Amazon held its first major product event of the year on Wednesday and, as expected, it was largely about Alexa. The company first announced its next-gen, AI-powered voice assistant back in 2023, but technical issues forced Amazon to delay its formal unveiling and rollout. An Alexa upgrade means that Amazon has a swathe of new devices ready to support the latest version of the voice assistant. Amazon's hardware chief, Panos Panay, and his devices and services team were at the event to show off Alexa . Here's a rundown of everything Amazon announced at its first devices event of 2025: After lots (and lots) of boring rambling about generative AI from Amazon CEO Andy Jassy at Wednesday's event, Panay took the mic to start sharing the actual news. Alexa is the name of the company's upgraded voice assistant.

Alibaba is giving people free access to its generative artificial intelligence models that can produce highly realistic videos and images from both text and image input. The company has announced that four variants of its Wan 2.1 series, the latest version of its generative AI technology, are now open source and can be downloaded and modified by users. Researchers, academics and commercial entities can all get them from Alibaba Cloud's ModelScope and Hugging Face platforms, both of which give people access to open-source AI models. As Reuters said, the models Alibaba has open sourced are called T2V-1.3B, T2V-14B, I2V-14B-720P and I2V-14B-480P, with the 14B indicating that the model can accept 14 billion parameters.

British universities have been warned to "stress-test" all assessments after new research revealed "almost all" undergraduates are using generative artificial intelligence (genAI) in their studies. A survey of 1,000 students – both domestic and international – found there had been an "explosive increase" in the use of genAI in the past 12 months. Almost nine out of 10 (88%) in the 2025 poll said they used tools such as ChatGPT for their assessments, up from 53% last year. The proportion using any AI tool surged from 66% in 2024 to 92% in 2025, meaning just 8% of students are not using AI, according to a report published by the Higher Education Policy Institute and Kortext, a digital etextbook provider. Josh Freeman, the report's author, said such dramatic changes in behaviour in just 12 months were almost unheard of, and warned: "Universities should take heed: generative AI is here to stay. "There are urgent lessons here for institutions," Freeman said. "Every assessment must be reviewed in case it can be completed easily using AI.

We propose a methodology that allows communication with Piping and Instrumentation Diagrams (P&IDs) using natural language. In particular, we represent P&IDs through the DEXPI data model as labeled property graphs and integrate them with Large Language Models (LLMs). The approach consists of three main parts: 1) P&IDs are cast into a graph representation from the DEXPI format using our pyDEXPI Python package. 2) A tool for generating P&ID knowledge graphs from pyDEXPI. 3) Integration of the P&ID knowledge graph to LLMs using graph-based retrieval augmented generation (graph-RAG). This approach allows users to communicate with P&IDs using natural language. It extends LLM's ability to retrieve contextual data from P&IDs and mitigate hallucinations. Leveraging the LLM's large corpus, the model is also able to interpret process information in PIDs, which could help engineers in their daily tasks. In the future, this work will also open up opportunities in the context of other generative Artificial Intelligence (genAI) solutions on P&IDs, and AI-assisted HAZOP studies.

Thematic analysis of social media posts provides a major understanding of public discourse, yet traditional methods often struggle to capture the complexity and nuance of unstructured, large-scale text data. This study introduces a novel methodology for thematic analysis that integrates tweet embeddings from pre-trained language models, dimensionality reduction using and matrix factorization, and generative AI to identify and refine latent themes. Our approach clusters compressed tweet representations and employs generative AI to extract and articulate themes through an agentic Chain of Thought (CoT) prompting, with a secondary LLM for quality assurance. This methodology is applied to tweets from the autistic community, a group that increasingly uses social media to discuss their experiences and challenges. By automating the thematic extraction process, the aim is to uncover key insights while maintaining the richness of the original discourse. This autism case study demonstrates the utility of the proposed approach in improving thematic analysis of social media data, offering a scalable and adaptable framework that can be applied to diverse contexts. The results highlight the potential of combining machine learning and Generative AI to enhance the depth and accuracy of theme identification in online communities.

Generative artificial intelligence (generative AI) has entered the mainstream culture and become a subject of extensive academic investigation. However, the character and background of its impact on art require subtler scrutiny and more nuanced contextualization. This paper summarizes a broader study of the roles that AI's conceptual and ideological substrata play in influencing art notions. The focus is on divergent but coalescing and often questionable ideas, values, and political views that generative AI and other art-related AI technologies propagate from the computer science and AI/tech industry to the contemporary art and culture. The paper maps the main areas of this complex relationship and concisely critiques their key aspects.

