On the Future of Information: Reunification, Computability, Adaptation, Cybersecurity, Semantics

Abstract

The vulnerability of software and the Internet and the accumulation of unprocessed information in big data are serious problems in informatics. Both are human-related. The former was traced to flaws caused by human interventions in development. In the latter case, humans also intervene to connect the dots, find meaningful patterns, and make sense of the information. The proposed solutions are based on more human interventions, which tend to aggravate the problems rather than solving them. I propose the complete elimination of human interventions in both cases. This goal is conceptually easy to achieve. The approach, however, is radical and theoretical. It considers the causal set, a mathematical object, as the universal language underlying all information in nature and, hence, also all computation. This assumption is recognized as the fundamental principle of causality, that effects follow their causes. The new theory is based solely on the causal set, its metric, and its vast array of algebraic properties. The consequences are unexpected, fascinating, and totally new. Since translations between causal sets and programming languages are easy, I also propose to confine the use of programming languages to the human interface, and create an inner layer of mathematical code expressed as a causal set. Machines talk only to mathematically verified, bug-free, secure code. Included in this paper are experimental and computational verifications of the theory, proposed applications to Internet vulnerability, science and technology, machine learning, computer intelligence, and details for building a first prototype.