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.
Abstract (translated)
人工智能神经网络（ANNs）在许多任务中表现出色，包括分类或预测，例如语音处理和图像分类。这些新功能基于一种计算模型，该模型允许在最终实现其预期功能时自由选择所有必要的内部模型参数。在这里，我们回顾了机器学习（ML）算法中模型参数选择与新格言主义理论之间的联系，重点关注其理论的实用性和反表现主义 aspects。为了了解ANN模型参数选择的影响，我们建议使用研究 implications 好的新格言主义理论。值得注意的是，新格言主义的优化概念也是基于效用考虑的。这意味着应用这种方法恰当地揭示了在机器学习中的优化问题，以及在伦理后果理论中的优化问题，那里它在作为行动最大化的目标时发生。我们建议，这些联系源于ML系统中的相关性计算方式。这最终可能揭示出ML系统中的特定行动趋势。
URL
https://arxiv.org/abs/2405.04386
