Rapid advances in AI are beginning to reshape national security. Destabilizing AI developments could rupture the balance of power and raise the odds of great-power conflict, while widespread proliferation of capable AI hackers and virologists would lower barriers for rogue actors to cause catastrophe. Superintelligence -- AI vastly better than humans at nearly all cognitive tasks -- is now anticipated by AI researchers. Just as nations once developed nuclear strategies to secure their survival, we now need a coherent superintelligence strategy to navigate a new period of transformative change. We introduce the concept of Mutual Assured AI Malfunction (MAIM): a deterrence regime resembling nuclear mutual assured destruction (MAD) where any state's aggressive bid for unilateral AI dominance is met with preventive sabotage by rivals. Given the relative ease of sabotaging a destabilizing AI project -- through interventions ranging from covert cyberattacks to potential kinetic strikes on datacenters -- MAIM already describes the strategic picture AI superpowers find themselves in. Alongside this, states can increase their competitiveness by bolstering their economies and militaries through AI, and they can engage in nonproliferation to rogue actors to keep weaponizable AI capabilities out of their hands. Taken together, the three-part framework of deterrence, nonproliferation, and competitiveness outlines a robust strategy to superintelligence in the years ahead.

Machine learning (ML) models serve as powerful tools for threat detection and mitigation; however, they also introduce potential new risks. Adversarial input can exploit these models through standard interfaces, thus creating new attack pathways that threaten critical network operations. As ML advancements progress, adversarial strategies become more advanced, and conventional defenses such as adversarial training are costly in computational terms and often fail to provide real-time detection. These methods typically require a balance between robustness and model performance, which presents challenges for applications that demand instant response. To further investigate this vulnerability, we suggest a novel strategy for detecting and mitigating adversarial attacks using eXplainable Artificial Intelligence (XAI). This approach is evaluated in real time within intrusion detection systems (IDS), leading to the development of a zero-touch mitigation strategy. Additionally, we explore various scenarios in the Radio Resource Control (RRC) layer within the Open Radio Access Network (O-RAN) framework, emphasizing the critical need for enhanced mitigation techniques to strengthen IDS defenses against advanced threats and implement a zero-touch mitigation solution. Extensive testing across different scenarios in the RRC layer of the O-RAN infrastructure validates the ability of the framework to detect and counteract integrated RRC-layer attacks when paired with adversarial strategies, emphasizing the essential need for robust defensive mechanisms to strengthen IDS against complex threats.

The rapid advancement of artificial intelligence has coincided with increasing concerns regarding the safe and ethical deployment of these systems. As AI models become more capable, ensuring that their behavior aligns with societal norms and safety standards has emerged as a critical research challenge. State-of-the-art alignment techniques--such as reinforcement learning from human feedback and rulebased fine-tuning--strive to constrain models to acceptable ethical behaviors. However, these methods face an inherent tension: while alignment is designed to prevent the disclosure of harmful or sensitive information, adversaries can leverage the gap between a model's potential and its restricted behavior through what is known as a jailbreak. In the context of AI, a jailbreak is any method that circumvents established safety protocols, effectively enabling functionalities that the system would otherwise suppress. Current jailbreaks typically deploy elaborate prompt constructions or optimization strategies; in contrast, in this paper we present the Context Compliance Attack (CCA), a simple optimization-free jailbreak. CCA leverages a basic yet critical design flaw--the reliance on client-supplied conversation history--to subvert the AI systems' safeguards and jailbreak them. This paper investigates the efficacy of CCA and explores its implications on current AI safety architectures.

Over the past decades, researchers have primarily focused on improving the generalization abilities of models, with limited attention given to regulating such generalization. However, the ability of models to generalize to unintended data (e.g., harmful or unauthorized data) can be exploited by malicious adversaries in unforeseen ways, potentially resulting in violations of model ethics. Non-transferable learning (NTL), a task aimed at reshaping the generalization abilities of deep learning models, was proposed to address these challenges. While numerous methods have been proposed in this field, a comprehensive review of existing progress and a thorough analysis of current limitations remain lacking. In this paper, we bridge this gap by presenting the first comprehensive survey on NTL and introducing NTLBench, the first benchmark to evaluate NTL performance and robustness within a unified framework. Specifically, we first introduce the task settings, general framework, and criteria of NTL, followed by a summary of NTL approaches. Furthermore, we emphasize the often-overlooked issue of robustness against various attacks that can destroy the non-transferable mechanism established by NTL. Experiments conducted via NTLBench verify the limitations of existing NTL methods in robustness. Finally, we discuss the practical applications of NTL, along with its future directions and associated challenges.

In recent decades, machine learning research has focused on developing vector-based representations for various types of data, including images, text, and time series [22]. Learning a meaningful representation space is a foundational task that accelerates research progress, as exemplified by the success of Large Language Models (LLMs) [182]. A complementary challenge is learning a distance function (defining a metric space) that encodes aspects of the data's internal structure. This task is known as distance metric learning, or simply metric learning [20]. Metric learning methods find applications in every field using algorithms relying on a distance such as the ubiquitous k-nearest neighbors classifier: Classification and clustering [195], recommendation systems [89], optimal transport [45], and dimension reduction [116, 186]. However, when using only a global distance, the set of available modeling tools to derive computational algorithms is limited and does not capture the intrinsic data structure. Hence, in this paper, we present a literature review of Riemannian metric learning, a generalization of metric learning that has recently demonstrated success across diverse applications, from causal inference [51, 59, 147] to generative modeling [100, 111, 170]. Unlike metric learning, Riemannian metric learning does not merely learn an embedding capturing distance information, but estimates a Riemannian metric characterizing distributions, curvature, and distances in the dataset, i.e. the Riemannian structure of the data.

