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Title: The state space of artificial intelligence
Author(s): Lyre, Holger
Issue Date: 2020
Type: Article
Language: English
URN: urn:nbn:de:gbv:ma9:1-1981185920-703876
Subjects: Artificial intelligence
Deep learning
Semantic grounding
State space of AI
Abstract: The goal of the paper is to develop and propose a general model of the state space of AI. Given the breathtaking progress in AI research and technologies in recent years, such conceptual work is of substantial theoretical interest. The present AI hype is mainly driven by the triumph of deep learning neural networks. As the distinguishing feature of such networks is the ability to self-learn, self-learning is identified as one important dimension of the AI state space. Another dimension is recognized as generalization, the possibility to go over from specific to more general types of problems. A third dimension is semantic grounding. Our overall analysis connects to a number of known foundational issues in the philosophy of mind and cognition: the blockhead objection, the Turing test, the symbol grounding problem, the Chinese room argument, and use theories of meaning. It shall finally be argued that the dimension of grounding decomposes into three sub-dimensions. And the dimension of self-learning turns out as only one of a whole range of “self-x-capacities” (based on ideas of organic computing) that span the self-x-subspace of the full AI state space.
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Sponsor/Funder: Projekt DEAL 2020
Journal Title: Minds and machines
Publisher: Springer Science + Business Media B.V
Publisher Place: Dordrecht [u.a.]
Volume: 30
Issue: 3
Original Publication: 10.1007/s11023-020-09538-3
Page Start: 325
Page End: 347
Appears in Collections:Fakultät für Humanwissenschaften (ehemals: Fakultät für Geistes-, Sozial- und Erziehungswissenschaften) (OA)

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