We consider the problem of tracking multiple, unknown, and time-varying numbers of objects using a distributed network of heterogeneous sensors. In an effort to derive a formulation for practical settings, we consider limited and unknown sensor field-of-views (FoVs), sensors with limited local computational resources and communication channel capacity. The resulting distributed multi-object tracking algorithm involves solving an NP-hard multidimensional assignment problem either optimally for small-size problems or sub-optimally for general practical problems. For general problems, we propose an efficient distributed multi-object tracking algorithm that performs track-to-track fusion using a clustering-based analysis of the state space transformed into a density space to mitigate the complexity of the assignment problem. The proposed algorithm can more efficiently group local track estimates for fusion than existing approaches. To ensure we achieve globally consistent identities for tracks across a network of nodes as objects move between FoVs, we develop a graph-based algorithm to achieve label consensus and minimise track segmentation. Numerical experiments with a synthetic and a real-world trajectory dataset demonstrate that our proposed method is significantly more computationally efficient than state-of-the-art solutions, achieving similar tracking accuracy and bandwidth requirements but with improved label consistency.

To understand how models resolve such conflicts, we study Llama-2-chat models on the forbidden fact task. Specifically, we instruct Llama-2 to truthfully complete a factual recall statement while forbidding it from saying the correct answer. This often makes the model give incorrect answers. We decompose Llama-2 into 1000+ components, and rank each one with respect to how useful it is for forbidding the correct answer. We find that in aggregate, around 35 components are enough to reliably implement the full suppression behavior. However, these components are fairly heterogeneous and many operate using faulty heuristics. We discover that one of these heuristics can be exploited via a manually designed adversarial attack which we call The California Attack. Our results highlight some roadblocks standing in the way of being able to successfully interpret advanced ML systems.

At least 10 people, including a child, have been killed and 45 injured following a Ukrainian attack on the centre of the Russian provincial capital of Belgorod, the Russian Emergencies Ministry has said. Governor Vyacheslav Gladkov said on Saturday that the attack on Belgorod, about 30km (19 miles) from the border with Ukraine, had hit a residential area. In a Telegram post, he urged all residents to move to air raid shelters as sirens sounded. Belgorod borders Ukraine's Luhansk, Sumy and Kharkiv regions, some of which were hit by Russian air raids on Ukraine on Friday, in what was one of the deadliest attacks since the war began in February 2022. The death toll has risen to 39 from those attacks.

Ukrainian President Volodomyr Zelenskyy gives his outlook on the conflict and offers an update on his countrys counter-offensive on Special Report. Russia's Defense Ministry said Ukraine launched a series of rocket and drone attacks into Russian territories with local officials reporting that two people, including a child, were killed in the attacks. Russia said 13 rockets and 32 drones were shot down over several Russian regions, according to Reuters. A child, born in 2014, was killed in the Bryansk region, while a man in the Belgorod region was also said to have died. Both regions are in western Russia and adjoin Ukraine.

When Nolan began working on the movie about the 20th century scientist, he says he had no idea it would be so relevant to this year's tech debate. He frequently discussed AI during his "Oppenheimer" media blitz, and in November, he was awarded the Federation of American Scientists' Public Service Award alongside policymakers working on artificial intelligence, including Sen. Charles E. Schumer (D-N.Y.), Sen. Todd C. Young (R-Ind.)

America's Newsroom anchor Bill Hemmer looks back at the top headlines of the past 12 months. The mainstream attention on artificial intelligence (AI) in 2023 allowed militaries to more openly discuss some of the astonishing initiatives they've undertaken as they race toward the future of warfare. AI presented an entirely different challenge and revealed an arms race many did not even know had already gotten well underway: Advanced and automated targeting capabilities, virtual environment weapon testing and AI-controlled vehicles present just the tip of a substantial and rapidly developing iceberg. The allure of AI is so strong that the Pentagon has some 800 AI-related unclassified projects in the works to attain a "force multiplier" integration and gain the upper hand over its rivals. This year gave the general public a better idea of where militaries stand with their astonishing development and where they might head next.