The rise of generative artificial intelligence (AI) as a novel frontier that uniquely merges advanced levels of intelligence with revolutionary user experiences is redefining the AI landscape for future cellular networks. In particular, the transition towards 6G systems has introduced a myriad of challenges inherent to their AI-native network design, requiring innovative solutions to enable real-time network orchestration, intelligent decision-making, and adaptive dynamic configurations. Meanwhile, the envisioned user experiences for 6G are growing increasingly complex, exceeding the capabilities offered by vintage wireless technologies and conventional AI solutions to satisfy their advanced demands. With its disruptive impact evident across diverse fields, generative AI possesses immense potential to tackle these challenges, leveraging its exceptional capabilities to manage complex tasks, operate autonomously, and adapt seamlessly to scenarios beyond its training domain. Remarkably, generative AI provides a transformative opportunity for telecom and cellular networks to bridge this defined gap in 6G systems, thereby shifting towards a new era with cutting-edge AI innovations across the different system and user levels.

Humans frequently grasp, manipulate, and move objects. Interactive systems assist humans in these tasks, enabling applications in Embodied AI, human-robot interaction, and virtual reality. However, current methods in hand-object synthesis often neglect dynamics and focus on generating static grasps. The first part of this dissertation introduces dynamic grasp synthesis, where a hand grasps and moves an object to a target pose. We approach this task using physical simulation and reinforcement learning. We then extend this to bimanual manipulation and articulated objects, requiring fine-grained coordination between hands. In the second part of this dissertation, we study human-to-robot handovers. We integrate captured human motion into simulation and introduce a student-teacher framework that adapts to human behavior and transfers from sim to real. To overcome data scarcity, we generate synthetic interactions, increasing training diversity by 100x. Our user study finds no difference between policies trained on synthetic vs. real motions.

Many less-resourced languages struggle with a lack of large, task-specific datasets that are required for solving relevant tasks with modern transformer-based large language models (LLMs). On the other hand, many linguistic resources, such as dictionaries, are rarely used in this context despite their large information contents. We show how LLMs can be used to extend existing language resources in less-resourced languages for two important tasks: word-sense disambiguation (WSD) and word-sense induction (WSI). We approach the two tasks through the related but much more accessible word-in-context (WiC) task where, given a pair of sentences and a target word, a classification model is tasked with predicting whether the sense of a given word differs between sentences. We demonstrate that a well-trained model for this task can distinguish between different word senses and can be adapted to solve the WSD and WSI tasks. The advantage of using the WiC task, instead of directly predicting senses, is that the WiC task does not need pre-constructed sense inventories with a sufficient number of examples for each sense, which are rarely available in less-resourced languages. We show that sentence pairs for the WiC task can be successfully generated from dictionary examples using LLMs. The resulting prediction models outperform existing models on WiC, WSD, and WSI tasks. We demonstrate our methodology on the Slovene language, where a monolingual dictionary is available, but word-sense resources are tiny.

Large language models (LLMs) like OpenAI ChatGPT, Google Gemini, and GitHub Copilot are rapidly gaining traction in the software industry, but their full impact on software engineering remains insufficiently explored. Despite their growing adoption, there is a notable lack of formal, qualitative assessments of how LLMs are applied in real-world software development contexts. To fill this gap, we conducted semi-structured interviews with sixteen early-adopter professional developers to explore their use of LLMs throughout various stages of the software development life cycle. Our investigation examines four dimensions: people - how LLMs affect individual developers and teams; process - how LLMs alter software engineering workflows; product - LLM impact on software quality and innovation; and society - the broader socioeconomic and ethical implications of LLM adoption. Thematic analysis of our data reveals that while LLMs have not fundamentally revolutionized the development process, they have substantially enhanced routine coding tasks, including code generation, refactoring, and debugging. Developers reported the most effective outcomes when providing LLMs with clear, well-defined problem statements, indicating that LLMs excel with decomposed problems and specific requirements. Furthermore, these early-adopters identified that LLMs offer significant value for personal and professional development, aiding in learning new languages and concepts. Early-adopters, highly skilled in software engineering and how LLMs work, identified early and persisting challenges for software engineering, such as inaccuracies in generated content and the need for careful manual review before integrating LLM outputs into production environments. Our study provides a nuanced understanding of how LLMs are shaping the landscape of software development, with their benefits, limitations, and ongoing implications.

The growing computational demand for deep neural networks ( DNNs) has raised concerns about their energy consumption and carbon footprint, particularly as the size and complexity of the models continue to increase. To address these challenges, energy-efficient hardware and custom accelerators have become essential. Additionally, adaptable DNN s are being developed to dynamically balance performance and efficiency. The use of these strategies became more common to enable sustainable AI deployment. However, these efficiency-focused designs may also introduce vulnerabilities, as attackers can potentially exploit them to increase latency and energy usage by triggering their worst-case-performance scenarios. This new type of attack, called energy-latency attacks, has recently gained significant research attention, focusing on the vulnerability of DNN s to this emerging attack paradigm, which can trigger denial-of-service ( DoS) attacks. This paper provides a comprehensive overview of current research on energy-latency attacks, categorizing them using the established taxonomy for traditional adversarial attacks. We explore different metrics used to measure the success of these attacks and provide an analysis and comparison of existing attack strategies. We also analyze existing defense mechanisms and highlight current challenges and potential areas for future research in this developing field. The GitHub page for this work can be accessed at https://github.com/hbrachemi/Survey_energy_attacks/