Such models, recently coined foundation models, have been transformational to the field of natural language processing.

Teenager who lost his legs in crash will'never forgive' driver 38 minutes agoShareSaveKen Banks and Louise HosieBBC Scotland NewsShareSaveBBC Adam Golebiewski had a double amputation after the crash last year A teenager who lost his lower legs in a crash says he "will never forgive" the drink-driver at the wheel. Young footballer Adam Golebiewski, 18, had been a passenger in Arran Paterson's car in Macduff, Aberdeenshire, in September last year. Paterson, 19, admitted dangerous driving, being over the drink-drive limit and driving without insurance at Aberdeen Sheriff Court. Adam walked into court unaided on prosthetic legs following intensive rehabilitation. He said: "I want to try to enjoy life again and stay positive."

A terrifying societal collapse worthy of Hollywood can never be entirely ruled out. But according to one former Google executive, it may come a lot sooner than we expected. Mo Gawdat, a tech entrepreneur and author who spent 11 years at Google, has given a bleak warning about the near-future of society. Speaking with The Diary of a CEO podcast, Mr Gawdat said we'll be living in a dystopia in just two years' time. Sounding worthy of George Orwell's novel '1984', the dystopia will last up to 15 years, the expert said.

It took just minutes for a new Greek-made anti-drone system to show what it is capable of. On its first test run with a European Union patrol in the Red Sea a year ago, the Centauros system detected and swiftly brought down two aerial drones launched by Yemen's Houthis, who have been attacking merchant vessels in the busy shipping lane. Another two drones swiftly retreated: Centauros had jammed their electronics, said Kyriakos Enotiadis, electronics director at state-run Hellenic Aerospace Industry (HAI), which produces the anti-drone system.

Despite advancements in Natural Language Processing (NLP), models remain vulnerable to adversarial attacks, such as synonym substitutions. While prior work has focused on improving robustness for feed-forward and convolutional architectures, the robustness of recurrent networks and modern state space models (SSMs), such as S4, remains understudied. These architectures pose unique challenges due to their sequential processing and complex parameter dynamics. In this paper, we introduce a novel regularization technique based on Growth Bound Matrices (GBM) to improve NLP model robustness by reducing the impact of input perturbations on model outputs. We focus on computing the GBM for three architectures: Long Short-Term Memory (LSTM), State Space models (S4), and Convolutional Neural Networks (CNN). Our method aims to (1) enhance resilience against word substitution attacks, (2) improve generalization on clean text, and (3) providing the first systematic analysis of SSM (S4) robustness. Extensive experiments across multiple architectures and benchmark datasets demonstrate that our method improves adversarial robustness by up to 8.8% over existing baselines. These results highlight the effectiveness of our approach, outperforming several state-of-the-art methods in adversarial defense. Codes are available at https://github.com/BouriMohammed/GBM

Sentiment analysis in low-resource, culturally nuanced contexts challenges conventional NLP approaches that assume fixed labels and universal affective expressions. We present a diagnostic framework that treats sentiment as a context-dependent, culturally embedded construct, and evaluate how large language models (LLMs) reason about sentiment in informal, code-mixed WhatsApp messages from Nairobi youth health groups. Using a combination of human-annotated data, sentiment-flipped counterfactuals, and rubric-based explanation evaluation, we probe LLM interpretability, robustness, and alignment with human reasoning. Framing our evaluation through a social-science measurement lens, we operationalize and interrogate LLMs outputs as an instrument for measuring the abstract concept of sentiment. Our findings reveal significant variation in model reasoning quality, with top-tier LLMs demonstrating interpretive stability, while open models often falter under ambiguity or sentiment shifts. This work highlights the need for culturally sensitive, reasoning-aware AI evaluation in complex, real-world communication.

OpenAI is partnering with the US government to make its leading frontier models available to federal employees. Under the agreement, federal agencies can access OpenAI's models for 1 for the next year, per a Wednesday announcement from the company and the General Services Administration (GSA). The partnership is the culmination of months of effort on the part of OpenAI CEO Sam Altman and other OpenAI executives, who have been cozying up to the Trump administration since before President Donald Trump retook the White House in January. Since at least May of this year, high-ranking OpenAI employees have been meeting with the GSA and other government agencies, such as the Food and Drug Administration, to promote the company's tools, according to documents obtained by WIRED. On July 23, OpenAI chief operating officer Brad Lightcap and other OpenAI executives were invited to a private after-party hosted by the Hill and Valley Forum in Washington, DC.

