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The Download: smarter AI in schools, and a robot “carnival” in Shanghai

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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How to encourage smarter AI use in the classroom Chatbots took schools by surprise. Suddenly, students carried an app in their…

Source: MIT Technology Review

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AI Companion Robots Are Closing the Human Connection in Modern Homes

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This article is brought to you by Ollobot.From about 2017, individuals began to truly connect with the initial wave of companion robots. These devices had personality, moved around, joked, and answered when you spoke to them. Most early companion robots, however, were still limited by…

Source: IEEE Spectrum

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I spent a day at a robot “carnival” in Shanghai. Here’s what I saw.

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Humanoid robots are having a moment in China. The popular machines are part of the country’s strategy to bring artificial intelligence into daily life. Embedding the technology into physical systems—an idea called embodied AI—was a key facet of China’s latest five-year plan, and companies here…

Source: MIT Technology Review

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Welcome to the spiderverse, a world measured through webs

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Counting the creatures in the world around us is critical for a raft of conservation efforts. It helps scientists gauge biodiversity, track migration, and spot invasive species. That census-taking, though, often requires humans to tabulate what they see, trap, or otherwise sense—a potentially laborious, costly…

Source: MIT Technology Review

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SAS: Segment Anything Small for Ultrasound — A Non-Generative Data Augmentation Technique for Robust Deep Learning in Ultrasound Imaging

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arXiv:2503.05916v2 Announce Type: replace-cross Abstract: Accurate segmentation of anatomical structures in ultrasound (US) images, particularly small ones, is challenging due to noise and variability in imaging conditions (e.g., probe position, patient anatomy, tissue characteristics and pathology). To address this, we introduce Segment Anything Small (SAS),…

Source: cs.AI updates on arXiv.org

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Multi-Context Fusion Transformer for Pedestrian Crossing Intention Prediction in Urban Environments

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arXiv:2511.20011v3 Announce Type: replace-cross Abstract: Pedestrian crossing intention prediction is essential for autonomous vehicles to improve pedestrian safety and reduce traffic accidents. However, accurate pedestrian intention prediction in urban environments remains challenging due to the multitude of factors affecting pedestrian behavior. In this paper, we…

Source: cs.AI updates on arXiv.org

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FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models

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arXiv:2605.03460v5 Announce Type: replace Abstract: Time series (TS) reasoning models (TSRMs) have shown promising capabilities in general domains, yet they consistently fail on financial domain, which exhibit unique characteristics. We propose a general 2 x 2 capability taxonomy for TSRMs by crossing 1) single-entity vs.…

Source: cs.AI updates on arXiv.org

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