True human-machine interaction implies machines to be endowed with expertise to foster self-motivation within the process of interaction itself; objectives are developed from scratch while interacting partners exercise unpredictable mutual influence. Rewarding human-machine interaction is imagined to be proportional to the appreciation of the recognition of relationships between human behaviour (e.g. spontaneous body language) and its impact on emerging behaviour in an otherwise self-organising micro-universe. Hybrid spaces may exist of biological and synthetic components interfaced in intimate interaction. We offer evidence that methods of machine learning and artificial evolution may contribute to the creation of highly complex audio-visual interactive systems. Such systems are experimental and speculative; they show that qualitative aesthetic experiences may emerge from unreliable degrees of understanding between cause and effect.
- Peter Beyls, Belgium peterbeyls.net