Aleksandra Przegalinska

TL;DR: Understanding the user’s side may be crucial for designing better chatbots in the future and, thus, can contribute to advancing the field of human–computer interaction.

TL;DR: A novel method of analyzing the content of messages produced in human-chatbot interactions is proposed, using the Condor Tribefinder system the authors developed for text mining that is based on a machine learning classification engine.

TL;DR: In this paper, the authors explored synergies between human workers and AI in managerial tasks, and they hypothesized that human-AI collaboration will increase productivity, and the study results generally confirmed their hypothesis.

TL;DR: It is argued that the current practice in cognitive (neuro)science has undergone, in effect, a silent mechanistic revolution, and has turned from initial binary oppositions and abstract proposals toward the integration of wide perspectives with the rest of the cognitive (Neuro)sciences.

TL;DR: It is found that the usefulness in measuring EEG signal of consumer-grade devices such as Muse is extremely limited in non-laboratory conditions.