AI
Wearables are reshaping healthcare, but most AI models track one physical condition at a time and overlook depression—despite frequent overlap. This study reframes multi-disease assessment as a multi-task learning problem, letting one model learn several conditions jointly and use their relationships to improve accuracy. The catch is “double heterogeneity”
AI
Words like "bank" can mean a riverbank or a financial institution. Getting the right meaning—called word sense disambiguation—is hard for AI, especially when we need rich, logic-friendly knowledge for reasoning. This paper introduces an annotation-free workflow that teams up symbolic language understanding with large language models:
Robotics
Robots are great at repeating tasks—but not at knowing when to trust their own choices. This study brings metacognition (self-monitoring) to robots by giving them a sense of confidence in each decision. * Inspired by neuroscience, the architecture treats confidence as a second-order judgment about actions. * In tests on autonomous
AI
Wearables and AI are reshaping healthcare, but most tools track only physical diseases. That misses depression, which often coexists with chronic conditions. This study introduces one model that assesses multiple chronic diseases and depression together, using data from everyday sensors. It treats each disease as its own task, so the
AI
Malnutrition screening is often slow and hard to scale. NutriScreener is a new AI tool that flags undernutrition from children’s images and estimates key body measurements to support early intervention. * Blends visual cues with a curated knowledge base matched to a child’s age and region to improve fairness
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
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
RecommenderSystems
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
AI
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)
AI
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
AI
What if AI could learn to translate without any bilingual textbooks? This research shows how pictures can act as a bridge between languages. When we look at the same photo, speakers of different languages describe the same objects and actions. The team uses images as “pivots” so a model can
AI
Look me in the eye, camera. Ever snapped a great photo except your eyes aren’t on the camera? This research introduces GazeCorrection, an AI that subtly redirects your gaze by re-synthesizing just the eye region—keeping your identity and expression intact. How it works: a self-supervised generative model treats