Image recognition accuracy: An unseen challenge confounding today’s AI
“Minimum viewing time” benchmark gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification.
“Minimum viewing time” benchmark gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification.
Justin Solomon applies modern geometric techniques to solve problems in computer vision, machine learning, statistics, and beyond.
MIT CSAIL researchers innovate with synthetic imagery to train AI, paving the way for more efficient and bias-reduced machine learning.
Computer vision enables contact-free 3D printing, letting engineers print with high-performance materials they couldn’t use before.
AI models that prioritize similarity falter when asked to design something completely new.
Amid the race to make AI bigger and better, Lincoln Laboratory is developing ways to reduce power, train efficiently, and make energy use transparent.
Inspired by physics, a new generative model PFGM++ outperforms diffusion models in image generation.
Researchers use multiple AI models to collaborate, debate, and improve their reasoning abilities to advance the performance of LLMs while increasing accountability and factual accuracy.
The machine-learning method works on most mobile devices and could be expanded to assess other motor disorders outside of the doctor’s office.
Researchers use synthetic data to improve a model’s ability to grasp conceptual information, which could enhance automatic captioning and question-answering systems.