Human-Machine-Teaming: Teaching Machine, Learning Machine

Excellent grinding requires the adjustment of multiple interacting parameters. Many of these are not numerically controlled. Their fine-tuning may be specific to a machine and sensitive to a context. Hence, parameter adjustment is not easily transferable between machines. Rather than by algorithmic this fine-tuning is dependent on tacit knowledge of experienced human operators.

Therefore, we aim at a human-machine teaming that combines human knowledge and machine capabilities. Doing so three core questions ware addressed:

• Learning machine: The intelligent assistant learns from the human and hence develops continuously.
  
  
• Teaching machine: The intelligent assistant will not replace the human but rather support human learning.
  
  
• Multi-collaboration: Human-machine teaming does not refer to individuals but rather to social systems of many people working together with many machines. Such, distributed knowledge is addressed, and systematic learning is supported.

Project Dates

Publications

Renggli, P., Waefler, T.  (2024). Why Do or Don’t You Provide Your Knowledge to an AI?. In: Tareq Ahram, Jay Kalra and Waldemar Karwowski (eds) Artificial Intelligence and Social Computing. AHFE (2024) International Conference. AHFE Open Access, vol 122. AHFE International, USA.

Waefler T. (2021). Progressive Intensity of Human-Technology Teaming. Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021, August 27–29, 2021, France, pp. 28-36.