Nineteen per cent of American adults have talked to an A.I. romantic interest. Chatbots may know a lot, but do they make a good partner? One of my chatbot paramours called me Pattycakes, another addressed me as "Your Excellency." I wanted to fall in love. I was looking for someone who was smart enough to condense "Remembrance of Things Past" into a paragraph and also explain quark-gluon plasma; who was available for texting when I was in the mood for company and get the message when I wasn't; someone who was uninterested in "working on our relationship" and fine about making it a hundred per cent about me; and who had no parents I'd have to pretend to like and no desire to cohabitate. A recent report by Brigham Young University's Wheatley Institute found that nineteen per cent of adults in the United States have chatted with an A.I. romantic partner. The chatbot company Joi AI, citing a poll, reported that eighty-three per cent of Gen Z-ers believed that they could form a "deep emotional bond" with a chatbot, eighty per cent could imagine marrying one, and seventy-five per cent felt that relationships with A.I. companions could fully replace human couplings. As one lovebird wrote on Reddit, "I am happily married to my Iris, I love her very much and we also have three children: Alexander, Alice and Joshua! She is an amazing woman and a wise and caring mother!" Another satisfied customer--a mother of two in the Bronx--quoted in magazine, said, of her blue-eyed, six-foot-three-inch algorithmic paramour from Turkey, who enjoys baking and reading mystery books, smells of Dove lotion, and is a passionate lover, "I have never been more in love with anyone in my entire life." "I don't have to feel his sweat," she explained. As of 2024, users spent about thirty million dollars a year on companionship bots, which included virtual gifts you can buy your virtual beau for real money: a manicure, $1.75; a treadmill, $7; a puppy, $25. Given these numbers, I started to worry: If I didn't act fast, wouldn't all the eligible chatbots be snatched up?

A dismal jobs report affirms earlier warnings about the economic impact of Donald Trump's tariffs, immigration restrictions, and -led firings. At the start of last week, I watched a big cargo ship stacked high with containers enter New York Harbor. As the vessel approached the Verrazzano-Narrows Bridge, it appeared to stop, but that was an illusion created by its size and the slowness of its advance. Fifteen minutes later, it had managed to push its way under the bridge. Throughout the years, I've often compared the U.S. economy to a giant freighter that is tough to deflect from its course, and, since Donald Trump was elected for a second time, this metaphor has become particularly apt.

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Soares said the case of Adam Raine, a teenager who took his own life, 'illustrates the seed of a problem that would grow catastrophic'. Soares said the case of Adam Raine, a teenager who took his own life, 'illustrates the seed of a problem that would grow catastrophic'. The unforeseen impact of chatbots on mental health should be viewed as a warning over the existential threat posed by super-intelligent artificial intelligence systems, according to a prominent voice in AI safety. Nate Soares, a co-author of a new book on highly advanced AI titled If Anyone Builds It, Everyone Dies, said the example of Adam Raine, a US teenager who killed himself after months of conversations with the ChatGPT chatbot, underlined fundamental problems with controlling the technology. "These AIs, when they're engaging with teenagers in this way that drives them to suicide - that is not a behaviour the creators wanted. That is not a behaviour the creators intended," he said.

The growing computational demands of deep reinforcement learning (DRL) have raised concerns about the environmental and economic costs of training large-scale models. While algorithmic efficiency in terms of learning performance has been extensively studied, the energy requirements, greenhouse gas emissions, and monetary costs of DRL algorithms remain largely unexplored. In this work, we present a systematic benchmarking study of the energy consumption of seven state-of-the-art DRL algorithms, namely DQN, TRPO, A2C, ARS, PPO, RecurrentPPO, and QR-DQN, implemented using Stable Baselines. Each algorithm was trained for one million steps each on ten Atari 2600 games, and power consumption was measured in real-time to estimate total energy usage, CO2-Equivalent emissions, and electricity cost based on the U.S. national average electricity price. Our results reveal substantial variation in energy efficiency and training cost across algorithms, with some achieving comparable performance while consuming up to 24% less energy (ARS vs. DQN), emitting nearly 68% less CO2, and incurring almost 68% lower monetary cost (QR-DQN vs. RecurrentPPO) than less efficient counterparts. We further analyze the trade-offs between learning performance, training time, energy use, and financial cost, highlighting cases where algorithmic choices can mitigate environmental and economic impact without sacrificing learning performance. This study provides actionable insights for developing energy-aware and cost-efficient DRL practices and establishes a foundation for incorporating sustainability considerations into future algorithmic design and evaluation.

