Generative AI, agentic AI, and other emerging technologies are morphing companies and driving businesses to rethink organizational structures and traditional roles. The research suggests current IT processes will not allow businesses to stay ahead of technology disruption, so even though IT continues to play a key role, advising the C-suite and guiding technology deployments across the organization, there is a need for IT to pivot away from traditional operating models towards specialized services focused on delivering value at the speed of need. A technology-first focus across the C-suite means answering the following questions: CEO -- how do I use technology to drive growth?; CIO -- how do I use technology to deliver value to the business?; CXO -- how do I use technology to make my function more productive and efficient? Also: ChatGPT's subscribers and revenue soar in 2025 - here's why Rethinking technology and the role of IT will drive a shift from the traditional model to a business technology-focused model.

Novelists Kate Mosse and Tracy Chevalier as well as poet and former Royal Society of Literature chair Daljit Nagra will be among those in attendance outside the company's King's Cross office. Protesters will meet at Granary Square at 1.30pm and a letter to Meta from the Society of Authors (SoA) will be hand-delivered at 1.45pm. It will also be sent to Meta headquarters in the US. Earlier this year, a US court filing alleged that Meta CEO Mark Zuckerberg approved the company's use of a notorious "shadow library", LibGen, which contains more than 7.5 million books. Last month, the Atlantic republished a searchable database of the titles contained in LibGen, through which many authors discovered their works may have been used to train Meta's AI models.

One person was killed and two others injured in a Russian overnight attack on southeast Ukraine's Zaporizhia region, Regional Governor Ivan Fedorov said. A Russian ballistic missile strike on Ukraine's Kryvyi Rih killed at least four people and injured 14 others, including two children, Ukrainian authorities said. An infant, a seven-year-old boy and six others were also injured in a drone attack on Ukraine's Kharkiv region, said Oleh Syniehubov, the region's governor. Kharkiv's Mayor Ihor Terekhov said 15 drone strikes were carried out in the region. At least 60 people were forced to evacuate from their homes in the Russian city of Kursk after falling debris from intercepted Ukrainian drones hit their apartment buildings, acting governor, Alexander Khinshtein, said.

The'Dimension 20' cast chooses their ultimate Intrepid Heroes squad Mashable Tech Science Life Social Good Entertainment Deals Shopping Games Search Cancel * * Search Result Tech Apps & Software Artificial Intelligence Cybersecurity Cryptocurrency Mobile Smart Home Social Media Tech Industry Transportation All Tech Science Space Climate Change Environment All Science Life Digital Culture Family & Parenting Health & Wellness Sex, Dating & Relationships Sleep Careers Mental Health All Life Social Good Activism Gender LGBTQ Racial Justice Sustainability Politics All Social Good Entertainment Games Movies Podcasts TV Shows Watch Guides All Entertainment SHOP THE BEST Laptops Budget Laptops Dating Apps Sexting Apps Hookup Apps VPNs Robot Vaccuums Robot Vaccum & Mop Headphones Speakers Kindles Gift Guides Mashable Choice Mashable Selects All Sex, Dating & Relationships All Laptops All Headphones All Robot Vacuums All VPN All Shopping Games Product Reviews Adult Friend Finder Bumble Premium Tinder Platinum Kindle Paperwhite PS5 vs PS5 Slim All Reviews All Shopping Deals Newsletters VIDEOS Mashable Shows All Videos Home Entertainment The'Dimension 20' cast chooses their ultimate Intrepid Heroes squad "I'm locked in in a way that I don't know how fun I'm going to be in this interview." By Mark Stetson and Belen Edwards Belen Edwards Entertainment Reporter Belen Edwards is an Entertainment Reporter at Mashable. She covers movies and TV with a focus on fantasy and science fiction, adaptations, animation, and more nerdy goodness. Read Full Bio on April 2, 2025 Share on Facebook Share on Twitter Share on Flipboard Watch Next The'Cobra Kai' cast chooses their ultimate'Karate Kid' squad 4:52 The'Dimension 20' cast reveal which campaign they would most like to revisit 8:48 'Dimension 20' heads to the wrestling ring in'Titan Takedown' trailer Amy Schumer and the'Kinda Pregnant' cast allow a Paper Magic 8 Ball to interview them 5:08 The Dimension 20 cast (Brennan Lee Mulligan, Ally Beardsley, Lou Wilson, Siobhan Thompson, and Zac Oyama) locked in to draft their ultimate teams of characters from Intrepid Heroes campaigns.Which PC will be the first pick? Which cast members betray each other in order to get the characters they want?

