This paper discusses the limitations of machine learning (ML), particularly deep artificial neural networks (ANNs), which are effective at approximating complex functions but often lack transparency and explanatory power. It highlights the `problem of induction' : the philosophical issue that past observations may not necessarily predict future events, a challenge that ML models face when encountering new, unseen data. The paper argues for the importance of not just making predictions but also providing good explanations, a feature that current models often fail to deliver. It suggests that for AI to progress, we must seek models that offer insights and explanations, not just predictions.

翻译：本文探讨了机器学习（ML），尤其是深度人工神经网络（ANN）的局限性——这些模型虽擅长逼近复杂函数，却往往缺乏透明度与解释能力。研究聚焦于“归纳问题”这一哲学难题：过去观察未必能预测未来事件，而机器学习模型在应对未见数据时正面临此类挑战。本文论证指出，人工智能不仅要实现预测功能，更应提供优质解释，当前模型在此方面普遍存在缺陷。研究建议，为实现人工智能的发展，必须构建能够提供洞见与解释（而不仅仅是预测）的模型。