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.
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MIT CSAIL researchers innovate with synthetic imagery to train AI, paving the way for more efficient and bias-reduced machine learning.
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…
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.
The system could improve image quality in video streaming or help autonomous vehicles identify road hazards in real-time.