Content for  TR 22.874  Word version:  18.2.0

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A  Introduction to AI/ML modelsp. 66

A.1  AI and MLp. 66

Artificial Intelligence (AI)/Machine Learning (ML) is being used in a range of application domains across industry sectors, realizing significant productivity gains. In particular, in mobile communications systems, mobile devices (e.g. smartphones, smart vehicles, UAVs, mobile robots) are increasingly replacing conventional algorithms (e.g. speech recognition, machine translation, image recognition, video processing, user behaviour prediction) with AI/ML models to enable applications like enhanced photography, intelligent personal assistants, VR/AR, video gaming, video analytics, personalized shopping recommendation, autonomous driving/navigation, smart home appliances, mobile robotics, mobile medicals, as well as mobile finance. As forecast by Gartner [44], more than 80% of enterprise IoT projects will include an AI component by 2022, up from only 10% today.
Artificial Intelligence (AI) is the science and engineering to build intelligent machines capable of carrying out tasks as humans do, defined by John McCarthy in 1956. The categorization of AI approaches can be illustrated in Figure A.1-1 [25].
Copy of original 3GPP image for 3GPP TS 22.874, Fig. A.1-1: Categorization of AI/ML approaches (Figure adopted from [25])
Within AI is a large subfield called machine learning (ML), which was defined in 1959 by Arthur Samuel as the field of study that gives computers the ability to learn without being explicitly programmed. Instead of the laborious and hit-or-miss approach of creating a distinct, custom program to solve each individual problem in a domain, a single ML algorithm simply needs to learn, via a process called training, to handle each new problem [25]. Many ML methodologies as exemplified by decision tree, K-means clustering, and Bayesian network have been developed to train the model to make classifications and predictions, based on the data obtained from the real world [19].

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