From iPhones to Apple Watches, Apple is known for its incredible range of gadgets. Now, the tech giant is about to launch a brand new product - and there's not long to wait to see it. Tim Cook, CEO at Apple, has revealed that a new device is coming on Wednesday 19 February. He posted a video of a holographic Apple logo on X (formerly Twitter), writing: 'Get ready to meet the newest member of the family. While further details are yet to be announced, the new device is widely rumoured to be Apple's latest budget iPhone SE.

The Dating Apps Reporting Project is an 18-month investigation. It was produced in partnership with the Pulitzer Center's AI Accountability Network and the Markup, now a part of CalMatters, and co-published with the Guardian and the 19th. When a young woman in Denver met up with a smiling cardiologist she matched with on the dating app Hinge, she had no way of knowing that the company behind the app had already received reports from two other women who had accused him of rape. She met the 34-year-old doctor with green eyes and thinning hair at Highland Tap & Burger, a sports bar in a trendy neighborhood. It went well enough that she accepted an invitation to go back to his apartment. As she emerged from his bathroom, he handed her a tequila soda. What transpired over the next 24 hours, according to court testimony, reads like every person's dating app nightmare. After sipping the drink, the woman started to lose control. She fell to the ground, and the man started to film her. He put her in a headlock, kissing her forehead; she struggled to free herself but managed to grab her things and leave. He followed her out the door, holding her shoes and trying to force her back inside, but she was able to call an Uber, vomiting in the car on the way home. She woke up at home, soaking wet on her bathroom floor, the key to her house still in her door. She continued vomiting for hours.

Offline Reinforcement Learning (RL) algorithms learn a policy using a fixed training dataset, which is then deployed online to interact with the environment and make decisions. Transformers, a standard choice for modeling time-series data, are gaining popularity in offline RL. In this context, Beam Search (BS), an approximate inference algorithm, is the go-to decoding method. Offline RL eliminates the need for costly or risky online data collection. However, the restricted dataset induces uncertainty as the agent may encounter unfamiliar sequences of states and actions during execution that were not covered in the training data. In this context, BS lacks two important properties essential for offline RL: It does not account for the aforementioned uncertainty, and its greedy left-right search approach often results in sequences with minimal variations, failing to explore potentially better alternatives. To address these limitations, we propose Portfolio Beam Search (PBS), a simple-yet-effective alternative to BS that balances exploration and exploitation within a Transformer model during decoding. We draw inspiration from financial economics and apply these principles to develop an uncertainty-aware diversification mechanism, which we integrate into a sequential decoding algorithm at inference time. We empirically demonstrate the effectiveness of PBS on the D4RL locomotion benchmark, where it achieves higher returns and significantly reduces outcome variability.

The pervasiveness of large language models and generative AI in online media has amplified the need for effective automated fact-checking to assist fact-checkers in tackling the increasing volume and sophistication of misinformation. The complex nature of fact-checking demands that automated fact-checking systems provide explanations that enable fact-checkers to scrutinise their outputs. However, it is unclear how these explanations should align with the decision-making and reasoning processes of fact-checkers to be effectively integrated into their workflows. Through semi-structured interviews with fact-checking professionals, we bridge this gap by: (i) providing an account of how fact-checkers assess evidence, make decisions, and explain their processes; (ii) examining how fact-checkers use automated tools in practice; and (iii) identifying fact-checker explanation requirements for automated fact-checking tools. The findings show unmet explanation needs and identify important criteria for replicable fact-checking explanations that trace the model's reasoning path, reference specific evidence, and highlight uncertainty and information gaps.

Recent attention to anthropomorphism -- the attribution of human-like qualities to non-human objects or entities -- of language technologies like LLMs has sparked renewed discussions about potential negative impacts of anthropomorphism. To productively discuss the impacts of this anthropomorphism and in what contexts it is appropriate, we need a shared vocabulary for the vast variety of ways that language can be anthropomorphic. In this work, we draw on existing literature and analyze empirical cases of user interactions with language technologies to develop a taxonomy of textual expressions that can contribute to anthropomorphism. We highlight challenges and tensions involved in understanding linguistic anthropomorphism, such as how all language is fundamentally human and how efforts to characterize and shift perceptions of humanness in machines can also dehumanize certain humans. We discuss ways that our taxonomy supports more precise and effective discussions of and decisions about anthropomorphism of language technologies.

Large language models (LLMs) can produce erroneous responses that sound fluent and convincing, raising the risk that users will rely on these responses as if they were correct. Mitigating such overreliance is a key challenge. Through a think-aloud study in which participants use an LLM-infused application to answer objective questions, we identify several features of LLM responses that shape users' reliance: explanations (supporting details for answers), inconsistencies in explanations, and sources. Through a large-scale, pre-registered, controlled experiment (N=308), we isolate and study the effects of these features on users' reliance, accuracy, and other measures. We find that the presence of explanations increases reliance on both correct and incorrect responses. However, we observe less reliance on incorrect responses when sources are provided or when explanations exhibit inconsistencies. We discuss the implications of these findings for fostering appropriate reliance on LLMs.

Originality: The authors propose a novel relaxation (to the best of my knowledge) for networks with ReLU activations that tighten previously proposed relaxations that ignore the correlations between neurons in the network. The theoretical results are also novel (although unsurprising). However, it would be useful for the authors to better clarify the computational requirements and tightness of k-ReLU relative to DeepPoly and other similar relaxations and bound propagation methods like [13] and https://arxiv.org/abs/1805.12514, Quality: The theoretical results are accurate (albeit unsurprising) in my opinion. The experimental section is missing several important details in my opinion: 1) The authors say that experiments are performed on both MNIST and CIFAR-10, but the tables 2/3 only report numbers on MNIST.

It is an interesting, reasoned and promising approach. But there a few issues which I would like to have clarified in the rebuttal to accept the paper. The idea of the paper seems to strongly rely on the paper "Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms" by Blondel et al., where ANOVA kernels have already been used. Can you explain in more detail the difference and contributions in comparison to this paper? I'm wondering why such approaches cannot be applied to the given problem or why it is not better to adapt them to HOFMs.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. The Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. In the first of our interviews with the 2025 cohort, we meet Kunpeng (Chris) Xu and find out more about his research and future plans. I am a final-year Ph.D. student at the ProspectUs-Lab, Université de Sherbrooke, Canada, where I have been working with Professor Shengrui Wang and Professor Lifei Chen since 2021. I explore data-driven kernel representation learning to develop more adaptive and expressive models for complex time series, while also investigating subspace learning and its applications in AI.

Data models are necessary for the birth of data and of any data-driven system. Indeed, every algorithm, every machine learning model, every statistical model, and every database has an underlying data model without which the system would not be usable. Hence, data models are excellent sites for interrogating the (material, social, political, ...) conditions giving rise to a data system. Towards this, drawing inspiration from literary criticism, we propose to closely read data models in the same spirit as we closely read literary artifacts. Close readings of data models reconnect us with, among other things, the materiality, the genealogies, the techne, the closed nature, and the design of technical systems. While recognizing from literary theory that there is no one correct way to read, it is nonetheless critical to have systematic guidance for those unfamiliar with close readings. This is especially true for those trained in the computing and data sciences, who too often are enculturated to set aside the socio-political aspects of data work. A systematic methodology for reading data models currently does not exist. To fill this gap, we present the CREDAL methodology for close readings of data models. We detail our iterative development process and present results of a qualitative evaluation of CREDAL demonstrating its usability, usefulness, and effectiveness in the critical study of data.