Human-Machine Teaming:Designing synergistic learning of humans and machines

本提案探讨了人类和机器利用其不同能力共同学习以实现互利的系统设计。机器学习已经商品化，应用于医学图像读取和自动驾驶汽车等领域，但它通常与人类分开操作，取代了人类的技能并导致去技能化。利用放射学培训和环境学习领域的人机交互研究技术、协同设计和迭代原型，我们将设计和评估支持人类交互式框架问题、探索和学习的范例系统，同时利用和改进机器模型，从而形成设计人机团队的指导框架。

This proposal investigates the design of systems in which humans and machines use their different abilities to learn together for mutual benefit. Machine learning has been commoditised, applied in areas such as medical image reading and autonomous vehicles, however it typically operates separately from humans, supplanting human skills and leading to deskilling. Using human-computer interaction research techniques, co-design and iterative prototyping in the domains of radiology training and environmental learning, we will devise and evaluate exemplar systems that support humans to interactively frame problems, explore and learn, while utilising and improving machine models, leading to a guiding framework for designing human-machine teaming.