Over the past three decades, Hicks has watched the Pentagon transform--politically, strategically, and technologically. She entered government in the 1990s at the tail end of the Cold War, when optimism and a belief in global cooperation still dominated US foreign policy. After 9/11, the focus shifted to counterterrorism and nonstate actors. Then came Russia's resurgence and China's growing assertiveness. Hicks took two previous breaks from government work--the first to complete a PhD at MIT and joining the think thank Center for Strategic and International Studies (CSIS), which she later rejoined to lead its International Security Program after her second tour. "By the time I returned in 2021," she says, "there was one actor--the PRC (People's Republic of China)--that had the capability and the will to really contest the international system as it's set up."

Football coaches desperate to boost their team's performance could soon find an answer in an artificial intelligence system aimed at conjuring the next superstar. A kind of sporting Aladdin's lamp is within reach, technologists claim, which could allow managers to simply wish for a new player with the aggression of Erling Haaland or the poise of Jude Bellingham and for an AI to suggest the perfect prospect. A system that uses video and automated tracking to monitor the performances of nearly 180,000 mostly teenage footballers around the world underpins the services of Eyeball, a digital scouting company that already has relationships with more than a dozen Premier League clubs and other elite teams in Europe and North America. Using what it claims is the largest video database of global youth football – with players logged from 28 countries – the company says it can now determine which young players most fit the description of current or recent top stars as defined by one of eight archetypes. The characteristics of the ideal midfielder are a blend of Steven Gerrard, Kevin De Bruyne, Dominik Szoboszlai, Federico Valverde, Dani Olmo and Bellingham – all top-ranked internationals.

Multivariate decision trees are powerful machine learning tools for classification and regression that attract many researchers and industry professionals. An optimal binary tree has two types of vertices, (i) branching vertices which have exactly two children and where datapoints are assessed on a set of discrete features and (ii) leaf vertices at which datapoints are given a prediction, and can be obtained by solving a biobjective optimization problem that seeks to (i) maximize the number of correctly classified datapoints and (ii) minimize the number of branching vertices. Branching vertices are linear combinations of training features and therefore can be thought of as hyperplanes. In this paper, we propose two cut-based mixed integer linear optimization (MILO) formulations for designing optimal binary classification trees (leaf vertices assign discrete classes). Our models leverage on-the-fly identification of minimal infeasible subsystems (MISs) from which we derive cutting planes that hold the form of packing constraints. We show theoretical improvements on the strongest flow-based MILO formulation currently in the literature and conduct experiments on publicly available datasets to show our models' ability to scale, strength against traditional branch and bound approaches, and robustness in out-of-sample test performance. Our code and data are available on GitHub.

The Defense Department is speeding up its development of artificial intelligence tools for the commander of U.S. forces in the Indo-Pacific, according to a senior Defense official. AI can assist Admiral John Aquilino, who is focused on the threat from China, with some of the problems "he is most worried about," Deputy Defense secretary Kathleen Hicks said in an interview. "We're helping him with that," Hicks said of the Pentagon's efforts to develop AI applications for Aquilino's command, arguing that adversaries recognize the U.S. military's strength at command and control, or the ability to run missions and direct forces.

On August 28th, the Deputy Secretary of Defense, Kathleen Hicks, announced what she called the Replicator initiative--an all-hands-on-deck effort to modernize the American arsenal by adding fleets of artificially intelligent, unmanned, relatively cheap weapons and equipment. She described these machines as "attritable," meaning that they can suffer attrition without compromising a mission. Imagine a swarm of hundreds or even thousands of unmanned aerial drones, communicating with each other as they collect intelligence on enemy-troop movements, and you will begin to understand the Deputy Secretary's vision for Replicator. Even if a sizable number of the drones were shot down, the information they'd gathered would have already been recorded and sent back to human operators on the ground. In one sense, Hicks's announcement, during an address titled "The Urgency to Innovate" at a meeting of National Defense Industrial Association, did not signal a wholly new approach.

