Researchers at NYU's Center for Data Science have developed a new framework, I2M2, that improves multi-modal AI performance by effectively modeling both inter- and intra-modality dependencies.

A novel data augmentation technique, SASSL, leverages Neural Style Transfer to improve self-supervised learning by preserving semantic information in images.

AI-generated code is transforming software development, offering both remarkable benefits and notable challenges.

Recent studies reveal innovative strategies in fine-tuning large language models (LLMs), enhancing their adaptability and performance across various domains.

Researchers are developing innovative methods to detect and reduce AI-generated inaccuracies, known as hallucinations, enhancing the reliability of large language models.

A recent study reveals that large language models (LLMs) can predict neuroscience study results more accurately than human experts, highlighting their potential to accelerate research.

A recent study reveals that large language models (LLMs) can predict neuroscience study outcomes more accurately than human experts, highlighting their potential to accelerate research.

Artificial intelligence is revolutionizing mental health care by offering personalized, accessible, and real-time support through innovative tools and platforms.

Recent advancements in frontier AI have led to significant breakthroughs, but these developments also bring challenges due to the unpredictable nature of AI behavior.