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flowx.ai-brings-mission-critical-industry-ai-agents-to-gemini-enterprise

FlowX.AI Brings Mission-Critical Industry AI Agents to Gemini Enterprise

FlowX.AI is among the first partners bringing specialized industry agents to Gemini Enterprise, with enterprise-grade AI built for complex, high-stakes work where making a mistake is not an option. FlowX.AI today announced that specialized FlowX.AI agents are available through Google Cloud Marketplace and as part of Gemini Enterprise, enabling Gemini Enterprise customers to bring AI into

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is-agentic-ai-just-automation?-|-towards-data-science

Is Agentic AI Just Automation? | Towards Data Science

Why most of the agents are just flowcharts in disguise, and what to build instead. I wrote most of this at an airport several months ago and then left it in drafts. The noise has only gotten louder since, so here it is. A few months ago, on our way back from a client workshop

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new-ai-tools-explore-the-climate-change-extreme-weather-link

New AI tools explore the climate change-extreme weather link

Article Category Article available in the following languages: XAIDA is designing and applying AI-based methodologies to reveal how climate change impacts extreme weather events and to foresee their occurrence in the future. Extreme weather events have become more frequent and intense in recent years, raising significant concerns over environmental impacts as well as human safety.

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streamline-uses-ai-to-tackle-one-of-nuclear-physics’-toughest-problems

STREAMLINE uses AI to tackle one of nuclear physics’ toughest problems

Newswise — A new project called STREAMLINE is using artificial intelligence and supercomputers to solve one of nuclear physics’ hardest challenges — unlocking insights into atomic nuclei, neutron stars and fundamental forces. At the heart of every atom lies a tightly bound cluster of protons and neutrons — a system governed by some of the most complex

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mistral-and-humain-announce-strategic-collaboration-to-advance-sovereign-ai-in-saudi-arabia-and-regionally

Mistral and HUMAIN Announce Strategic Collaboration to Advance Sovereign AI in Saudi Arabia and Regionally

Mistral and HUMAIN announced a strategic collaboration spanning AI infrastructure, advanced model development, and the deployment of AI solutions in Saudi Arabia and across the region. Together, the companies will pursue the development and localization of advanced AI models, with initial areas of focus including cybersecurity and voice. The companies also plan to develop frontier models

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ai-dishwasher-market

AI Dishwasher Market

Report Overview The Global AI Dishwasher Market generated USD 4.47 billion in 2024 and is projected to reach approximately USD 29.09 billion by 2034, expanding at a CAGR of 20.6% during 2025–2034. This represents nearly 6.5 times growth from the 2024 level. North America dominated the global market in 2025, generating approximately USD 1.58 billion

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sogang-hosts-ai-hackathon-for-high-schoolers-–-qs-gen

Sogang Hosts AI Hackathon for High Schoolers – QS GEN

On July 24 and 25, Sogang University hosted the 2026 AI Big Data Festival, bringing together 22 students and 3 teachers from Bokja Girls' High School alongside 6 members of Professor Jon-Lark Kim's research team from the Department of Mathematics. The event marked the seventh consecutive year of educational exchange between the two institutions —

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developing-and-evaluating-automated-deep-learning-and-human-in-the-loop-vision–language-systems-for-microplastic-characterization-–-scientific-reports

Developing and evaluating automated deep learning and human-in-the-loop vision–language systems for microplastic characterization – Scientific Reports

Introduction Microplastics (MPs), defined as plastic particles smaller than 5 mm in diameter, have become pervasive contaminants across marine, freshwater, terrestrial, and atmospheric environments1,2,3. Their persistence and capacity for ingestion by diverse organisms raise concerns regarding bioaccumulation, ecotoxicity, and ecosystem disruption4,5'6. Consequently, the accurate and efficient characterization of MPs—including morphological attributes such as shape, color

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clinical-validation-pipeline-of-a-deep-learning-model-for-segmenting-and-quantifying-intracranial-and-ventricular-volumes-on-computed-tomography-–-scientific-reports

Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography – Scientific Reports

Introduction The increasing availability of noninvasive imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) has led to higher demand for these services. Their widespread use for routine check-ups and early detection strategies has significantly increased the need for radiologists to interpret these exams. This demand is further exacerbated by the shortage

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