Sumitomo Mitsui Banking is tapping Fujitsu's artificial intelligence models to bolster its advisory services for customers grappling with rising wages and materials costs. The banking arm of Sumitomo Mitsui Financial Group is in talks to provide corporate client data to Fujitsu to run through the IT company's multimodal machine learning tools to make business forecasts, according to people familiar with the matter. The AI-inferred demand predictions would help some of the bank's biggest customers make key decisions from staffing to procurement to capital spending and financing, the people said, asking not to be named discussing nonpublic information. The two companies will soon sign a basic agreement, they said. The move would be a rare instance of a Japanese bank allowing another company access to sensitive customer data, such as store-by-store visitor and sales numbers.

The behavior of the random feature model in the high-dimensional regression framework has become a popular issue of interest in the machine learning literature}. This model is generally considered for feature vectors $x_i = \Sigma^{1/2} x_i'$, where $x_i'$ is a random vector made of independent and identically distributed (iid) entries, and $\Sigma$ is a positive definite matrix representing the covariance of the features. In this paper, we move beyond {\CB this standard assumption by studying the performances of the random features model in the setting of non-iid feature vectors}. Our approach is related to the analysis of the spectrum of large random matrices through random matrix theory (RMT) {\CB and free probability} results. We turn to the analysis of non-iid data by using the notion of variance profile {\CB which} is {\CB well studied in RMT.} Our main contribution is then the study of the limits of the training and {\CB prediction} risks associated to the ridge estimator in the random features model when its dimensions grow. We provide asymptotic equivalents of these risks that capture the behavior of ridge regression with random features in a {\CB high-dimensional} framework. These asymptotic equivalents, {\CB which prove to be sharp in numerical experiments}, are retrieved by adapting, to our setting, established results from operator-valued free probability theory. Moreover, {\CB for various classes of random feature vectors that have not been considered so far in the literature}, our approach allows to show the appearance of the double descent phenomenon when the ridge regularization parameter is small enough.

Knowledge Graph Completion with Mixed Geometry Tensor Factorization Viacheslav Yusupov Maxim Rakhuba Evgeny Frolov HSE University HSE University AIRI HSE University Abstract In this paper, we propose a new geometric approach for knowledge graph completion via low rank tensor approximation. We augment a pretrained and well-established Euclidean model based on a Tucker tensor decomposition with a novel hyperbolic interaction term. This correction enables more nuanced capturing of distributional properties in data better aligned with real-world knowledge graphs. By combining two geometries together, our approach improves expressivity of the resulting model achieving new state-of-the-art link prediction accuracy with a significantly lower number of parameters compared to the previous Euclidean and hyperbolic models. 1 INTRODUCTION Most of the information in the world can be expressed in terms of entities and the relationships between them. This information is effectively represented in the form of a knowledge graph (d'Amato, 2021; Peng et al., 2023), which serves as a repository for storing various forms of relational data with their interconnections. Particular examples include storing user profiles on social networking platforms (Xu et al., 2018), organizing Internet resources and the links between them, constructing knowledge bases that capture user preferences to enhance the functionality of recommender systems (Wang et al., 2019a; Guo et al., 2020). With the recent emergence of large language models (LLM), knowledge graphs have become an essential tool for improving the consistency and trustworthiness of linguis-Proceedings of the 28 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2025, Mai Khao, Thailand. Among notable examples of their application are fact checking (Pan et al., 2024), hallucinations mitigation (Agrawal et al., 2023), retrieval-augmented generation (Lewis et al., 2020), and generation of corpus for LLM pretraining (Agarwal et al., 2021). This utilization underscores the versatility and utility of knowledge graphs in managing complex datasets and facilitating the manipulation of interconnected information in various domains and downstream tasks. On the other hand, knowledge graphs may present an incomplete view of the world. Relations can evolve and change over time, be subject to errors, processing limitations, and gaps in available information.

