AI is rapidly changing how wars are fought. Winners of the AI arms race will become the world’s dominant military powers. Here’s what’s at stake.

Primer CEO Sean Gourley testified at the U.S. Chamber of Commerce AI Commission on July 21, 2022, about the global AI arms race — and what the U.S. and our allies need to do next. Below are his testimony notes. 

“The stakes are high, and the winner of this AI arms race will become the dominant military power in the world. It is a race that we cannot afford to lose.”

-Primer CEO Sean Gourley

Testimony by Primer CEO Sean Gourley 

Thank you Congressmen Ferguson and Delaney, members of the Commission. 

While there is a huge amount of discussion about the impact of AI on our society, the biggest impact that Artificial Intelligence (AI) will have in the next decade will be in warfare, where advanced AI will fundamentally change the way wars are fought. The impact of AI on warfare will be akin to that of nuclear weapons, where AI is a technology so powerful that the country that wields it will quickly defeat any opponent who does not.  

Artificial intelligence represents what is known as the “third offset,” a set of technological capabilities so advanced that it gives whoever wields them an advantage so large that the opponent without the technology is effectively defeated before a conflict even starts. 

“AI is a technology so powerful that the country that wields it will quickly defeat any opponent who does not.”

-Primer CEO Sean Gourley

The first offset was nuclear weapons, which ended the Second World War. The second offset was stealth weaponry and precision munitions, which resulted in the U.S. defeating the Iraq army — which at the time was the world’s 6th most powerful army — in less than 72 hours during the First Gulf War. The third offset is artificial intelligence. And it will have as profound an impact on warfare as any of the previous two offsets combined. 

This is not a hypothetical discussion about something that might happen in the future. Today, we are already seeing the impact of AI on the battlefield in Ukraine. From computer vision being used with commercial drones to identify camouflaged Russian vehicles, to AI that listens to radio communication and triangulates these with videos from social media to track Russian troop movement and intention in real time, through to AI being deployed in the information war to attempt to manipulate the narrative and win over the enemy population. 

These changes are happening rapidly. But Russia and Ukraine are not widely regarded as AI superpowers – it is China that we need to turn our attention to. 

We need to acknowledge that we are currently in an AI arms race with China. The stakes are high, and the winner of this AI arms race will become the dominant military power in the world. It is a race that we cannot afford to lose. 

The United States and its allies come into this arms race with a considerable set of advantages, but speed is going to determine the winner here — and China is moving fast.

To read more, download the full testimony here.

The more advanced our technology gets, the more intelligent our cities become. From smart mobility to sustainability to smart data and digital safety and security, smart city development brings together the best and brightest in science, business, and urban planning to bring big ideas to life for a brighter future. 

Tampere Smart City Week in Tampere, Finland, one of the safest countries in the world, drew 1700 participants from over 65 countries and 71 companies worldwide. The goal of the SURE Smart City initiative is building a city ready for any type of event using holistic smart technologies focused on security, safety and digital trust.

Primer’s Associate Director, Nick Melgaard, participated in a panel at the conference on June 15 about creating a data-driven and socially cohesive city. The panel discussed the impact of technology on social cohesion, as well as how to enhance social cohesion in smart cities while guaranteeing the respect of the citizens’ fundamental rights. Nick discussed the development of language AI and natural language processing at Primer, as well as the challenges of getting cutting-edge AI into difficult places for mission-critical use cases. 

While Primer Command’s primary use case is to monitor fast-breaking global events for national security, smart cities can use its AI & NLP technology closer to home to create safer and more resilient cities. 

Monitoring fast-moving information

Smart cities are connected cities. Sensor data from IoT devices, digital infrastructure, telecommunications, and more all produce an overwhelming amount of data at any given moment. And the bigger the city, the more significant the amount of data produced. That said, vast quantities of data make it hard to get a pulse on fast-moving events that impact the safety and security of a city’s residents. 

This use case is where AI and NLP technology come in: It creates structured data streams from raw data to help people understand the world by identifying locations, people, organizations, objects, and other critical information. This process creates real-time situational awareness so local governments and businesses can respond quickly and appropriately to unfolding events. These events aren’t only limited to national security—they can be anything from everyday crime and homelessness to a major sporting event or concert. By making sense of data, smart cities can create a safer environment for people to live in.