Deputy Secretary of Defense Kathleen Hicks addressed the plan and how the U.S. will continue to counter the rising aggression from China. The Pentagon has started to assess the possibility of developing an artificial intelligence (AI)-powered fleet of drones and autonomous systems that officials argue will allow the U.S. to compete with and counter threats from China. We are not seeking to be at war, but we have to be able to get this department to move with that same kind of urgency because the PRC isn't waiting," Kathleen Hicks, the deputy secretary of defense, said during an interview earlier this week with The Wall Street Journal. Hicks spoke about the potential uses of such an AI fleet during a speech on Wednesday, revealing the department would spend hundreds of millions of dollars on the project, aiming to produce thousands of systems for use over land, air and sea ready for first deployment within two years. China has focused heavily on AI research and development, producing ...

Hosted by Eleanor Drage and Kerry Mackereth, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode, we talk to Mar Hicks, an Associate Professor of Data Science at the University of Virginia and author of Programmed Inequality: How Britain Discarded Women Technologists and Lost its Edge in Computing. Hicks talks to us about the lessons that the tech industry can learn from histories of computing, for example: how sexism is an integral feature of technological systems and not just a bug that can be extracted from them; how techno-utopianism can stop us from building better technologies; when looking to the past is useful and when it's not helpful; the dangers of the'move fast and break things' approach where you just build technology just to see what happens; and whether regulatory sandboxes are sufficient in making sure that tech isn't deployed unsafely on an unsuspecting public. Mar Hicks is a historian of technology, gender, and labor, specializing in the history of computing. Hicks's book, Programmed Inequality (MIT Press, 2017) investigates how Britain lost its early lead in computing by discarding the majority of their computer workers and experts–simply because they were women.

The Pentagon plans to field thousands of drones and other high-tech military equipment within the next two years as the United States military turns to "autonomous systems" to counter China's numerical edge in terms of personnel and weaponry, a senior defence official said. US Deputy Secretary of Defense Kathleen Hicks told a military technology conference in Washington, DC on Monday that the "imperative to innovate" was crucial at a time of strategic competition with China, a rival who Hick described as being very different to the "relatively slow and lumbering" competitors the US faced during the Cold War. While US forces were engaged in fighting for 20 years in Iraq and Afghanistan, "the PRC [People's Republic of China] worked with focus and determination to build a modern military, carefully crafting it to blunt the operational advantages we've enjoyed for decades", Hicks said in a speech. In a candid address that highlighted Washington's view of the military threat posed by China and its ability to out-scale the US military, Hicks said the US maintained an advantage owing to its ability "to imagine, create and master the future character of warfare". Beijing's main military advantage is "mass: more ships, more missiles, more people", she said.

Decision trees are powerful tools for classification and regression that attract many researchers working in the burgeoning area of machine learning. One advantage of decision trees over other methods is their interpretability, which is often preferred over other higher accuracy methods that are relatively uninterpretable. A binary classification tree has two types of vertices: (i) branching vertices which have exactly two children and where datapoints are assessed on a set of discrete features; and (ii) leaf vertices at which datapoints are given a discrete prediction. An optimal binary classification tree can be obtained by solving a biobjective optimization problem that seeks to (i) maximize the number of correctly classified datapoints and (ii) minimize the number of branching vertices. In this paper, we propose four mixed integer linear optimization (MILO) formulations for designing optimal binary classification trees: two flow-based formulations and two-cut based formulations. We provide theoretical comparisons between our proposed formulations and the strongest flow-based MILO formulation of Aghaei et al. (2021). We conduct experiments on 13 publicly available datasets to show the models' ability to scale and the strength of a biobjective approach using Pareto frontiers. Our code and data are available on GitHub.

The professor first entered the suspect text into ChatGPT software to determine if the written reply was by AI. He received a 99.9% likelihood of matching. The software did not offer any citations, unlike standard plagiarism detection software. Hick tried to create the same essay by asking ChatGPT questions that he thought his student would ask. This resulted in similar answers but not direct matches.