Technology

Large language models (LLMs) have made significant strides in the field of artificial intelligence, paving the way for machines that understand and generate human-like text. However, these models face the inherent challenge of their knowledge being fixed at the point of their training, limiting their adaptability and ability to incorporate new information post-training. This proves…

Artificial intelligence (AI) researchers from Stanford University and Notbad AI Inc are striving to improve language models' AI capabilities in interpreting and generating nuanced, human-like text. Their project, called Quiet Self-Taught Reasoner (Quiet-STaR), embeds reasoning capabilities directly into language models. Unlike previous methods, which focused on training models using specific datasets for particular tasks, Quiet-STaR…

Computer vision, the field dealing with how computers can gain understanding from digital images or videos, has seen remarkable growth in recent years. A significant challenge within this field is the precise interpretation of intricate image details, understanding both global and local visual cues. Despite advances with conventional models like Convolutional Neural Networks (CNNs) and…