Learn more about NLP

Applying AI & NLP to smart cities and smart event security 

Primer Command is an AI-powered, real-time intelligence solution that monitors fast-breaking global events with NLP technology. With Primer Command, organizations can monitor, analyze, and respond to unfolding events after summarizing a high volume of publicly available information (such as news and social feeds). The real-time situational awareness that Primer Command enables gives governments and businesses the power to make decisions regarding the security of their people, assets, infrastructure, and more. 

While governments and armed forces can use AI and NLP to keep entire countries and regions safe, it can also help build smarter, safer, and more resilient cities. Smart event security is just one example of how powerful AI and NLP can be when brought closer to home to protect the daily livelihood of residents and visitors so they can keep doing the activities that matter most to them—safely.

For more information about Primer and to access product demos, contact Primer here.

Primer’s solutions use AI to help the DOD transform petabytes of data into Open Source Intelligence (OSINT)

There’s an endless amount of open source information available, especially relating to breaking issues. Yet, open-source information is not clean, organized, reliable, or authoritative, which overwhelms analysts relying on it to gain real-time intelligence for situational awareness. Government entities need to leverage AI-powered solutions to harness the power of the information found in open-source reporting.

Too much data, too little time and resources

We have zettabytes of global data at our fingertips. Still, only 20 percent is utilized, leaving 80% of the world’s most valuable information sitting idle in unstructured, text-based data such as social media, news feeds, audio recordings, and video feeds. Moreover, processing, analyzing, and drawing actionable insights from this volume of data require a large, highly-skilled workforce. Yet, no matter how much time or human capital you have, you can’t even begin to scratch the surface of this data—and promptly at that. 

Unlocking the full potential of information with AI & NLP 

By leveraging artificial intelligence (AI), government entities can transform petabytes  of information into Open Source Intelligence (OSINT). The Department of Defense (DOD) defines OSINT as “intelligence that is produced from publicly available information and is collected, exploited, and disseminated on time to an appropriate audience for the purpose of addressing a specific intelligence requirement. 

Read more: AI for Open-Source Intelligence is a Key Mission-Critical Capability in 2022 

Powered by natural language processing (NLP), AI machine learning tools automate information gathering and then inject this information into knowledge bases that cluster, curate, and organize it into specific areas of interest. Primer’s solution is designed to automate the discovery and sharing of information with speed, precision, comprehension, and scale, enabling change agents to access real-time data. 

Solving the DOD’s top data challenges

Primer platform meets the requirements set forth by the 2022 DOD Data Strategy, otherwise known as VAULTIS: visible, accessible, understandable, linked, trustworthy, interoperable, and secure. Primer’s technologies are interoperable, meaning that they are modular and can easily integrate with other technologies in cloud and on-premise environments. Additionally, Primer deployment can be a shared resource between services, collapsing silos and reducing duplicative spending. 

Primer’s NLP models search and analyze open-source information more rapidly than human analysts by pulling out key intelligence deliverables such as people profiles, situation reports, and taskings. These models help analysts quickly and more accurately identify suspected information operations and enable faster source validation. 

Specifically, Primer Platform enables the DOD to:

  • Create machine-generated, self-updating entity profiles 
  • Curate data to understand information operations
  • Automate situation and watch floor reports
  • Leverage AI-powered multi-intelligence analysis for historical and current datasets
  • Supercharge its counter-operations 

As breaking events unfold, change agents need to be able to act with agility and confidence. With AI, front-line analysts, operators, and decision-makers can ingest and access incoming unstructured open-source information and transform it into real-time intelligence to better achieve their mission objective. 

Read the white paper: AI for Open-Source Intelligence is a Key Mission-Critical Capability in 2022

When Russian Military commanders started using unencrypted radio communication, we were able to capture this and deploy our AI tools to extract key insights from the data. What we found were indications of a disorganized army, bogged down, and confused.

Read: “As Russia Plots Its Next Move, an AI Listens to the Chatter,WIRED, April 4, 2022

Troops frequently go out to the battlefield not fully knowing what they might encounter. Even the best armies in the world rarely have all needed capabilities ready and designed for the task at hand. At Primer, we increase operational readiness by building and deploying flexible AI solutions that are optimized for today’s and tomorrow’s defense and intelligence use cases. To help U.S. and strategic partner Services navigate continuously evolving needs, we at Primer believe that the best approach is not a single perfect AI algorithm. The solution is a powerful AI infrastructure that enables subject-matter experts to build, customize, and deploy AI models that help them rapidly process massive volumes of data. When intercepted Russian audio of military commanders discussing tactics recently became available, we saw an opportunity to test our AI platform capabilities. 