As large language models (LLMs) and generative AI become widely adopted, guardrails have emerged as a key tool to ensure their safe use. However, adding guardrails isn't without tradeoffs; stronger security measures can reduce usability, while more flexible systems may leave gaps for adversarial attacks. In this work, we explore whether current guardrails effectively prevent misuse while maintaining practical utility. We introduce a framework to evaluate these tradeoffs, measuring how different guardrails balance risk, security, and usability, and build an efficient guardrail. Our findings confirm that there is no free lunch with guardrails; strengthening security often comes at the cost of usability. To address this, we propose a blueprint for designing better guardrails that minimize risk while maintaining usability. We evaluate various industry guardrails, including Azure Content Safety, Bedrock Guardrails, OpenAI's Moderation API, Guardrails AI, Nemo Guardrails, and Enkrypt AI guardrails. Additionally, we assess how LLMs like GPT-4o, Gemini 2.0-Flash, Claude 3.5-Sonnet, and Mistral Large-Latest respond under different system prompts, including simple prompts, detailed prompts, and detailed prompts with chain-of-thought (CoT) reasoning. Our study provides a clear comparison of how different guardrails perform, highlighting the challenges in balancing security and usability.

Probabilistic electricity price forecasting (PEPF) is a key task for market participants in short-term electricity markets. The increasing availability of high-frequency data and the need for real-time decision-making in energy markets require online estimation methods for efficient model updating. We present an online, multivariate, regularized distributional regression model, allowing for the modeling of all distribution parameters conditional on explanatory variables. Our approach is based on the combination of the multivariate distributional regression and an efficient online learning algorithm based on online coordinate descent for LASSO-type regularization. Additionally, we propose to regularize the estimation along a path of increasingly complex dependence structures of the multivariate distribution, allowing for parsimonious estimation and early stopping. We validate our approach through one of the first forecasting studies focusing on multivariate probabilistic forecasting in the German day-ahead electricity market while using only online estimation methods. We compare our approach to online LASSO-ARX-models with adaptive marginal distribution and to online univariate distributional models combined with an adaptive Copula. We show that the multivariate distributional regression, which allows modeling all distribution parameters - including the mean and the dependence structure - conditional on explanatory variables such as renewable in-feed or past prices provide superior forecasting performance compared to modeling of the marginals only and keeping a static/unconditional dependence structure. Additionally, online estimation yields a speed-up by a factor of 80 to over 400 times compared to batch fitting.

Scientific discovery is poised for rapid advancement through advanced robotics and artificial intelligence. Current scientific practices face substantial limitations as manual experimentation remains time-consuming and resource-intensive, while multidisciplinary research demands knowledge integration beyond individual researchers' expertise boundaries. Here, we envision an autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle. This system could dynamically interact with both physical and virtual environments while facilitating the integration of knowledge across diverse scientific disciplines. By deploying these technologies throughout every research stage -- spanning literature review, hypothesis generation, experimentation, and manuscript writing -- and incorporating internal reflection alongside external feedback, this system aims to significantly reduce the time and resources needed for scientific discovery. Building on the evolution from virtual AI scientists to versatile generalist AI-based robot scientists, AGS promises groundbreaking potential. As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws, potentially shaped by the number and capabilities of these autonomous systems, offering novel perspectives on how knowledge is generated and evolves. The adaptability of embodied robots to extreme environments, paired with the flywheel effect of accumulating scientific knowledge, holds the promise of continually pushing beyond both physical and intellectual frontiers.