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
What if you and an AI could sketch together? Coevo is an online platform where humans and artificial agents co-design shapes to solve different tasks. The big idea: give people and AIs a shared set of design tools and a common “language,” so both can create, learn, and iterate in
ReinforcementLearning
Reinforcement learning (RL) wins in games and simulators—but deploying it on real products is a different story. Gabriel Dulac‑Arnold, Daniel Mankowitz, and Todd Hester outline nine must-solve challenges before RL can safely power real-world systems. * Safety & constraints: avoid harmful actions while learning. * Sample efficiency: learn from limited,
MachineLearning
Training AI usually needs lots of human-labeled examples. This work shows a different path: make models learn from plenty of unlabeled data by asking them to stay consistent under strong "noise." Instead of simple tweaks (like small crops or word drops), they use powerful data augmentation: RandAugment for
AI
Shopping sites host millions of product photos. Some slip through with offensive, non-compliant logos or messages, hurting customers and brands. This paper introduces an AI system that flags those images at massive scale. What’s new: a practical blend of image classification + object detection, tuned for messy, real-world retail catalogs.
AI that writes: past, present, future In a sweeping survey, Zhang, Guo, Wang, Liang, Hao, and Yu trace how text generation has evolved - from simple templates to neural networks that can draft emails, stories, and more. * From fluency to personality: systems now aim to reflect tone, style, and user
AI
Can AI essay graders be fooled? New research shows many automatic essay scoring (AES) systems reward “word soups” over real writing. Using interpretability tools, the authors found that a few keywords drive most of the score, while flow, grammar, and coherence matter far less. The wild part: because these models
Your face isn’t a password New research shows that a popular way to “lock” face-recognition data—called fuzzy commitments—doesn’t keep it truly safe when used with modern deep‑learning systems. Why? The face templates these systems produce don’t have enough randomness (entropy). That lets attackers reverse
LiDAR lets cars "see" the world, but low-cost sensors often produce sparse, blurry 3D points. This study introduces FLASH, a method that turns low-res scans into high-detail 3D—fast enough for real-time use. How it works * Two domains, one model: FLASH looks at data in space and in
TL;DR LoGo lets large language models pick and blend the right LoRA adapters for each input—no extra training, labels, or task setup. * Training-free: uses signals from a single forward pass to select and weight adapters. * Instance-level: decisions happen on-the-fly for every query. * Practical: keeps inference throughput while handling
AI
Lowering CT radiation keeps patients safer—but it can make images noisy and blurry. LMM-IQA is an AI that judges low‑dose CT quality and explains what went wrong. * Gives a quality score plus short, plain‑English notes on noise, blur, and contrast loss. * Works without task‑specific training and
Lightning Grasp: faster robot gripping Finding a good grip on everyday, irregular tools is still hard for robot hands. Lightning Grasp changes that: it generates diverse, realistic grasps in real time—without training data, delicate parameter tuning, or lucky starting guesses. The trick is a simple Contact Field: a lightweight