Today, linguists and analysts spend their days pouring through noisy radio chatter, on the hunt for actionable information. Analysts are at pains to listen to each minute of audio in hopes of finding useful information and for fear of missing things. Intelligence may only find its way to decision-makers hours later, which could be too late to act on it. Using Primer’s platform capabilities, we can transform these workflows and shorten the time from insight to action. With Primer, users can build and run models across radio chatter that detect Russian language and extract mentions of location, persons, certain topics of interest, or military equipment, summarize text into paragraphs or bullets, and identify trends. By instantly surfacing the audio files and information that is likely most relevant, users cut through the noise, focus on the actual analysis, and develop robust near-real time intelligence for decision-makers. 

How we use AI to transform Russian military audio into operational intelligence 

We started with audio captured from Software Defined Radio (SDR) feeds transmitted over the internet at and connected this to our Primer audio ingestion engine which downloaded the communications as wav files. These audio intercepts were then fed into the Primer Platform. First, we used a noise reduction engine and the audio recordings were cleaned to eliminate static or noise, improving sound quality. Cuts containing music or non-Russian speakers were filtered out as they were unlikely to contain our desired targets. These cuts were converted to English text by first using the Russian speech recognition and then Russian to English translation engines within Primer’s platform. Human experts were then able to review the output to understand any areas where the models may have had difficulty.

Sample of the audio captured and then ingested by Primer for analysis

With our cleaned and prepared dataset in hand, we pulled in our commercially available national security models to get a better understanding of what was going down at the frontlines. 

Here are the analytical machine learning capabilities we used to rapidly extract key information from the Russian radio chatter:

  • A topic identification model to find and identify the main audio topics so that analysts can navigate precisely to the audio that they are most interested in.
  • A dialog summarizer to give analysts a concise description and overview of what was said across hours of captured audio.
  • A “call-to-action” model to find and time-stamp orders being issued.
  • A weapons classifier to identify mentions of military equipment.
  • A Named Entity Recognition (NER) engine to identify, extract, and disambiguate all the people, locations, and organizations that are mentioned in the radio communications. 
The AI models from speech-to-text, diarization, and translation showed a pair of soldiers lost and confused.
An AI-generated summary that distills the key points of the dialog saving human analysts critical time.
A NER model extracts people and locations mentioned in the transcript.
Our Military Equipment model identifies mentions of military equipment to help users quickly assess the intelligence value of audio intercepts.
An abstract topic model reduces the main points of an intercept to only a few words, or in this case, user names that are discussed.
The call-to-action model extracts commands from the audio. For the purposes of this blog post, the specific date is obscured to keep exact movements hidden.

Analysts can subscribe to this feed of “calls to action” and consume these in real time on the Primer Platform in apps like Primer Command, or integrate the feed into a Common Operating Picture next to their other battlefield data. This radically democratizes access, as analysts can now directly consume and leverage this information, removing the constraint of needing to be a Russian language specialist or listening for hours to multiple radio frequencies. Instead, analysts can conduct deeper analysis based on a near real time call to action feed of Russian military chatter.

One afternoon’s work on the audio files revealed actionable intelligence that Russian forces were reporting heavy fire in their operating area and losing tanks. They also received orders to retreat from their position. But more importantly we were able to build a custom AI pipeline on our platform to continually and automatically monitor Russian radio chatter and extract tactical insights in near real time. 

This type of real-time intelligence can be a game changer for analysts and commanders. Whoever succeeds in deploying operational AI – reliable AI models that are trained, validated, and in production on live operational data during missions – will have the advantage of faster AI-augmented human decision-making, thereby improving the odds at outmaneuvering the adversary. 

When it comes to battlefield capabilities, the ability to rapidly customize models that meet constantly evolving needs is a crucial differentiator. The models used above are not perfect and will benefit from further training and specialization for specific national security use cases and niches.  Here are a few of the challenges our platform of pre-trained models, labeling, and model training can address to help analysts make immediate workflow improvements in the course of their mission:

