Task Specific Training

Task-specific training, traditionally requiring extensive labeled data for each task, is being challenged by approaches aiming for greater data efficiency and generalization. Current research focuses on leveraging pre-trained foundation models and incorporating diverse input modalities (e.g., visual, linguistic) to enable in-context learning and zero-shot transfer across tasks, often employing transformer architectures and neuro-symbolic methods. This shift promises to significantly reduce the cost and time associated with training AI systems for new tasks, impacting fields like robotics, natural language processing, and computer vision by enabling more adaptable and versatile AI agents.