Phenomenology and the Cognitive Sciences: Phenomenology and Artificial Intelligence: Bridges and New Paths

• Type: Another Publication in an International Scientific Journal
• Date: 2024

Authors

Gouveia, Steven S. (Editor) FLUP; Morujão, Carlos (Editor) Other

Journal

Title: Phenomenology and the Cognitive Sciences

Vol. 23

ISSN: 1568-7759

Publisher: Springer Nature

Other information

Abstract (EN): The new developments in the field of Artificial Intelligence raise many different philosophical questions that can profoundly change our thinking about various mental concepts, such as what it means to be a conscious subject, how perception works, how humans interact with their environment via their bodies, and so on (cf. Andler, 2006; Froese & Ziemke, 2009). Oddly enough, these new developments – from machine learning tools to deep learning algorithms and artificial neural networks – have not been the primary focus of philosophical deliberation (with some exceptions, such as Buccella & Springle, 2022). As a philosophical approach that focuses on the study of the universal structures of subjective experiences and the way people perceive and interpret the world around them, phenomenology can provide valuable insights when applied to AI in general (cf. Mensch, 1991; Preston, 1993; Beavers, 2002). Relevant issues are related to topics such as how the Internet can be considered a new kind of cognitive ecology (cf. Smart, Heersmink & Clowes, 2017), the impact that virtual reality and the metaverse can have on the 4E’s approach to the mind (cf. Smart, 2022), how the research in Robotics can be improved by making use of the 4E Cognition framework (cf. Hoffmann & Pfeifer, 2018), or the philosophical debates raised by the existence of large language models such as ChatGPT by OpenAI (e.g. is there a relationship between linguistic behavior/performance and subjectivity?) (cf. Floridi, 2023). The Special Issue aims to consider how the combination of phenomenology and AI can provide new ways of understanding subjective experience in its broadest sense on the one hand, and how AI practice and development can be improved and understood via an inspired phenomenological approach broadly considered.

Language: English

Type (Professor's evaluation): Scientific