Hypercomputation: philosophical issues

TL;DR: This paper provides a critical review of the major assumptions which guide the development of modern computer science and engineering towards emulating biological systems and explores the potential and the virtues of biology to reshape contemporary science.

TL;DR: The book provides an extended argument for the semantic view of computation, which states that semantic properties are involved in the nature of computing systems.

TL;DR: A novel refined attractor-based complexity measurement for Boolean recurrent neural networks is provided that represents an assessment of their computational power in terms of the significance of their attractor dynamics.

TL;DR: This chapter discusses the application of the diagonal process of the universal computing machine, which automates the calculation of circle and circle-free numbers.

TL;DR: If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll.

TL;DR: We use compact methodologies to verify that DHTs and local-area networks can cooperate to fulfill this aim.

TL;DR: In a substantive new introduction to this MIT Press edition of Discovering Complexity, Bechtel and Richardson examine both philosophical and scientific developments in research on mechanistic models since 1993.