just as they can be trained to recognize cats in datasets filled with all kinds of images.[3]

“AI applied to perception tasks such as imagery analysis can extract useful information from raw data and equip leaders with increased situational awareness,” the DoD notes in its AI strategy report. “AI can generate and help commanders explore new options so that they can select courses of action that best achieve mission outcomes, minimizing risks to both deployed forces and civilians.”[4]

Cybersecurity

Cyber-warfare will clearly be one of the battlefields of the future. AI can help military organizations combat the threat of cyber-attacks, which can now be launched from virtually anywhere in the world.

A few examples of the way AI is being deployed in this new digital battlefield:

  • The U.S. Department of Homeland Security has piloted AI tools for detecting cyber-network intrusions and malicious activities.[5]
  • The DoD has a project under way that will develop an algorithm to detect and deter cyber-attackers whose skills are aimed at DoD information systems.[6]

Predictive maintenance

Keeping equipment battlefield-ready is a huge challenge for national defense agencies. AI can help with this labor-intensive work.

A case in point: The DoD plans to use AI to predict the failure of critical parts, automate diagnostics, and plan maintenance based on data and equipment condition. Similar technology will be used to guide the provisioning of spare parts and optimize inventory levels. The department says these advances will ensure appropriate inventory levels, assist in troubleshooting, and enable more rapidly deployable and adaptable forces at reduced cost.[7]

Service automation

Defense and security agencies increasingly rely on AI-driven applications to automate services, streamline business processes, cut the time spent on repetitive tasks, and reduce the chances for human errors.

For example:

  • In the recruiting process, the U.S. Army uses an AI-driven interactive virtual assistant to answer questions from prospective recruits, check users’ qualifications, and refer prospects to human recruiters. In a report on AI-augmented government, Deloitte says that the interactive virtual assistant does the work of 55 recruiters, with an accuracy rate of more than 94 percent.[8]
  • The Defense Advanced Research Projects Agency developed a digital tutor for computer skills training. After 16 weeks of training with the tutor, Navy students who had no prior IT experience scored higher in tests of IT knowledge and job-sample troubleshooting than others who received 35 weeks of classroom instruction.[9]

Infrastructure considerations

As AI makes ever-deeper inroads in defense and security applications, machine learning algorithms and technologies continue to develop and increase in scope. All of this puts new demands on the computing infrastructure that powers the AI-driven solutions. At the same time, it creates the need for infrastructure that can be adapted to the unique and changing requirements of defense and security use cases.

In this rapidly growing field, reprogrammable FPGAs give defense and security agencies a new class of IT armaments. With their flexible programming model, FPGAs allow for the continual implementation of the newest algorithms and neural network topologies.

Intel offers these examples of FPGA use cases for defense and security applications that are ideally suited for FPGAs.[10]

  • Radar and sensors — Radar has been a foundational technology area in which the semiconductor industry has played a large role for the last two decades. FPGAs can help system designers meet requirements for high-performance data processing, ultra-wide bandwidth, high dynamic range, and adaptive systems.
  • Electronic warfare — In electronic warfare systems, key drivers for continuous enhancements are electronic counter-counter-measures, stealth technologies, closely interlinked smart sensor networks, and intelligent guided weapons. Systems must be able to rapidly analyze and respond to multiple threats in extremely short timeframes. FPGAs can help meet these challenges.
  • Security — Strong encryption and authentication are keys to ensuring communications and data security at ever increasing data throughput rates. Strong cryptographic algorithms implemented on FPGAs that are secure by design provide the foundation for trusted information assurance systems.

Key takeaways

AI is now an essential technology for military and defense agencies across the wide range of their operations. It’s one of the keys to protecting the safety and security of our Armed Forces, our citizens and our allies. It’s also one of the keys to increasing the efficiency and effectiveness of military operations, at both home and abroad.

Ultimately, to be the best at what they do, our defense and security agencies need the real-time insights and intelligent automation that is possible only with AI-driven systems.

To learn more

For a look at leading-edge systems and solutions to power AI applications, including new Ready Solutions for AI – Deep Learning with Intel, visit dellemc.com/ai. Learn more about Dell EMC High Performance Computing and AI solutions at dellemc.com/hpc.

 


[1] U.S. Department of Defense, “Summary of the 2018 Department of Defense Artificial Intelligence Strategy.”

[2] KPMG, “Artificial intelligence in Defense,” accessed July 15, 2019.

[3] Congressional Research Service, “Artificial Intelligence and National Security,” January 30, 2019.

[4] U.S. Department of Defense, “Summary of the 2018 Department of Defense Artificial Intelligence Strategy.”

[5] Washington Technology, “More signs pointing to AI’s growth in the federal market,” May 15, 2018.

[6] Dell Technologies, “Pentagon Projects Move Military into AI Arena,” April 25, 2019.

[7] U.S. Department of Defense, “Summary of the 2018 Department of Defense Artificial Intelligence Strategy.”

[8] Deloitte, “AI-augmented government,” 2017.

[9] Defense Technical Information Center, “Accelerating Development of Expertise: A Digital Tutor for Navy Technical Training,” November 1, 2014.

[10] Intel, Military, Aerospace, and Government website, accessed July 17, 2019.

 

 

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire