A growing body of empirical work suggests that the widespread adoption of generative AI produces a significant homogenizing effect on information, creativity, and cultural production. I first develop a novel theoretical framework to explain this phenomenon. I argue that a dynamic of AI-derivative epistemology, in which individuals increasingly defer to AI outputs, allows a centralized AI Prism to function, a technical mechanism whose architecture is designed to reduce variance and converge on the statistical mean. This provides a causal explanation for the generative monocultures observed in recent studies. However, I contend this represents only the first stage of a more complex and dialectical process. This paper's central and paradoxical thesis is that the very homogenization that flattens knowledge within specialized domains simultaneously renders that knowledge into consistent modules that can be recombined across them, a process foundational to innovation and creativity. However, this recombinant potential is not automatic, but rather conditional. This paper argues that these opposing forces, homogenizing defaults versus recombinant possibilities, are governed by the nature of human engagement with the technology. The ultimate effect of generative AI is conditional on whether individuals act as passive consumers deferring to the AI's statistical outputs, or as active curators who critically interrogate, re-contextualize, and recombine them. The paper concludes by outlining the cognitive and institutional scaffolds required to resolve this tension, arguing they are the decisive variable that determine whether generative AI becomes an instrument of innovation or homogenization.

The accelerated evolution of large language models has raised questions about their comparative performance across domains of practical importance. GPT-4 by OpenAI introduced advances in reasoning, multimodality, and task generalization, establishing itself as a valuable tool in education, clinical diagnosis, and academic writing, though it was accompanied by several flaws. Released in August 2025, GPT-5 incorporates a system-of-models architecture designed for task-specific optimization and, based on both anecdotal accounts and emerging evidence from the literature, demonstrates stronger performance than its predecessor in medical contexts. This study provides one of the first systematic comparisons of GPT-4 and GPT-5 using human raters from linguistics and clinical fields. Twenty experts evaluated model-generated outputs across five domains: lesson planning, assignment evaluation, clinical diagnosis, research generation, and ethical reasoning, based on predefined criteria. Mixed-effects models revealed that GPT-5 significantly outperformed GPT-4 in lesson planning, clinical diagnosis, research generation, and ethical reasoning, while both models performed comparably in assignment assessment. The findings highlight the potential of GPT-5 to serve as a context-sensitive and domain-specialized tool, offering tangible benefits for education, clinical practice, and academic research, while also advancing ethical reasoning. These results contribute to one of the earliest empirical evaluations of the evolving capabilities and practical promise of GPT-5.

As generative AI continues to reshape artistic production and alternate modes of human expression, artists whose livelihoods are most directly affected have raised urgent concerns about consent, transparency, and the future of creative labor. However, the voices of artists are often marginalized in dominant public and scholarly discourse. This study presents a twelve-year analysis, from 2013 to 2025, of English-language discourse surrounding AI-generated art. It draws from 439 curated 500-word excerpts sampled from opinion articles, news reports, blogs, legal filings, and spoken-word transcripts. Through a reproducible methodology, we identify five stable thematic clusters and uncover a misalignment between artists' perceptions and prevailing media narratives. Our findings highlight how the use of technical jargon can function as a subtle form of gatekeeping, often sidelining the very issues artists deem most urgent. Our work provides a BERTopic-based methodology and a multimodal baseline for future research, alongside a clear call for deeper, transparency-driven engagement with artist perspectives in the evolving AI-creative landscape.

The artificial intelligence company Anthropic launched a National Security and Public Sector Advisory Council in efforts to deepen ties with Washington and allied governments as AI becomes increasingly central to defence. The San Francisco-based start-up announced the new panel on Wednesday. The council's launch underscores AI firms' growing efforts to shape policies and ensure their technology supports democratic interests amid global competition. Anthropic's new effort comes as rivals, such as OpenAI and Google DeepMind, step up engagement with governments and regulators on AI safety, though neither has announced a dedicated national security advisory council. Anthropic's council brings together former senators and senior officials from the US Department of Defense, intelligence agencies, as well as the Departments of Energy and Justice.

