The rising potential of AI, intertwines our lives with the use of AI-technology. It will increasingly behave as a partner rather than a tool. Yet AI-technology is always embedded within a larger organisation, where humans decide its purpose and framework. AI-technology does not act in isolation, therefore it must allow itself to be included in a larger network: a human-machine team.
Contact us about Human-machine teaming
To ensure sustainable human-machine teaming, certain things are crucially important. Humans and AI-technologies must be able to mutually understand and anticipate one another’s context, needs, abilities and shortcomings. Currently, a proper level of mutual understanding and anticipation is lacking. TNO is advancing this mutual understanding by developing:
Tackling these research challenges will enable us to harness the full potential of AI as a partner in everyday human life.
Human-machine teaming is strongly represented in the domains of health and mobility, safety and security. They are characterized by unpredictable or adverse conditions. A human-machine team must therefore be capable of reorganising itself efficiently. This should maximise the potential of the team. AI-technology can operate effectively and robustly, when the technology is socially capable, flexible, and aware of the larger context within which it operates.
The success of human-machine teaming depends on humans and AI-technology having a shared understanding. This relates to the team context, team member roles, responsibilities and resource needs. AI-technology should be capable of proactively inviting the human in the loop by sharing information about relevant developments. Furthermore, AI-technology should be capable of developing sustainable, dependable, and trustworthy relations with its team members. For example, by explaining its way of reasoning when suggesting a solution.
Achieving a shared understanding of roles and competencies is a gradual process. Relationships between team members develop over time as a result of experiences gained during training, exercises and operations. These are all occasions offering AI systems continuous input to learn from the dynamics with their team members and surroundings.