Challenging the Computational Metaphor: Implications for How We Think

This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think

A more general treatment of the philosophy of physics and the existence of universes

Natural philosophy necessarily combines the process of scientific observation with an abstract (and usually symbolic) framework, which provides a logical structure to the development of a scientific theory. The metaphysical underpinning of science includes statements about the process of science itself, and the nature of both the philosophical and material objects involved in a scientific investigation. By developing a formalism for an abstract mathematical description of inherently non-mathematical, physical objects, an attempt is made to clarify the mechanisms and implications of the philosophical tool of Ansatz. Outcomes of the analysis include a possible explanation for the philosophical issue of the 'unreasonable effectiveness' of mathematics as raised by Wigner, and an investigation into formal definitions of the terms: principles, evidence, existence and universes that are consistent with the conventions used in physics. It is found that the formalism places restrictions on the mathematical properties of objects that represent the tools and terms mentioned above. This allows one to make testable predictions regarding physics itself (where the nature of the tools of investigation is now entirely abstract) just as scientific theories make predictions about the universe at hand. That is, the mathematical structure of objects defined within the new formalism has philosophical consequences (via logical arguments) that lead to profound insights into the nature of the universe, which may serve to guide the course of future investigations in science and philosophy, and precipitate inspiring new avenues of research

A Diagram Is Worth A Dozen Images

Diagrams are common tools for representing complex concepts, relationships and events, often when it would be difficult to portray the same information with natural images. Understanding natural images has been extensively studied in computer vision, while diagram understanding has received little attention. In this paper, we study the problem of diagram interpretation and reasoning, the challenging task of identifying the structure of a diagram and the semantics of its constituents and their relationships. We introduce Diagram Parse Graphs (DPG) as our representation to model the structure of diagrams. We define syntactic parsing of diagrams as learning to infer DPGs for diagrams and study semantic interpretation and reasoning of diagrams in the context of diagram question answering. We devise an LSTM-based method for syntactic parsing of diagrams and introduce a DPG-based attention model for diagram question answering. We compile a new dataset of diagrams with exhaustive annotations of constituents and relationships for over 5,000 diagrams and 15,000 questions and answers. Our results show the significance of our models for syntactic parsing and question answering in diagrams using DPGs

Pragmatic Holism

The reductionist/holist debate seems an impoverished one, with many participants appearing to adopt a position first and constructing rationalisations second. Here I propose an intermediate position of pragmatic holism, that irrespective of whether all natural systems are theoretically reducible, for many systems it is completely impractical to attempt such a reduction, also that regardless if whether irreducible `wholes' exist, it is vain to try and prove this in absolute terms. This position thus illuminates the debate along new pragmatic lines, and refocusses attention on the underlying heuristics of learning about the natural world

Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code

The current trend in next-generation exascale systems goes towards integrating a wide range of specialized (co-)processors into traditional supercomputers. However, the integration of different specialized devices increases the degree of heterogeneity and the complexity in programming such type of systems. Due to the efficiency of heterogeneous systems in terms of Watt and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. In order to bridge the gap between heterogeneous systems and programmers, in this paper we propose a machine learning-based approach to learn heuristics for defining transformation strategies of a program transformation system. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of the approach for easing the programmability of heterogeneous systems.Comment: Part of the Program Transformation for Programmability in Heterogeneous Architectures (PROHA) workshop, Barcelona, Spain, 12th March 2016, 9 pages, LaTe