RoboLabs: Building Surfaces of Intelligibility

Abstract

Whether it is humanoid robots or chatbots: their thinking is on the surface and their promises are on the deep end. Superficial expression and activity data are used to make internal emotions, motives and attitudes available for development—mostly based on technologies that are not introspective, i.e. that do not know any impressions or experiential qualities. This paper translates this ambivalence into an analytics of the interconnectivity of intelligence types. Humans, but also machines, become visible here only as designs of the coupling of various intelligences—as carriers that have to be arranged in such a way that different types of intelligence can offer each other their need for complementation. An ethnographic case study draws on these theoretical considerations by comparing two situations of a) statistically and algorithmically modeling future users and b) repairing robot motions in a playful way. In both scenarios, algorithmic and hermeneutical intelligences complement each other by constitutively different modes of interoperability.