论文标题
知名事物的机器学习
Machine learning of the well known things
论文作者
论文摘要
当前形式的机器学习（ML）意味着任何问题的答案都可以通过非常奇特的形式的函数很好地近似：专门调整了heavyside theta功能的迭代。自然要问我们已经知道的问题的答案是否可以自然地以这种形式表示。我们提供了基本的，仍然不可行的例子，这确实是可能的，并建议以ML一致的方式寻找对现有知识的系统重新制定。这些尝试的成功或失败会阐明科学和认识论的各种问题。
Machine learning (ML) in its current form implies that an answer to any problem can be well approximated by a function of a very peculiar form: a specially adjusted iteration of Heavyside theta-functions. It is natural to ask if the answers to the questions, which we already know, can be naturally represented in this form. We provide elementary, still non-evident examples that this is indeed possible, and suggest to look for a systematic reformulation of existing knowledge in a ML-consistent way. Success or a failure of these attempts can shed light on a variety of problems, both scientific and epistemological.
