Artificial intelligence (AI) appears to have altered the entire process of processing and disseminating the information to larger audiences. The present study is intended to investigate the similitude, variance, and inferences of AI-driven communication in relation to human communication. This study explores AI-driven vs. human communication through a literature review, emphasizing human communication's unique ability to foster connections, empathy, trust, and authenticity. AI offers speed but lacks depth. Ethical concerns include privacy and misinformation. It highlights the importance of an ethical approach when using AI in communication.
TopIntroduction
Artificial Intelligence (AI) represents a vast domain within computer science that revolves around the creation of intelligent machines, capable of undertaking tasks typically associated with human intellect. In essence, AI’s core aim lies in imparting computer systems or machines with the capacity to think, learn, and function in a manner akin to human cognition. From a philosophical standpoint, AI holds the potential to enhance the quality of human existence, reducing the necessity for arduous toil, while effectively managing the intricate web of interconnected individuals, businesses, states, and nations for the collective benefit of all.
Consequently, the primary objective of AI is to empower computers and machines to execute cognitive functions, encompassing problem-solving, decision-making, perceiving their environment, and comprehending human communication. Hence, AI-based modeling stands as the linchpin for crafting automated, intelligent, and astute systems, tailored to contemporary requirements. This development represents a pivotal technological milestone that is reshaping the future of virtually every industry, fostering enhancements in efficiency, speed, and precision across all processes.
Over the past couple of years, there has been a renewed surge of interest among academic scholars in the field of Artificial Intelligence (AI). The Dartmouth Research Project defined AI in 1959 as the challenge of enabling machines to exhibit behavior considered intelligent in the same manner as humans (McCarthy et al., 1959). This notion encompasses the capacity of systems to exhibit insightful actions across expanding domains (Neilson, 2008). This involves the accurate interpretation of external data and the utilization of this knowledge to accomplish specific objectives and tasks through adaptable configurations. Notably, AI stands apart from concepts like the Internet of Things (IoT) and Big Data, even though there are connections. (Kaplan, 1996).
The Internet of Things allows the integration of external data to serve as input for AI processes, while Big Data encompasses information collected through various means (Kumar & Thakur, 2012). Furthermore, intelligent systems can authentically emulate human behaviors, encompassing cognitive, emotional, and social intelligence (Misselhorn, 2018). It is equipping machines to gather and process information from their environment to make decisions, resolve issues, and undertake other activities where human brain could not penetrate to do such things (Von Krogh, 2018).
Emerging from disciplines such as philosophy, mathematics, computation, psychology, and neuroscience, AI aims to imbue machines with human-like thought processes, surpassing human capabilities in certain aspects (Brynjolfsson & Mcafee, 2017). Its purpose is to enable machines to observe, process, and learn from their surroundings, enabling them to make decisions, tackle challenges, and engage in tasks that would be beyond the reach of the human mind (Gretzel, Sigala, Xiang, & Koo, 2015).
Certainly, the interplay between artificial technologies and human intelligence relies on algorithms designed to assist managers in making optimal decisions, leading to a cultural shift where numerous data points, connections, and interactions become integral to standard organizational management (Schneider & Leyer, 2019). These algorithmic models streamline managerial tasks by presenting meticulously categorized and organized datasets. In fact, prior studies have demonstrated that in many scenarios, these models outperform human decision-making (Kahneman, Rosenfield, Gandhi, & Blaser, 2016).
Sousa and Rocha (2019) put forth a framework delineating the development of essential skills – namely innovation, leadership, and management – for managers steering disruptive businesses. This strategic approach is propelled by the realization that AI’s applications extend to business operations, thus influencing intelligence-driven processes.