Machine learning, inductive reasoning, and reliability of generalisations
The present paper shows how statistical learning theory and machine learning models can be used to enhance understanding of AI-related epistemological issues regarding inductive reasoning and reliability of generalisations. Towards this aim, the paper proceeds as follows. First, it expounds Price’s dual image of representation in terms of the notions of e-representations and i-representations that constitute subject naturalism. For Price, this is not a strictly anti-representationalist position but rather a dualist one (e- and i-representations). Second, the paper links this debate with machine learning in terms of statistical learning theory becoming more viable epistemological tool when it abandons the perspective of object naturalism. The paper then argues that machine learning grounds a form of knowing that can be understood in terms of e- and i-representation learning. Third, this synthesis shows a way of analysing inductive reasoning in terms of reliability of generalisations stemming from a structure of e- and i-representations. In the age of Artificial Intelligence, connecting Price’s dual view of representation with Deep Learning provides an epistemological way forward and even perhaps an approach to how knowing is possible.
KeywordsRepresentation Object naturalism Subject naturalism Machine learning Statistical learning theory Deep learning
This research was supported by the Charles University's research centers program no. UNCE/HUM/037. The Human-Machine Nexus and Its Implications for the International Order and by the grant from VVZ C4SS 52-04 at Metropolitan University Prague. I would like to express my gratitude to both anonymous reviewers and dr. Vit Stritecky for the time they invested in making insightful suggestions greatly improving the paper.
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