This literature review interrogates the intersections between artificial intelligence, poetry, and art, offering a comprehensive exploration of both historical evolution and current debates in digital creative practices. It traces the development of computer-generated poetry from early template-based systems to generative models, critically assessing evaluative frameworks such as adaptations of the Turing Test, the FACE model, and ProFTAP. It also examines how these frameworks endeavour to measure creativity, semantic coherence, and cultural relevance in AI-generated texts, whilst highlighting the persistent challenges in replicating the nuance of human poetic expression. The review contributes a Marketing Theory discussion that deconstructs the figurative marketing narratives employed by AI companies, which utilise sanitised language and anthropomorphic metaphors to humanise their technologies. This discussion reveals the reductive nature of such narratives and underscores the tension between algorithmic precision and the realities of human creativity.The review also incorporates an auto-ethnographic account that offers a self-reflexive commentary on its own composition. By acknowledging the use of AI in crafting this review, the auto-ethnographic account destabilises conventional notions of authorship and objectivity, resonating with deconstruction and challenging logocentric assumptions in academic discourse. Ultimately, the review calls for a re-evaluation of creative processes that recognises the interdependence of technological innovation and human subjectivity. It advocates for interdisciplinary dialogue addressing ethical, cultural, and philosophical concerns, while reimagining the boundaries of artistic production.

Peer review is a critical process for ensuring the integrity of published scientific research. Confidence in this process is predicated on the assumption that experts in the relevant domain give careful consideration to the merits of manuscripts which are submitted for publication. With the recent rapid advancements in large language models (LLMs), a new risk to the peer review process is that negligent reviewers will rely on LLMs to perform the often time consuming process of reviewing a paper. However, there is a lack of existing resources for benchmarking the detectability of AI text in the domain of peer review. To address this deficiency, we introduce a comprehensive dataset containing a total of 788,984 AI-written peer reviews paired with corresponding human reviews, covering 8 years of papers submitted to each of two leading AI research conferences (ICLR and NeurIPS). We use this new resource to evaluate the ability of 18 existing AI text detection algorithms to distinguish between peer reviews written by humans and different state-of-the-art LLMs. Motivated by the shortcomings of existing methods, we propose a new detection approach which surpasses existing methods in the identification of AI written peer reviews. Our work reveals the difficulty of identifying AI-generated text at the individual peer review level, highlighting the urgent need for new tools and methods to detect this unethical use of generative AI.

The multivariate, asynchronous nature of real-world clinical data, such as that generated in Intensive Care Units (ICUs), challenges traditional AI-based decision-support systems. These often assume data regularity and feature independence and frequently rely on limited data scopes and manual feature engineering. The potential of generative AI technologies has not yet been fully exploited to analyze clinical data. We introduce ICU-BERT, a transformer-based model pre-trained on the MIMIC-IV database using a multi-task scheme to learn robust representations of complex ICU data with minimal preprocessing. ICU-BERT employs a multi-token input strategy, incorporating dense embeddings from a biomedical Large Language Model to learn a generalizable representation of complex and multivariate ICU data. With an initial evaluation of five tasks and four additional ICU datasets, ICU-BERT results indicate that ICU-BERT either compares to or surpasses current performance benchmarks by leveraging fine-tuning. By integrating structured and unstructured data, ICU-BERT advances the use of foundational models in medical informatics, offering an adaptable solution for clinical decision support across diverse applications.

Research in AI for Science often focuses on using AI technologies to augment components of the scientific process, or in some cases, the entire scientific method; how about AI for scientific publications? Peer-reviewed journals are foundational repositories of specialized knowledge, written in discipline-specific language that differs from general Internet content used to train most large language models (LLMs) and vision-language models (VLMs). We hypothesized that by combining a family of scientific journals with generative AI models, we could invent novel tools for scientific communication, education, and clinical care. We converted 23,000 articles from Neurosurgery Publications into a multimodal database - NeuroPubs - of 134 million words and 78,000 image-caption pairs to develop six datasets for building AI models. We showed that the content of NeuroPubs uniquely represents neurosurgery-specific clinical contexts compared with broader datasets and PubMed. For publishing, we employed generalist VLMs to automatically generate graphical abstracts from articles. Editorial board members rated 70% of these as ready for publication without further edits. For education, we generated 89,587 test questions in the style of the ABNS written board exam, which trainee and faculty neurosurgeons found indistinguishable from genuine examples 54% of the time. We used these questions alongside a curriculum learning process to track knowledge acquisition while training our 34 billion-parameter VLM (CNS-Obsidian). In a blinded, randomized controlled trial, we demonstrated the non-inferiority of CNS-Obsidian to GPT-4o (p = 0.1154) as a diagnostic copilot for a neurosurgical service. Our findings lay a novel foundation for AI with Science and establish a framework to elevate scientific communication using state-of-the-art generative artificial intelligence while maintaining rigorous quality standards.