  • Our dialogue summarizer model had to contend with the idiosyncrasies of tactical radio communications such as speakers repeatedly saying their own name. In the Primer Platform, this model can be retrained to improve performance, by starting with the machine-generated summary a human analyst can correct and improve. This corrected summary turns into another training datapoint to teach and fine tune the model.  
  • Our weapons classifier model was trained last year during the crisis in the Donbass region of Ukraine. Since then, many more types of military equipment have been deployed. Additionally dialog on radio chatter is much more informal and references to “Helis” are not captured by the initial weapons classifier as “Helicopters.” Users and analyst teams can take the initial models and quickly label new data using our LightTag software and then retrain them to improve their performance in the Primer Platform. 
  • The geolocation engine was not initially trained on tactical radio communication data. As such it was not initially trained to pick up references to map coordinates which are common on radio chatter for artillery units. The users are able to improve the geolocation model by retraining our Named Entity Recognition engine in LightTag and the Platform to identify these and other geographic references.
Creating additional labels within LightTag guarantees the most performant models customized to specific data feeds.

How strategic advantage depends on flexible and customizable AI solutions 

Deployment of AI in a conflict environment is not about having a single perfect algorithm. Instead, the best solution is often a collection of AI algorithms working together as a complex system — the optimal models will change as quickly as the crisis evolves. The most successful approach is to have flexible and customizable AI tools and infrastructure available to users closest to the need so that they can quickly test, customize, and connect the most performant models together. 

At Primer, our goal is to empower those closest to the problem to leverage advanced AI to help them make sense of an incredibly complex information landscape. Machine learning is a force multiplier for linguists and analysts tasked with processing hundreds of hours of audio files.  Small teams can process many hours or days worth of intercepts in a matter of minutes. This enables faster and better tactical responses to unfolding events. Intelligence information previously only available hours or days later can now be in the hands of battlefield commanders when it’s needed most. A strong AI infrastructure for Defense must let users retrain ML models quickly – as these opportunities may not last very long.

Primer’s end-to-end platform provides the AI infrastructure to rapidly build, deploy, and customize models tailored to evolving intelligence and defense use cases.  

The ability to rapidly exploit OSINT, even when messy and in other languages, makes it possible for poorly equipped armies to outmaneuver much better trained and resourced ones. That is a huge paradigm shift, the likes of which we have yet come to terms with. 

Despite having the best trained and resourced services in the world, the hard truth is that a lack of agility and urgency in integrating and operationalizing AI by the U.S. and our strategic allies means that we fall behind faster than we’re willing to admit. As we look ahead, we’re now in a new era of warfare where those with sophisticated and customizable AI capabilities have a distinct advantage in decision superiority, and thereby the ability to outcompete and outmaneuver adversaries to increase the odds of winning wars that cannot be prevented.

For more information about Primer and to access product demos, contact Primer here.

On the heels of the rapid departure of U.S. and allied troops from Afghanistan, Primer moved quickly to provide users in the intelligence and defense communities with free access to a new AI tool that uses open source data and social media to provide near-real time situational awareness of rapidly changing environments.

Primer Command is the first machine learning tool to perform natural language processing (NLP) and computer vision inferences in near-real time to continuously structure and analyze millions of pieces of content streaming in from more than 80,000 news and social media sources in more than 100 languages.

Primer’s algorithms extract key entities such as people and organizations, de-duplicate entities, cluster related images, and detect suspected disinformation. This enables users to summarize fast-breaking events, identify relevant novel information, and enable decision makers to assess options and tradeoffs – faster, easier, and with greater accuracy than ever before.

Primer Command was developed over the past year in partnership with the U.S. Air Force and U.S. Special Operations Command. We planned to launch the product later this year. However, the evolving situation in Afghanistan underscored the timely relevance of these capabilities for the intelligence community, so we accelerated the delivery of Primer Command into the hands of our users.

Now through the end of November, Primer users in the national security space can obtain free access to Primer Command.

Additionally, in the two weeks following the rapid withdrawal of U.S. troops from Afghanistan, Primer built a new feature in Command called Tactical Insights to address the urgent need for on-the-ground information across the country. Tactical Insights leverages the full power of Primer’s machine learning library and natural language generation capabilities to automatically generate in-depth situation reports on Afghanistan at the country level and, importantly, for individual provinces.

By automatically reading, analyzing, and curating vast volumes of incoming and often conflicting news and information into concise, relevant situational reports, Primer Command arms users with the intelligence needed to make well-informed decisions in the face of shifting circumstances.

Supporting the national security mission has been a priority for Primer since our inception. We are confident Primer Command – with its top-performing named entity recognition engine as well as its geolocation and summarization capabilities – will fill a critical gap for our users who need to track and analyze rapidly changing situations such as those unfolding in Afghanistan.

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