Pragmatist Intelligence: Where the Principle of Usefulness Can Take ANNs

TL;DR

This paper explores the connection between neural network parameter selection and neopragmatist philosophy, highlighting how utility-based optimization in machine learning relates to ethical consequentialism and relevance calculation.

Contribution

It introduces a neopragmatist perspective to understand neural network optimization, linking it to philosophical theories of utility and relevance, offering a novel interpretative framework.

Findings

Neural network optimization aligns with neopragmatist utility principles.

Relevance calculation in ML systems influences their action tendencies.

Philosophical insights can inform understanding of ANN behavior.

Abstract

Artificial neural networks (ANNs) perform extraordinarily on numerous tasks including classification or prediction, e.g., speech processing and image classification. These new functions are based on a computational model that is enabled to select freely all necessary internal model parameters as long as it eventually delivers the functionality it is supposed to exhibit. Here, we review the connection between the model parameter selection in machine learning (ML) algorithms running on ANNs and the epistemological theory of neopragmatism focusing on the theory's utility and anti-representationalist aspects. To understand the consequences of the model parameter selection of an ANN, we suggest using neopragmatist theories whose implications are well studied. Incidentally, neopragmatism's notion of optimization is also based on utility considerations. This means that applying this approach elegantly reveals the inherent connections between optimization in ML, using a numerical method during the learning phase, and optimization in the ethical theory of consequentialism, where it occurs as a maxim of action. We suggest that these connections originate from the way relevance is calculated in ML systems. This could ultimately reveal a tendency for specific actions in ML systems.