Robots will dominate the use of our language

Abstract

Robots are able to influence the usage of human language even after the interaction between the human and robot has ended. Humans influence each other in the usage of words and hence the robots they program indirectly affect the development of our society's vocabulary. Most human-robot interaction studies focus on one robot interacting with one human. Studying the dynamic development of language in a group of humans and robots is difficult and requires considerable resource. We therefore conducted a social simulation of a human-robot communication network based on a real-world human-human network, allowing us to study how the centrality of the robots' owners influences the propagation of words in the network and what influence the number of robots in the network has on achieving a Fixation State. Our results show that robots owned by highly connected people has less effect on the dynamics of language than robots owned by less connected people. Highly connected people interact with many others, and therefore are more strongly influenced by a greater number of people and their robots. We have found that 11% of the humans owning a robot is sufficient for the robots to dominate the development of the language resulting in 95% of the humans using or adopting their words.

Electric Sheep Team Description Paper Humanoid League Kid-Size 2019

“…The Human Interface Technology lab (HITlab NZ) has a number of people working on HRI research; specifically, social HRI (see for example [19,7,3]). Even when well aware that robots are machines without consciousness, humans still automatically interact with them as if they were social agents [11,13].…”

Electrophysiological markers of language adaptation in joint production. Evidence from human-robot interaction

“…In this work, we implemented a joint picture-naming task to monitor the prediction dynamics to the lexical and lexico-semantic properties of a robot's naming pattern using EEG. Robots have proven to be good task partners in joint production studies (Brandstetter & Bartneck, 2017;Marge et al, 2022;Wudarczyk et al, 2021). Typical coordinating signals, such as gaze cueing, facial expressions, hesitation sounds have been found in human-robot interaction as much as in human-human interaction (Kilner et al, 2003;Loth et al, 2015;Skantze, 2016).…”