Due to the limits of science and its steep learning curve, we must rely on the expertise of others to develop our knowledge and skills.26 Toward this end, social media platforms have revolutionized how netizens--users who are actively engaged in online communities--gain knowledge and skills by facilitating the exchange of costless information with the public (for example, followers or influencers). Businesses around the world also use these platforms along with tools based on generative artificial intelligence (GenAI) to craft synthetic media, hoping to grow revenue by attracting more customers and improving their online experience.28 Generative AI tools can empower cyber threats and have cyberpsychological effects on netizens, allowing malicious actors to craft deepfakes in the form of disinformation, misinformation, and malinformation. Service providers not only must enhance GenAI tools to reduce hallucinations, but they also have a statutory duty to mitigate data-driven biases.

Non-alcoholic steatohepatitis (NASH), a severe form of fatty liver disease, is projected to become the leading cause of liver transplants globally. Despite advances in diagnostics, the lack of continuous, personalized patient engagement remains a key barrier to effective prevention and care. This post explores an innovative solution: MirrorLiver-MCP, a generative AI-powered conversational digital twin integrated with modular clinical pathways (MCP) to transform liver health management. As a researcher and AI practitioner, I've spent the past few years exploring how generative models, those that create text, dialogue, or even medical hypotheses, can move beyond novelty and become embedded in clinical workflows. The idea behind MirrorLiver-MCP arose from a simple question: "What if every patient had an AI-powered twin that could proactively coach them through lifestyle-based care before reaching irreversible liver damage?"

As cybercrime surges around the world, new research increasingly shows that ransomware is evolving as a result of widely available generative AI tools. In some cases, attackers are using AI to draft more intimidating and coercive ransom notes and conduct more effective extortion attacks. But cybercriminals' use of generative AI is rapidly becoming more sophisticated. Researchers from the generative AI company Anthropic today revealed that attackers are leaning on generative AI more heavily--sometimes entirely--to develop actual malware and offer ransomware services to other cybercriminals. Ransomware criminals have recently been identified using Anthropic's large language model Claude and its coding-specific model, Claude Code, in the ransomware development process, according to the company's newly released threat intelligence report.

"We extend our deepest sympathies to the Raine family during this difficult time," the company said. It also published a note on its website on Tuesday that said "recent heartbreaking cases of people using ChatGPT in the midst of acute crises weigh heavily on us". It added that "ChatGPT is trained to direct people to seek professional help," such as the 988 suicide and crisis hotline in the US or the Samaritans in the UK. The company acknowledged, however, that "there have been moments where our systems did not behave as intended in sensitive situations". Warning: This story contains distressing details.

Chip giant Nvidia is set to release its latest earnings report – and the results could move the entire US stock market. Over the past two years, the chipmaker has risen to become the world's most valuable company, with a market capitalisation of more than 4 trillion. When Nvidia announces its earnings on Wednesday, investors will get to see how the tech giant has been faring amid the tumult of President Donald Trump's trade salvoes and concerns about whether artificial intelligence has been overhyped. Nvidia specialises in making the graphics processing units (GPUs) that power AI, including the Blackwell B200, marketed as the world's most powerful chip. The California-based company's chips have become essential to the world's largest tech companies, including Microsoft, Meta, Amazon and Alphabet, since AI exploded into the mainstream with the release of OpenAI's generative AI chatbot, ChatGPT, in November 2022.

As deep generative models proliferate across the AI landscape, industrial practitioners still face critical yet unanswered questions about which deep generative models best suit complex manufacturing design tasks. This work addresses this question through a complete study of five representative models (Variational Autoencoder, Generative Adversarial Network, multimodal Variational Autoencoder, Denoising Diffusion Probabilistic Model, and Multinomial Diffusion Model) on industrial tire architecture generation. Our evaluation spans three key industrial scenarios: (i) unconditional generation of complete multi-component designs, (ii) component-conditioned generation (reconstructing architectures from partial observations), and (iii) dimension-constrained generation (creating designs that satisfy specific dimensional requirements). To enable discrete diffusion models to handle conditional scenarios, we introduce categorical inpainting, a mask-aware reverse diffusion process that preserves known labels without requiring additional training. Our evaluation employs geometry-aware metrics specifically calibrated for industrial requirements, quantifying spatial coherence, component interaction, structural connectivity, and perceptual fidelity. Our findings reveal that diffusion models achieve the strongest overall performance; a masking-trained VAE nonetheless outperforms the multimodal variant MMVAE\textsuperscript{+} on nearly all component-conditioned metrics, and within the diffusion family MDM leads in-distribution whereas DDPM generalises better to out-of-distribution dimensional constraints.