Recent Trends in Artificial Intelligence Technology: A Scoping Review

TL;DR

This paper reviews recent advances in artificial intelligence technologies across various domains, emphasizing data processing, unsupervised learning, and the importance of safety and interpretability for real-world deployment.

Contribution

It provides a systematic scoping review of 2022 AI research, highlighting trends like semi-supervised learning and the focus on data exploitation and safety considerations.

Findings

Growing research on unsupervised and semi-supervised learning.

Emphasis on data processing and minimal manual intervention.

Safety and interpretability are crucial for real-world AI applications.

Abstract

Artificial intelligence is more ubiquitous in multiple domains. Smartphones, social media platforms, search engines, and autonomous vehicles are just a few examples of applications that utilize artificial intelligence technologies to enhance their performance. This study carries out a scoping review of the current state-of-the-art artificial intelligence technologies following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The goal was to find the most advanced technologies used in different domains of artificial intelligence technology research. Three recognized journals were used from artificial intelligence and machine learning domain: Journal of Artificial Intelligence Research, Journal of Machine Learning Research, and Machine Learning, and articles published in 2022 were observed. Certain qualifications were laid for the technological solutions: the technology must be tested against comparable solutions, commonly approved or otherwise well justified datasets must be used while applying, and results must show improvements against comparable solutions. One of the most important parts of the technology development appeared to be how to process and exploit the data gathered from multiple sources. The data can be highly unstructured, and the technological solution should be able to utilize the data with minimum manual work from humans. The results of this review indicate that creating labeled datasets is very laborious, and solutions exploiting unsupervised or semi-supervised learning technologies are more and more researched. The learning algorithms should be able to be updated efficiently, and predictions should be interpretable. Using artificial intelligence technologies in real-world applications, safety and explainable predictions are mandatory to consider before mass adoption can occur.