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#artificialintelligence

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🤔 How can artificial intelligence become true "community intelligence"?
The challenge isn't just technological but organizational: we need an approach that puts people and collaborative processes at the center to transform data into shared value.
A paradigm shift that can revolutionize how we work together 👥

Learn more ➡️ relaia.org/it/blog/intelligenz

relaia.orgIntelligenza di ComunitàRelaia aiuta le comunità ad interagire con dati e algoritmi, per prendere decisioni migliori. L'intelligenza artificiale è solo una parte dello spettro.

In this week's newsletter, we talk about

• an AI misdiagnosing a NHS patient
• an unfortunate use of ChatGPT at Genentech
• the FDA's Elsa AI troubles
• STAT's own AI misadventures

Plus: A tale of my 2003 trip to the Mall of America:

statnews.com/2025/07/30/maybe-

STAT · Maybe AI isn’t ready for primetime yetIn this edition of AI Prognosis: Uses of AI at Genentech, the FDA, and in the UK NHS, as well as Trump's 'woke AI' order.

Synthetic Data Generation Using LLM for AI to Train Safely #artificialintelligence #business

Explore the transformative potential of synthetic data generation using large language models for AI training. Discover how to train your AI safely while addressing the demand for high-quality, diverse data. Learn more in our latest blog post: ift.tt/rZH6fWq

Source: ift.tt/rZH6fWq | Image: ift.tt/JuogsIf

Digital illustration of a human head profile formed by glowing blue dots and lines on a dark background, symbolizing artificial intelligence.
RS Web Solutions · Synthetic Data Generation Using LLM for Safe AI Training

New article published: Syntax Without Subject
What happens when AI writes rules but removes the speaker?
This study tracks how LLMs erase the subject from legal, medical, and policy texts.
We call this structural delegation.
🔗 zenodo.org/records/16571077
#MedicalNLP #LegalTech
#MedTech #AIethics #AIgovernance #healthcare #ArtificialIntelligence #NLP #aifutures #LawFedi #lawstodon #tech #finance #business #agustinvstartari #medical #linguistics #ai #LRM #ClinicalAI #politics

ZenodoSyntax Without Subject: Structural Delegation and the Disappearance of Political Agency in LLM-Governed ContextsAbstract This article examines the syntactic disappearance of the subject in LLM-governed documents. Structural delegation refers to the transfer of agency to impersonal grammatical forms that preclude subject reappearance. Subjects are not censored but syntactically eliminated through passive constructions, nominalizations, and imperative prompt formats with suppressed agents. Building on prior work on synthetic ethos and impersonal command grammars, the article shows that AI-generated institutional texts display consistent patterns of subject erasure. The study analyzes 172 documents produced by GPT‑4 class models (temperature 0.2–0.7, 2024–2025) across legal, healthcare, and administrative domains. Metrics include passive ratio (via dependency label parsing), nominalization density (via POS and suffix filters), and instruction-format frequency. The result is a form of executable authority grounded not in referential authorship but in compliance with a regla compilada (type-0 production). The study proposes a typology of structural delegation and a formal framework for detecting syntactic absence in automated governance. This work is also published with DOI reference in Figshare https://doi.org/10.6084/m9.figshare.29665697 and Pending SSRN ID to be assigned. ETA: Q3 2025. Resumen Este artículo examina la desaparición sintáctica del sujeto en documentos generados por modelos de lenguaje de gran escala (LLMs). Delegación estructural se define como la transferencia de agencia a formas gramaticales impersonales que impiden la reaparición del sujeto. No se censura a los agentes, sino que se los elimina mediante construcciones pasivas, nominalizaciones y formatos imperativos de instrucción con agente suprimido. Basado en trabajos previos sobre ethos sintético y gramáticas de mando impersonales, el artículo demuestra que los textos institucionales generados por IA presentan patrones consistentes de borramiento del sujeto. El estudio analiza 172 documentos producidos por modelos de clase GPT‑4 (temperatura 0.2–0.7, años 2024–2025) en los sectores legal, sanitario y administrativo. Las métricas incluyen proporción de pasivas (vía etiquetas de dependencia), densidad de nominalización (a través de filtros de sufijo y categoría gramatical), y frecuencia de formatos de instrucción. El resultado es una forma de autoridad ejecutable basada no en autoría referencial, sino en la adhesión a una regla compilada (producción tipo 0). El estudio propone una tipología de la delegación estructural y un marco formal para detectar la ausencia sintáctica en entornos de gobernanza automatizada.

The #Autistic Self-Advocacy Network (#ASAN) warns "We are very worried that people are using #ArtificialIntelligence #AI to translate text into plain language without realizing that it cannot do that work correctly. We tested multiple artificial intelligence models, and all of them made big mistakes that changed the meaning of the text. For this and other reasons, we call on other organizations not to use artificial intelligence for plain language translation. "

autisticadvocacy.org/2025/07/a