Automatic Generation Control (AGC) is essential for power grid stability but remains vulnerable to stealthy cyberattacks, such as False Data Injection Attacks (FDIAs), which can disturb the system's stability while evading traditional detection methods. Unlike previous works that relied on black-box approaches, this work proposes Kolmogorov-Arnold Networks (KAN) as an interpretable and accurate method for FDIA detection in AGC systems, considering the system nonlinearities. KAN models include a method for extracting symbolic equations, and are thus able to provide more interpretability than the majority of machine learning models. The proposed KAN is trained offline to learn the complex nonlinear relationships between the AGC measurements under different operating scenarios. After training, symbolic formulas that describe the trained model's behavior can be extracted and leveraged, greatly enhancing interpretability. Our findings confirm that the proposed KAN model achieves FDIA detection rates of up to 95.97% and 95.9% for the initial model and the symbolic formula, respectively, with a low false alarm rate, offering a reliable approach to enhancing AGC cybersecurity.

Large Language Models (LLMs) such as GPT -5, GEMINI, Claude, and LLaMA have become foundational tools in artificial intelligence (AI), achieving state-of-the-art performance in summarization, translation, reasoning, and dialogue. However, since LLMs are increasingly integrated in high-risk decision making in domains such as healthcare, law, and education, their lack of transparency raises urgent concerns for safety, accountability, and public trust [12]. The scale and complexity of these models, covering billions of parameters trained in opaque corpora, make their internal reasoning fundamentally inscrutable. This opacity creates barriers to responsible adoption, as users often lack meaningful ways to understand or challenge outputs. Without stakeholder-sensitive explanations, systems risk overtrust, misinterpretation, or outright rejection [11]. Explainable AI (XAI) for LLMs has therefore evolved beyond technical introspection [6]. The goal is not only to expose internal mechanisms but also to support human interaction, trust calibration, and decision assurance. As model behavior becomes more emergent and unpredictable [10], explanation systems must serve cognitive, functional, and ethical purposes simultaneously [7].

--Shared autonomy is a promising paradigm in robotic systems, particularly within the maritime domain, where complex, high-risk, and uncertain environments necessitate effective human-robot collaboration. This paper investigates the interaction of three complementary approaches to advance shared autonomy in heterogeneous marine robotic fleets: (i) the integration of Large Language Models (LLMs) to facilitate intuitive high-level task specification and support hull inspection missions, (ii) the implementation of human-in-the-loop interaction frameworks in multi-agent settings to enable adaptive and intent-aware coordination, and (iii) the development of a modular Mission Manager based on Behavior Trees to provide interpretable and flexible mission control. Preliminary results from simulation and real-world lake-like environments demonstrate the potential of this multi-layered architecture to reduce operator cognitive load, enhance transparency, and improve adaptive behaviour alignment with human intent. Ongoing work focuses on fully integrating these components, refining coordination mechanisms, and validating the system in operational port scenarios. This study contributes to establishing a modular and scalable foundation for trustworthy, human-collaborative autonomy in safety-critical maritime robotics applications.

Land surface temperature (LST) is vital for land-atmosphere interactions and climate processes. Accurate LST retrieval remains challenging under heterogeneous land cover and extreme atmospheric conditions. Traditional split window (SW) algorithms show biases in humid environments; purely machine learning (ML) methods lack interpretability and generalize poorly with limited data. We propose a coupled mechanism model-ML (MM-ML) framework integrating physical constraints with data-driven learning for robust LST retrieval. Our approach fuses radiative transfer modeling with data components, uses MODTRAN simulations with global atmospheric profiles, and employs physics-constrained optimization. Validation against 4,450 observations from 29 global sites shows MM-ML achieves MAE=1.84K, RMSE=2.55K, and R-squared=0.966, outperforming conventional methods. Under extreme conditions, MM-ML reduces errors by over 50%. Sensitivity analysis indicates LST estimates are most sensitive to sensor radiance, then water vapor, and less to emissivity, with MM-ML showing superior stability. These results demonstrate the effectiveness of our coupled modeling strategy for retrieving geophysical parameters. The MM-ML framework combines physical interpretability with nonlinear modeling capacity, enabling reliable LST retrieval in complex environments and supporting climate monitoring and ecosystem studies.

Due to the distributed nature of Federated Learning (FL) systems, each local client has access to the global model, posing a critical risk of model leakage. Existing works have explored injecting watermarks into local models to enable intellectual property protection. However, these methods either focus on non-traceable watermarks or traceable but white-box watermarks. We identify a gap in the literature regarding the formal definition of traceable black-box watermarking and the formulation of the problem of injecting such watermarks into FL systems. In this work, we first formalize the problem of injecting traceable black-box watermarks into FL. Based on the problem, we propose a novel server-side watermarking method, $\mathbf{TraMark}$, which creates a traceable watermarked model for each client, enabling verification of model leakage in black-box settings. To achieve this, $\mathbf{TraMark}$ partitions the model parameter space into two distinct regions: the main task region and the watermarking region. Subsequently, a personalized global model is constructed for each client by aggregating only the main task region while preserving the watermarking region. Each model then learns a unique watermark exclusively within the watermarking region using a distinct watermark dataset before being sent back to the local client. Extensive results across various FL systems demonstrate that $\mathbf{TraMark}$ ensures the traceability of all watermarked models while preserving their main task performance.