At the intersection of what I call uncomputable art and computational epistemology, a form of experimental philosophy, we find an exciting and promising area of science related to causation with an alternative, possibly best possible, solution to the challenge of the inverse problem. That is the problem of finding the possible causes, mechanistic origins, first principles, and generative models of a piece of data from a physical phenomenon. Here we explain how generating and exploring software space following the framework of Algorithmic Information Dynamics, it is possible to find small models and learn to navigate a sci-fi-looking space that can advance the field of scientific discovery with complementary tools to offer an opportunity to advance science itself.
翻译:在我称之为不可辩驳的艺术和计算认知学的交汇点——一种实验哲学的形式——我们发现一个与因果关系有关的令人振奋和充满希望的科学领域,以另一种可能的最佳办法解决反向问题的挑战,这就是寻找可能的原因、机械起源、首要原则以及从物理现象中产生的数据的基因模型的问题。在这里,我们解释如何在算法信息动态的框架内产生和探索软件空间,可以找到小模型,并学习如何探索一个可推进科学发现领域的显微镜空间,以补充工具提供推进科学本身的机会。