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.
Computer vision
A computer scientist pushes the boundaries of geometry
Justin Solomon applies modern geometric techniques to solve problems in computer vision, machine learning, statistics, and beyond.
Synthetic imagery sets new bar in AI training efficiency
MIT CSAIL researchers innovate with synthetic imagery to train AI, paving the way for more efficient and bias-reduced machine learning.
Foundational vision models and visual prompt engineering for autonomous driving applications | Amazon Web Services
Prompt engineering has become an essential skill for anyone working with large language models (LLMs) to generate high-quality and relevant texts. Although text prompt engineering has been widely discussed, visual…
This 3D printer can watch itself fabricate objects
Computer vision enables contact-free 3D printing, letting engineers print with high-performance materials they couldn’t use before.
To excel at engineering design, generative AI must learn to innovate, study finds
AI models that prioritize similarity falter when asked to design something completely new.
New tools are available to help reduce the energy that AI models devour
Amid the race to make AI bigger and better, Lincoln Laboratory is developing ways to reduce power, train efficiently, and make energy use transparent.
From physics to generative AI: An AI model for advanced pattern generation
Inspired by physics, a new generative model PFGM++ outperforms diffusion models in image generation.
Multi-AI collaboration helps reasoning and factual accuracy in large language models
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.
A pose-mapping technique could remotely evaluate patients with cerebral palsy
The machine-learning method works on most mobile devices and could be expanded to assess other motor disorders outside of the doctor’s office.