Smart Network Field Theory: The Technophysics of Blockchain and Deep Learning

Abstract

The aim of this paper is to propose a theoretical construct, smart network field theory, for the characterization, monitoring, and control of smart network systems. Smart network systems are intelligent autonomously-operating networks, a new form of global computational infrastructure that includes blockchains, deep learning, and autonomous-strike UAVs. These kinds of largescale networks are a contemporary reality with thousands, millions, and billions of constituent elements, and entail a foundational and theoretically-robust model for their design and operation. Hence this work proposes smart network field theory, drawing from statistical physics, effective field theories, and model systems, for criticality detection and fleet-many item orchestration in smart network systems. Smart network field theory falls within the broader concern of technophysics (the application of physics to the study of technology), in which a key objective is deriving standardized methods for assessing system criticality and phase transition, and defining interim system structure between the levels of microscopic noise and macroscopic labels. The farther implications of this work include the possibility of recasting the P/NP computational complexity schema as one no longer based on traditional time (concurrency) and space constraints, due to the availability of smart network computational resources. Deep Learning ChainsThe diverse smart network technologies are emblematic of an emerging class of global computational infrastructure that increasingly acts autonomously based on intelligence built directly into the network operating software. Smart network technologies may be used in convergence. Some species of smart network technologies, such as blockchain and deep learning, are in a special class in that they are both smart network technologies themselves, and can serve as control technologies for other smart network systems. The functionality of blockchains and deep learning converges in the concept of deep learning chains. Deep learning chains are a smart network control technology with properties of both blockchain and deep learning: the secure automation, audit-log tracking, remunerability, and validated transaction execution of blockchains, and the object identification (IDtech a ), pattern recognition, and optimization technology of deep learning.Deep learning chains might be used to control other fleet-many internet-connected smart network technologies such as UAVs, autonomous driving fleets, medical nanorobots, and spacebased asteroid mining rigs. For example, deep learning chains could be important in autonomous driving fleets for tracking what the vehicle does (blockchain), and for identifying objects in its driving field (deep learning). Deep learning chains could likewise apply to the body, as a smart network control technology for medical nanorobots, identifying pathogens (deep learning) and tracking and expunging them (blockchain). Likewise in supply chain automated receiving, deep learning chains could provide the integra...

Perspective Chapter: Actor-Network Theory as an Organising Structure for Blockchain Adoption in Government

“…The successful alignment of the interests of a diverse set of actors with the interests of the focal actor by encouraging one another towards the pursuit of self-interest and collective objectives [20]. Translation is a process that creates 'a temporary social order, or movement from one order to another, through changes in the alignment of interests in a network' [29].…”

“…BT revolves around the smart networks theory [29] in which value is replicated in a network using a sophisticated protocol that validates, confirms and controls transactions through the network. These protocols offer a peer-to-peer transfer of value using algorithmic trust compared with the classical typology of trust between human agents [30][31][32].…”