We study the optimization and sample complexity of gradient-based training of a two-layer neural network with quadratic activation function in the high-dimensional regime, where the data is generated as $y \propto \sum_{j=1}^{r}λ_j σ\left(\langle \boldsymbol{θ_j}, \boldsymbol{x}\rangle\right), \boldsymbol{x} \sim N(0,\boldsymbol{I}_d)$, $σ$ is the 2nd Hermite polynomial, and $\lbrace\boldsymbolθ_j \rbrace_{j=1}^{r} \subset \mathbb{R}^d$ are orthonormal signal directions. We consider the extensive-width regime $r \asymp d^β$ for $β\in [0, 1)$, and assume a power-law decay on the (non-negative) second-layer coefficients $λ_j\asymp j^{-α}$ for $α\geq 0$. We present a sharp analysis of the SGD dynamics in the feature learning regime, for both the population limit and the finite-sample (online) discretization, and derive scaling laws for the prediction risk that highlight the power-law dependencies on the optimization time, sample size, and model width. Our analysis combines a precise characterization of the associated matrix Riccati differential equation with novel matrix monotonicity arguments to establish convergence guarantees for the infinite-dimensional effective dynamics.

--W e address the problem of scarcity of educational Arabic Language Learning tools that advocates modern pedagogical models such active learning which ensures language proficiency . In fact, we investigate the design and evaluation of an AI-powered educational tool designed to enhance Arabic language learning for non-native speakers with beginner-to-intermediate proficiency level. The tool leverages advanced AI models to generate interactive visual quizzes, deploying Visual Question Answering as the primary activity . Adopting a constructivist learning approach, the system encourages active learning through real-life visual quizzes, and image-based questions that focus on improving vocabulary, grammar, and comprehension. The system integrates Vision-Language Pretraining models to generate contextually relevant image description from which Large Language Model generate assignments based on customized Arabic language Learning quizzes thanks to prompting. The effectiveness of the tool is evaluated through a manual annotated benchmark consisting of 1266 real-life visual quizzes, with human participants providing feedback. The results show a suitable accuracy rates, validating the tool's potential to bridge the gap in Arabic language education and highlighting the tool's promise as a reliable, AI-powered resource for Arabic learners, offering personalized and interactive learning experiences. I. Introduction Language learning has never been more important than it is today. Since the onset of globalization, language learning has become essential in facilitating communication across cultures and opening up numerous educational and professional opportunities [6]. To excel in any language, it is crucial to develop proficiency in all four core skills: listening, writing, reading, and speaking.

The rapid expansion of publicly-available medical data presents a challenge for clinicians and researchers alike, increasing the gap between the volume of scientific literature and its applications. The steady growth of studies and findings overwhelms medical professionals at large, hindering their ability to systematically review and understand the latest knowledge. This paper presents an approach to information extraction and automatic knowledge graph (KG) generation to identify and connect biomedical knowledge. Through a pipeline of large language model (LLM) agents, the system decomposes 44 PubMed abstracts into semantically meaningful proposition sentences and extracts KG triples from these sentences. The triples are enhanced using a combination of open domain and ontology-based information extraction methodologies to incorporate ontological categories. On top of this, a context variable is included during extraction to allow the triple to stand on its own - thereby becoming `quadruples'. The extraction accuracy of the LLM is validated by comparing natural language sentences generated from the enhanced triples to the original propositions, achieving an average cosine similarity of 0.874. The similarity for generated sentences of enhanced triples were compared with generated sentences of ordinary triples showing an increase as a result of the context variable. Furthermore, this research explores the ability for LLMs to infer new relationships and connect clusters in the knowledge base of the knowledge graph. This approach leads the way to provide medical practitioners with a centralised, updated in real-time, and sustainable knowledge source, and may be the foundation of similar gains in a wide variety of fields.