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
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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
Self-driving cars may face no-win crash scenarios. To build fairer AI, researchers often ask the public which option they prefer, then aggregate the answers. But your moral preferences are deeply personal—and sharing them can risk your privacy. This study introduces a first privacy-preserving, crowd-guided way to train AI for
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Nature builds brains from a single cell. This study shows how an artificial network can do something similar - growing itself from "noise". Inspired by the early visual system (retina to LGN), the authors propose a simple developmental algorithm that starts with one "cell" and self-organizes
In plain English ARCHANGEL is a way for national archives to prove that a video hasn’t been secretly edited—today or decades from now. * AI creates a temporal content hash: a compact fingerprint from the audio and visuals across minutes or hours. * That fingerprint changes if content is tampered
Freeway on-ramps use signals to meter cars, but most systems rely on a few roadside sensors. What if the signal could watch the road and learn in real time? Researchers propose a deep reinforcement learning (DRL) system that reads live traffic camera frames and decides when to let cars onto