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Who’s Liable When GenAI Produces Illegal CSAM? A German Law Perspective

New research examines how German criminal law could apply when generative AI is used to create child sexual abuse material (CSAM). The takeaway: legal risk isn’t limited to the user—it can reach model developers, researchers, and company representatives. The authors combine legal analysis with scenarios to show that

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Women Worry, Men Adopt: How Gendered Perceptions Shape the Use of Generative AI

Generative AI is spreading fast—but not equally. UK survey data (2023–2024) shows women use GenAI far less than men, largely because they view its societal risks differently. * A composite “risk concern” index (mental health, privacy, climate, jobs) explains 9–18% of who adopts GenAI. * For women, this index

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When AI Makers Could Be Criminally Liable in Germany for CSAM Outputs

Can creators of generative AI face criminal charges if users make child sexual abuse material (CSAM) with their models? A new multidisciplinary study of German law says: sometimes, yes. The authors examine realistic scenarios and find that liability may extend beyond the user to independent developers, researchers, and company representatives—

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New tool lets creators check if AI trained on their work

TL;DR An open-source platform helps creators check if their content was used to train large language models, without heavy compute or opaque workflows. What it does * Verifies if a specific work appears in LLM training datasets. * Improves similarity matching and dataset validation. * Cuts compute by 10-30% via efficient API

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From Pixels to Posts: Retrieval-Augmented Fashion Captioning and Hashtag Generation

Meet a new way to write captions and hashtags for fashion photos—grounded in what is actually in the picture. * Detects multiple garments in an image with a YOLO-based model. * Extracts dominant colors and infers fabric and gender by retrieving similar products with CLIP and FAISS. * Packages these facts as

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Fast, adaptable detection of AI-generated images

Can we quickly tell if an image is AI-made — and even which generator produced it — without retraining huge models every month? This study says yes. The authors propose a two-stage system: * Learn a visual "fingerprint" space. A vision model trained with supervised contrastive learning separates real vs. synthetic

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Generative AI in Bioinformatics: What a New Systematic Review Finds

GenAI is reshaping genomics, proteomics, transcriptomics, structural biology, and drug discovery. This systematic review asks six big questions to map where GenAI helps most—and what’s next. * Where it works: Sequence analysis, molecular design, and integrative modeling often beat traditional methods via pattern discovery and realistic output generation. * Best