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.

人工神经网络在分类，预测等任务上表现出色，本研究回顾了在人工神经网络上运行的机器学习算法中模型参数选择与新实用主义理论之间的联系，探讨了优化在机器学习系统中的作用以及伦理学理论中的最佳行动的优化的内在关联。

实用主义智能：在 ANNs 中应用有用性原则

Pragmatist Intelligence: Where the Principle of Usefulness Can Take ANNs

Multi-source Domain Generalization (DG) measures a classifier's ability to
generalize to new distributions of data it was not trained on, given several
training domains. While several multi-source DG methods have been proposed,
they incur additional complexity during training by using domain labels. Recent
work has shown that a well-tuned Empirical Risk Minimization (ERM) training
procedure, that is simply minimizing the empirical risk on the source domains,
can outperform most existing DG methods. We identify several key candidate
techniques to further improve ERM performance, such as better utilization of
training data, model parameter selection, and weight-space regularization. We
call the resulting method ERM++, and show it significantly improves the
performance of DG on five multi-source datasets by over 5% compared to standard
ERM, and beats state-of-the-art despite being less computationally expensive.
Additionally, we demonstrate the efficacy of ERM++ on the WILDS-FMOW dataset, a
challenging DG benchmark. We hope that ERM++ becomes a strong baseline for
future DG research. Code is released at
this https URL

通过改进经验风险最小化模型训练方法，提出了一种新的 Multi-source Domain Generalization 方法 ERM++，它可以在不增加计算复杂度的情况下，在多个数据集上取得领先于现有方法超过 5% 的性能表现，成为未来 Domain Generalization 研究的强大基准。