人与机器之间的最佳界面是什么?
在零压力播客中, Helen Sharman is joined by Commodore Michael Brasseur and Sameer Alam教授 to explore the concept of ‘human-machine teaming’, 我们如何处理人与人之间的关系, the machine, 以及它们之间的相互作用和相互依赖.
The concept of machine learning is becoming increasingly visible and valuable in society, but understanding how to optimise the human and machine interface and how they work together is key to the success of this technology.
Michael Brasseur准将和Sameer Alam教授如是说, two AI experts working in unmanned maritime applications and air traffic control respectively, 在最新一期的帝国理工学院和 亚洲体育博彩平台的零压力播客.
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海事机器人的作用
Zero Pressure’s host, 英国宇航员海伦·沙曼, hears first from Michael Brasseur about his role as leader of the US Navy’s new Task Force 59. The team aims to rapidly integrate unmanned systems and artificial intelligence into operations in the navy’s 5th Fleet area of operations in the Arabian Gulf. The technology is eventually expected to be used in the likes of counterpiracy and maritime interdiction actions.
Commodore Brasseur tells Helen that autonomous capabilities can have great value in reducing the load on human operators, especially in processing a huge amount of data or in multi-tasking operations under pressure. However, 如果人类要相信机器算法, 我们需要确定机器会做我们期望它做的事.
“For example, we wouldn’t want one of our un-crewed missions with a machine violate someone else’s territorial waters. We need to understand how machines behave when potential adversaries try to disrupt our communications or GPS,” he says.
“这真的是一个难题. The only way to build trust is by operating in a communications-contested environment, 通过走几步, doing several exercises where the machine is being used what it’s been designed to do.”
Ultimately, Commodore Brasseur believes that one of the great values of maritime robotics is to be able to utilise the fact that they are less expensive than manned solutions, 比如更多的驱逐舰或巡洋舰, 因此有更多的可用资源.
“There’s value in getting more sensors out on the water to enhance our maritime awareness,” he says.
他在上图中描绘了一个人控制12或13个海上机器人的画面, 在水上和水下, 提供全面的数据，建立一种生活模式, 所以机器可以学会判断什么时候出现异常, 高亮显示，以便操作者仔细观察. However, 他补充说，他对未来的理想设想是人类始终参与其中, 特别是对于高风险的操作.
空中交通管制的机器学习
Air traffic management is further along in its embrace of machine learning research than its maritime counterpart, partly due to the less harsh physical environment and an embedded culture of automation.
Sameer Alam教授, Deputy Director of Air Traffic Management Research Institute and Co-Director of SAAB-NTU Joint Research Lab in Singapore, has 20 years’ experience of researching machine learning for air traffic management, and leads a team of 20 research scientists and seven PhD students at the Singapore lab.
For Professor Alam, 主要关注的领域包括解决诸如机器如何, or AI agent, 感知环境, takes action, 同时还要评估这一行动的后续影响. And, how to get to the stage where the machine has collected enough data on human actions to recognise established patterns of behaviours, 在此基础上，它可以开始做出自己的决策.
“This makes the algorithms very powerful because now they are taking a collective human knowledge that has evolved over time,” he says.
其他研究领域包括可解释人工智能, 当机器不仅建议你做一个决定, 同时也会告诉你做决定背后的逻辑. 并以一种可理解的、因而值得信赖的方式这样做.
用户界面的重要性
Interestingly, both Commodore Brasseur and Professor Alam highlight the importance of the interface technology in building trust between the human user and the AI agents.
在空中交通管制场景中, 阿拉姆提到了增强现实和虚拟现实设备, 包括微软全息眼镜的试用, 控制器可以在家工作的地方, 不需要来控制中心.
In the maritime case, Brasseur says that a piloting trial of an unmanned surface vessel was being controlled by an X-Box controller. “It’s a well-known interface among the operators, and makes the transition easy to operate,” he says.
Another important benefit in improving interface technology and utilizing autonomy is reducing the cognitive load on a decision maker or operator. “我们正处于一个传感器过载的时期，人类的大脑可能会过载, it gets fatigued, whereas the machine learning can process tonnes and tonnes of information and make sense of it,布拉瑟准将补充道.