Based on the availability data, more and more cognitive tasks can be transferred to a machine, which learns from individual and crowd behavior to increase our understanding, enhance our problem-solving capacity, or help us to remember.
This points the way towards a new generation of human-machine systems that may finally turn the vision of human-machine symbiosis into a reality. Originally proposed in the 1960s Intelligence Amplification, in contrast to Artificial Intelligence, describes a future in which humans and machines are closely coupled and in which their individual capabilities but also limitations complement each other, thereby resulting in more efficient, natural, as well as enjoyable interactions.
Starting from the original vision of human-machine symbiosis, in this talk I like to address three areas in which this combination reveals great potential, i.e. gaze-based attention recognition, visual sentiment analysis, and multi-perspective memory aids. For these areas, I will give insights into recent advances and show how knowledge-based systems as well as machine learning may complement our cognitive problem solving capabilities.