Book

Description

Aamir Ayub, in his book The Dynamics of Communication: Understanding the Role of Artificial Intelligence in Communication Studies, has challenged the conventional anthropocentric notions of media and communication studies in order to develop a new framework for understanding communication in the current digital age. Conventional approaches to communication identified by Shannon and Weaver’s linear model, Berlo’s SMCR model, and Schramm’s interactive model tend to see media only as a channel for passing messages from one person to another. The birth of Generative AI, Large Language Models (LLMs), and agentive machines has radically changed this notion. In present-day conditions, machines have stopped being passive agents but have become active interlocutors in conversation by generating new formulas of message delivery depending on the context of the communication. Ayub has developed Dynamic-Agentic Communication Theory (DACT) to overcome the limitations of 20th-century theories of communication. The theory, based on the principles of cybernetics, Human-Machine Communication (HMC), media ecology, and social constructivism, presents communication as a multi-level feedback loop. The book comprises seven detailed sections in which the theory of AI is analyzed and various ways of its application in practice are studied: A Shift in Paradigms: There has been a transition from using a keyword-based search to the use of AI-powered language processing models. The issue of how people converse and comprehend language comes to our attention, as well as what needs to be done for these models to function correctly. Another question to come up is whether these models understand their own words. Old Theories Reanalyzed: Ancient theories of language and communication fail when it comes to implementation in artificial intelligence systems. We need to understand the reason for this failure. D-ACT Concept: This is a model that will help us understand how machines process language and comprehend the surrounding reality. It includes studying how machines learn from context, represent concepts, and produce incorrect outputs.