Meet SAM 3D: a generative model that 3Dfies anything you capture. From a single photo, it rebuilds an object's geometry, texture, and layout in the scene. It is built for messy, natural images with clutter and occlusion, using context cues to stay visually grounded. How it works * Human
What if robots could pick up dexterous, multi-finger skills just by watching you do everyday chores? That’s the promise of AINA, a new framework that learns robot hand policies from in‑the‑wild human demonstrations captured with Aria Gen 2 smart glasses. Why it matters: collecting robot data is
AI that thinks while it draws Meet TwiG (“Thinking‑while‑Generating”), a framework that lets an image model think in words as it generates pixels. Instead of only planning before or fixing after, TwiG interleaves short textual reasoning during generation—guiding upcoming regions and reflecting on what’s already made.
AI
Spotting AI-made images—without constant retraining Generative models evolve fast, making deepfake detectors go stale. This study introduces a two-stage method that adapts quickly to new image generators. * Stage 1: A vision model learns subtle “fingerprints” via supervised contrastive learning, trained while holding out some generator families to force cross-model
Generative AI’s recent leaps aren’t due to obscure tricks—they come from clear, testable principles. This paper argues that neuroscience should probe which of these principles the brain may share. * World models: Brains may not build full simulators; they might use task-tuned shortcuts with limited predictive scope. * Thought
AI
What if the brain builds and tests ideas the way modern generative AI produces images and text? A new paper by Claudius Gros argues that clear, testable generative principles—not obscure tricks—drove AI's leap, and neuroscience can probe whether similar rules guide the brain. Five takeaways * World
GDPR compliance is more than checkboxes. A new roadmap from the Privatech project shows how automation and machine learning can help companies and law firms assess—and even generate—privacy compliance. * Shift the focus to data processors’ real workflows: drafting policies, mapping data uses, documenting decisions. * Break compliance into machine-ready
Deep learning drives image search, robots, and medical scans. Most systems lean on CPUs and GPUs. This review asks: what if we run convolutional neural networks (CNNs) on FPGAs—reconfigurable chips you can tailor to the model? * Why FPGAs: custom dataflows, low latency, and strong energy efficiency—great for cameras,
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Most apps show a fixed number of “top” items—say 10 movies or 20 products—assuming there are always enough good options. But that’s not always true: sometimes there are few relevant items, or some users are extra picky. The result? Filler recommendations. Dynamic-K flips the script. Instead of
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Teaching chatbots to stop contradicting themselves Ever had a bot say one thing, then the opposite a few turns later? This study introduces DECODE—a new task and dataset for spotting contradictions in everyday conversations, drawn from both human-human and human-bot chats. * New data beats existing natural language inference (NLI)
What if you could train any classifier and fix its bias afterward? This paper shows you often can. The mathematically best fair rule can be achieved by applying group-specific score thresholds to your model's predictions, with a tiny bit of randomization at the boundary if needed. That insight
Can someone copy a black-box robot brain just by watching how it acts? This study says yes. Researchers Vahid Behzadan and William Hsu show that deep reinforcement learning (DRL) agents—used in games, robotics, and trading—can be cloned via policy imitation. An attacker who can repeatedly query an agent