Abstract
Artificial intelligence (AI) and extended reality (XR) differ in their origin and primary objectives. However, their combination is emerging as a powerful tool for addressing prominent AI and XR challenges and opportunities for cross-development. To investigate the AI-XR combination, we mapped and analyzed published articles through a multi-stage screening strategy. We identified the main applications of the AI-XR combination, including autonomous cars, robotics, military, medical training, cancer diagnosis, entertainment, and gaming applications, advanced visualization methods, smart homes, affective computing, and driver education and training. In addition, we found that the primary motivation for developing the AI-XR applications include 1) training AI, 2) conferring intelligence on XR, and 3) interpreting XR- generated data. Finally, our results highlight the advancements and future perspectives of the AI-XR combination.
Highlights
Artificial Intelligence (AI) refers to the science and engineering used to produce intelligent machines (Hamet and Tremblay, 2017)
A review of the affiliations of the authors of the included papers indicated that most AI-XR combination applications were developed in high-ranking research institutes, underscoring the importance of the AI-XR research area
The leading highranking research institutes cited by the selected articles include Google Brain, Google Health, Intel Labs, different labs in Massachusetts Institute of Technology (MIT), Microsoft Research, Disney Research, Stanford Vision and Learning Laboratory, Ohio Supercomputer Center, Toyota Research Institute, Xerox Research Center, as well as a variety of robotics research laboratories, medical schools, and AI labs
Summary
Artificial Intelligence (AI) refers to the science and engineering used to produce intelligent machines (Hamet and Tremblay, 2017). In the first period (1956–1970), the field of AI began, and terms such as machine learning (ML) and natural language processing (NLP) started to develop (Newell et al, 1958; Samuel, 1959; Warner et al, 1961; Weizenbaum, 1966). The third period (2012–2016) began with the advancement of the deep learning (DL) method with the ability to detect cats in pictures (Krizhevsky et al, 2012). Owing to DL advancements, in the fourth period (2016 to the present), the application of AI has achieved notable success as AI has outperformed humans in various tasks (Gulshan et al, 2016; Ehteshami Bejnordi et al, 2017; Esteva et al, 2017; Wang et al, 2017; Yu et al, 2017; Strodthoff and Strodthoff, 2019)
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