We re-evaluate universal computation based on the synthesis of Turing machines. This leads to a view of programs as singularities of analytic varieties or, equivalently, as phases of the Bayesian posterior of a synthesis problem. This new point of view reveals unexplored directions of research in program synthesis, of which neural networks are a subset, for example in relation to phase transitions, complexity and generalisation. We also lay the empirical foundations for these new directions by reporting on our implementation in code of some simple experiments.
翻译:我们根据图灵机器的合成,重新评价通用计算方法,从而将程序视为分析品种的独一性,或相当于合成问题巴耶西亚后方的阶段。这一新的观点揭示出方案合成研究的未探索方向,其中神经网络是其子项,例如阶段过渡、复杂性和概括性。我们通过以一些简单实验代码报告我们的执行情况,也为这些新方向奠定了经验基础。