-- In this paper, m ilitary use cases or applications and implementation thereof are considered for natural language processing and large language models, which have broken into fame with the invention of the generative pre - trained transformer (GPT) and the extensive foundation model pretraining done by OpenAI for ChatGPT and others . First, we interrogate a GPT - based language model (viz. Microsoft Copilot) to make it reveal its own knowledge about their potential military application s and then critically assess the information . Second, we study how commercial cloud services (viz. Microsoft Azure) could be used readily to build such applications and assess which of the m are feasible. We conclude that t he summarization and generative properties of language models directly facilitate many applications at large and other features may find particular uses . This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by ...

Abstract: The design and performance analysis of relay lenses that provide high-performance image transmission for target acquisition and tracking in military optical systems. Relay lenses are critical components for clear and lossless image transmission over long distances. In this study, the optical performance of a relay lens system designed and optimized using ZEMAX software is investigated in detail. The analysis focuses on important optical properties such as modulation transfer function (MTF), spot diagrams, Seidel diagram, field curvature and distortion. The results show that the lens has significant potential in military applications for target detection and tracking with high resolution and low aberration. Accepted: 1. Introduction Military optical systems provide high-performance and reliable monitoring for target identification and tracking in critical missions. These systems have become indispensable in modern warfare, where the ability to process and analyze real-time visual data can determine the success or failure of operations. By combining advanced optical technologies with robust design methodologies, military systems aim to deliver precise and effective solutions for a variety of applications. In this context, optical components must ensure exceptional image clarity, resolution, and durability to withstand challenging operational environments.

Meta will make its generative artificial intelligence (AI) models available to the United States' government, the tech giant has announced, in a controversial move that raises a moral dilemma for everyone who uses the software. Meta last week revealed it would make the models, known as Llama, available to government agencies, "including those that are working on defence and national security applications, and private sector partners supporting their work". The decision appears to contravene Meta's own policy which lists a range of prohibited uses for Llama, including "[m]ilitary, warfare, nuclear industries or applications" as well as espionage, terrorism, human trafficking and exploitation or harm to children. Meta's exception also reportedly applies to similar national security agencies in the United Kingdom, Canada, Australia and New Zealand. It came just three days after Reuters revealed China has reworked Llama for its own military purposes.

The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support.

Artificial Intelligence (AI) plays a significant role in enhancing the capabilities of defense systems, revolutionizing strategic decision-making, and shaping the future landscape of military operations. Neuro-Symbolic AI is an emerging approach that leverages and augments the strengths of neural networks and symbolic reasoning. These systems have the potential to be more impactful and flexible than traditional AI systems, making them well-suited for military applications. This paper comprehensively explores the diverse dimensions and capabilities of Neuro-Symbolic AI, aiming to shed light on its potential applications in military contexts. We investigate its capacity to improve decision-making, automate complex intelligence analysis, and strengthen autonomous systems. We further explore its potential to solve complex tasks in various domains, in addition to its applications in military contexts. Through this exploration, we address ethical, strategic, and technical considerations crucial to the development and deployment of Neuro-Symbolic AI in military and civilian applications. Contributing to the growing body of research, this study represents a comprehensive exploration of the extensive possibilities offered by Neuro-Symbolic AI.

United States, British and European Union representatives are visiting the United Arab Emirates amid concerns regarding shipments of goods, including computer chips, to Russia that could help Moscow in its war on Ukraine. The senior Western officials arrived in the Gulf nation this week to discuss sanctions on Russia, as concerns mounted that Moscow was bypassing them through various means, a US embassy spokesperson told CNN on Wednesday. The report came on the heels of another by the Wall Street Journal on Monday – citing US and European officials – that discussed plans to jointly press the UAE to halt shipments of goods to Russia. This was part of a collective global push to keep computer chips, electronic components and other so-called dual-use products out of Russian hands, the WSJ report said. The UAE, a member of the OPEC oil alliance that includes Russia, has maintained good ties with Moscow despite Western pressure to isolate Russia over the invasion of Ukraine that began in February 2022.

Lt. Gen. Keith Kellogg, former Pence National Security adviser, weighs in on reports that China is working to establish a military base in Cuba. This month, 79 years ago, Allied troops stormed the beaches of Normandy in World War II. The greatest amphibious invasion in human history was a product of unprecedented levels of planning, heroism, sacrifice, and new technology. Scientists, service members, and industrialists came together to develop and build underwater pipelines, artificial harbors, specialized landing craft, and tide prediction equipment. Everyone had a job to do – and everyone did it as one team in the fight.

French artificial intelligence start-up NukkAI has signed a contract with Thales to develop an AI-based data fusion solution for military applications. Thales believes military analysts in operations centers face significant challenges in extracting relevant information from the huge volumes of data generated by multiple sources such as video and audio streams, websites, Twitter feeds, satellite imagery, social media, and telephone conversations. According to Thales, real-time data analytics will enable them to develop advanced military strategies with greater efficiency. As a result, Thales is planning to implement NukkAI's solution in a number of its military data processing programs. When operators are swamped by information, the solution will use real-time data exploitation and fusion methods to automatically review the knowledge available so that analysts can focus on elements of interest.

Artificial Intelligence (AI) is proving to be as much a security tool as an economic one in the Indo-Pacific, with the US and China attempting to expand their influence in the region. Because of AI's increasing role in military applications, the Indo-Pacific, rather than Silicon Valley, is now fast emerging as the principal AI market in the world. A new report from "Research and Markets" predicts that the radar simulator market alone in the Indo-Pacific will boom from $326.2 million in 2022 to $470.5 million by 2028 for a compound annual growth rate (CAGR) of 6.3%. "Unmanned aerial vehicles (UAVs) developed by various enterprises have modern capabilities and have extended their reach and operating boundaries," the report states, adding, "There has been a surge in the development of laser-guided missiles with higher accuracy to hit the locked target. To combat this threat in the future, there would be a rise in the development of radars and radar simulators to train operators to combat the rising modern warfare systems."

In September 2021, the "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Michael Littman of Brown University. The report, entitled "Gathering Strength, Gathering Storms," answers a set of 14 questions probing critical areas of AI development addressing the major risks and dangers of AI, its effects on society, its public perception and the future of the field. The report concludes that AI has made a major leap from the lab to people's lives in recent years, which increases the urgency to understand its potential negative effects. The questions were developed by the AI100 Standing Committee, chaired by Peter Stone of the University of Texas at Austin, consisting of a group of AI leaders with expertise in computer science, sociology, ethics, economics, and other disciplines.