The universal Turing Machine (TM) is a model for Von Neumann computers — general-purpose computers. A human brain, linked with its biological body, can inside-skull-autonomously learn a universal TM so that he acts as a general-purpose computer and writes a computer program for any practical purposes. It is unknown whether a robot can accomplish the same. This theoretical work shows how the Developmental Network (DN), linked with its robot body, can accomplish this. Unlike a traditional TM, the TM learned by DN is a super TM — Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, and Abstractive (GENISAMA). A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). Its learning from a teacher TM is one transition observation at a time, immediate, and error-free until all its neurons have been initialized by early observed teacher transitions. From that point on, the DN is no longer error-free but is always optimal at every time instance in the sense of maximal likelihood, conditioned on its limited computational resources and the learning experience. This paper extends the Church–Turing thesis to a stronger version — a GENISAMA TM is capable of Autonomous Programming for General Purposes (APFGP) — and proves both the Church–Turing thesis and its stronger version.

中文翻译：

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通用自主编程：理论

通用图灵机 (TM) 是冯诺依曼计算机（通用计算机）的模型。与生物体相连的人脑可以在颅内自主学习通用 TM，因此他可以充当通用计算机并为任何实际目的编写计算机程序。目前尚不清楚机器人是否可以完成相同的任务。这项理论工作展示了与其机器人身体相连的发展网络 (DN) 如何实现这一目标。与传统的 TM 不同，DN 学习的 TM 是超级 TM——扎根、涌现、自然、增量、枯燥、专注、激励和抽象（GENISAMA）。DN 没有任何中央控制器（例如，主映射、卷积或错误反向传播）。它向老师 TM 学习是一次一次的过渡观察，立即，并且没有错误，直到它的所有神经元都被早期观察到的教师转换初始化。从那时起，DN 不再是无错误的，而是在最大似然意义上在每个时间实例中始终是最优的，这取决于其有限的计算资源和学习经验。这篇论文将 Church-Turing 论文扩展到了一个更强大的版本——GENISAMA TM 能够进行通用自主编程 (APFGP)——并证明了 Church-Turing 论文及其更强大的版本。