论文标题
沉重化的辉煌和苦难
The Splendors and Miseries of Heavisidisation
论文作者
论文摘要
机器学习（ML）适用于科学问题，即，仅当可以将此答案带入特殊形式$ {\ cal g}时：x \ longrightArrow z $带有$ {\ cal g}（\ cal g}（\ vec x）$，以迭代的Heaviside函数的组合表示。目前，如果存在和何时存在这种障碍，以及如果不存在这些障碍，那么将已知公式转换为这种形式的方法是什么。这引起了这种术语重新制定的普通科学的计划 - 这听起来像是对建设性数学方法的强烈增强，只是这次它涉及所有自然科学。我们在这方面描述了第一步。
Machine Learning (ML) is applicable to scientific problems, i.e. to those which have a well defined answer, only if this answer can be brought to a peculiar form ${\cal G}: X\longrightarrow Z$ with ${\cal G}(\vec x)$ expressed as a combination of iterated Heaviside functions. At present it is far from obvious, if and when such representations exist, what are the obstacles and, if they are absent, what are the ways to convert the known formulas into this form. This gives rise to a program of reformulation of ordinary science in such terms -- which sounds like a strong enhancement of the constructive mathematics approach, only this time it concerns all natural sciences. We describe the first steps on this long way.