Ukrainian officials have said that at least 30 people have been killed and more than 140 wounded after Russia targeted cities across the war-torn country with a massive salvo of missiles and drones in one of the largest aerial assaults of the war. Russia's defence ministry has said its forces downed 32 Ukrainian drones over the Bryansk, Oryol, Kursk and Moscow regions overnight. Polish military authorities have said that a Russian missile briefly passed through the country's airspace on Friday, prompting concern from the country that borders Ukraine. Ukrainian officials have said that at least 30 people have been killed and more than 140 wounded after Russia targeted cities across the war-torn country with a massive salvo of missiles and drones in one of the largest aerial assaults of the war. Russia's defence ministry has said its forces downed 32 Ukrainian drones over the Bryansk, Oryol, Kursk and Moscow regions overnight.

For generative AI to succeed, how engaging a conversationalist must it be? For almost sixty years, some conversational agents have responded to any question or comment to keep a conversation going. In recent years, several utilized machine learning or sophisticated language processing, such as Tay, Xiaoice, Zo, Hugging Face, Kuki, and Replika. Unlike generative AI, they focused on engagement, not expertise. Millions of people were motivated to engage with them. What were the attractions? Will generative AI do better if it is equally engaging, or should it be less engaging? Prior to the emergence of generative AI, we conducted a large-scale quantitative and qualitative analysis to learn what motivated millions of people to engage with one such 'virtual companion,' Microsoft's Zo. We examined the complete chat logs of 2000 anonymized people. We identified over a dozen motivations that people had for interacting with this software. Designers learned different ways to increase engagement. Generative conversational AI does not yet have a clear revenue model to address its high cost. It might benefit from being more engaging, even as it supports productivity and creativity. Our study and analysis point to opportunities and challenges.

AI-Based Safety-Critical Systems (AI-SCS) are being increasingly deployed in the real world. These can pose a risk of harm to people and the environment. Reducing that risk is an overarching priority during development and operation. As more AI-SCS become autonomous, a layer of risk management via human intervention has been removed. Following an accident it will be important to identify causal contributions and the different responsible actors behind those to learn from mistakes and prevent similar future events. Many authors have commented on the "responsibility gap" where it is difficult for developers and manufacturers to be held responsible for harmful behaviour of an AI-SCS. This is due to the complex development cycle for AI, uncertainty in AI performance, and dynamic operating environment. A human operator can become a "liability sink" absorbing blame for the consequences of AI-SCS outputs they weren't responsible for creating, and may not have understanding of. This cross-disciplinary paper considers different senses of responsibility (role, moral, legal and causal), and how they apply in the context of AI-SCS safety. We use a core concept (Actor(A) is responsible for Occurrence(O)) to create role responsibility models, producing a practical method to capture responsibility relationships and provide clarity on the previously identified responsibility issues. Our paper demonstrates the approach with two examples: a retrospective analysis of the Tempe Arizona fatal collision involving an autonomous vehicle, and a safety focused predictive role-responsibility analysis for an AI-based diabetes co-morbidity predictor. In both examples our primary focus is on safety, aiming to reduce unfair or disproportionate blame being placed on operators or developers. We present a discussion and avenues for future research.

While most prior research into the universe of political discourses is based in the genres of debate and speeches, studies of academic political discourse have been sparse. One of the goals of the project SKILL, from which this paper stems, is to fill this gap. SKILL - A social science lab for research-based learning - is dedicated to building and applying AI technologies to facilitate analysis of argumentation in scholarly articles in political science, especially in the context of teaching International Relations (IR). The ultimate goal of SKILL is to provide students with AI tools which would facilitate comprehension of original articles used as part of teaching syllabi and which would coach them in producing expert argumentation in the field. In order to gain insight into the structure and properties of arguments in the domain of political science theory, we developed an annotation scheme which enables analysis of scholarly IR discourse in terms of interaction between argumentation and types of domain content contributing to arguments. The scheme comprises two orthogonal dimensions: discourse and content domain.