AI refers to the simulation of human intelligence in machines that are programmed to think and act like humans. The term can also be applied to any machine that exhibits traits associated with a human mind, such as learning through recognition of patterns or features in data, reasoning or determining future actions, self-correction through adjusting and refining of methods to improve results, and problem-solving (including methods like search and mathematical optimization). Early commercial movers such as IBM’s Watson, simply referred to it as “software that learns.” AI systems can also be trained to perceive their environment by recognizing voices, texts, and images. Technologies like computer vision and natural language processing (NLP) fall under this category. In essence, AI is about creating algorithms that allow computers to perform tasks that would ordinarily require human intelligence. It spans a wide range of applications, from simple calculators that solve math problems to sophisticated systems that can understand language, recognize patterns, and make decisions (Spatharioti et al., 2023).
The question of how to define AI is key in this respect. Beginning with Turing’s question “Can machines think?” (1950), through McCarthy et al. (1955) idea that “the artificial intelligence problem is taken to be that of making a machine behave in ways that would be called intelligent if a human were so behaving,” to Floridi & Cowls (2019) argument that “the classic definition enables one to conceptualize AI as a growing resource of interactive, autonomous, and often self-learning agency that can deal with tasks that would otherwise require human intelligence and intervention to be performed successfully” definitions mostly focus on the link to human behavior and how AI might complement or diverge from it1. In contrast, Tegmark (2018) defines AI simply as “non-biological intelligence.” Building on Russell’s (2020) observation that “machines are intelligent to the extent that their actions can be expected to reach their objectives,” Cooke & Passingham (2022) consider it essential to clearly specify the AI program’s objectives, how it reaches conclusions, and the ethical and moral guidelines directing its operation, ensuring transparency and compliance in a global context.
AIs impact on businesses, economies, and societal structures is transformational: elevating efficiency and automation, boosting analysis and insights through big data processing (Agbehadji et al., 2020; Batko & Slezak, 2022; Bragazzi et al., 2020) and predictive analysis (Ali & Djalilian, 2023; Choi et al., 2023; Khan, 2023; Kumar et al., 2022; Lim et al., 2023; Perkins, 2023; Tong & Zhang, 2023), increasing user experience through personalization (customized user experiences) and almost-natural interactions (chatbots and voice assistants), fostering innovation in products and services through new offerings and service enhancement, supporting decision-making through complex problem solving and real-time decisions (for e.g., in sectors like finance and logistics), providing scalability by handling growth and globalizing operations, boosting creativity and design through content creation (Gozalo- Brizuela & Garrido-Merchan, 2023; Ipsos, 2023) and optimizing design (Brossard et al., 2020).
AI is used to address societal challenges in healthcare and environment, as well as to detect bias and enhance transparency, thus providing a more ethical and responsible notion of AI (Ipsos, 2023).
1See also IAB AI Standards Working Group (2021) definition: Artificial intelligence or AI is the empowerment of machines to use reason and understanding to complete tasks, unlike natural intelligence, which humans and animals employ and involve conscious reasoning and understanding (p. 34).