a. Late 2010s to 2020s: AI sees applications in almost every domain, from healthcare (Bohr and Memarzadeh 2020) to finance (Goodell et al., 2021) to entertainment (Mateas 2002) and education (Farrokhnia et al., 2023; Liang et al., 2023). The role of AI in data analytics (Kibria et al., 2018), natural language processing (Deng and Liu 2018), and robotics (Bogue 2014) becomes especially pronounced.
b. 2020s and Beyond: The convergence of AI with other technologies like quantum computing (Ajagekar and You 2022; Choi et al 2020) and augmented reality. Artificial intelligence and quantum computing share several common features. Quantum computing, in particular, offers artificial intelligence and machine learning algorithms a significant advantage in terms of training speed and computational power, often at a lower cost (Abdelgaber and Nikolopulos 2020). At the same time, the convergence of quantum computing and AI necessitates the achievement of various milestones to unlock their full potential in the realm of quantum computing AI (Rawat et al 2022).
Augmented Reality (AR) entails enhancing our perception of reality by superimposing digital information onto objects viewed through a device. The fusion of AR and AI stands out as a prominent and imminent direction, acknowledged by numerous industries and academic circles. By harnessing the capabilities of AI, there exists significant potential for industries to enhance production speed, workforce training, manufacturing processes, error handling, assembly tasks, and packaging procedures. As is the case with quantum computing, there are numerous technological obstacles when applying AI to AR. Overcoming these, however, are beneficial, because with the infusion of AI capabilities, AR systems will gain the autonomy to operate within production environments with minimal human intervention. This synergy between AI and AR empowers these systems to seamlessly interact with both the manufacturing environment and human operators, thereby fostering the realization of Industry 4.0 (Devagiri et al., 2022).
Increased focus on general AI (artificial general intelligence – AGI). General AI refers to a form of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks and domains, much like a human being. Unlike narrow or specialized AI, which is designed to excel at specific tasks or within predefined domains, general AI aims to exhibit human-like cognitive abilities and adaptability (Grudin et al 2019; Pei et al 2019). There are those that argue that there exists a genuine potential for substantial and interconnected progress in AGI design, engineering, evaluation, and theory in the relatively near future, possibly within the next few decades and potentially even sooner (Goertzel 2014).
c. In the 2020s and beyond, chat AI has transformed the landscape of digital communication and information processing. These advanced AI systems, exemplified by models like GPT-4 from OpenAI, have become integral in various fields, providing accurate, context-aware, and often creative responses (see Ray 2023 and Sohail et al., 2023 for comprehensive reviews). They assist in language translation, content creation, education, and even complex problem-solving. With continual advancements, these AI models are expected to become more sophisticated, offering even more personalized and nuanced interactions (Hassani and Silva 2023). Their impact on industries such as customer service, healthcare, and education is profound, automating tasks and providing insights that were previously impossible. As we move forward, the integration of AI in daily life promises to be more seamless and impactful, heralding a new era of human-AI collaboration.