Generalist Model

Generalist models aim to create single, unified artificial intelligence systems capable of handling diverse tasks without task-specific fine-tuning, unlike specialist models trained for individual applications. Current research focuses on developing and evaluating these models across various domains, employing architectures like transformers and diffusion models, and exploring training strategies such as multi-task learning and instruction tuning to improve generalization and efficiency. This research is significant because it addresses the limitations of specialist models, potentially leading to more adaptable and resource-efficient AI systems with broader applicability in fields ranging from healthcare and robotics to computer vision and natural language processing.