🤖 AI Summary
This study addresses key deployment bottlenecks of AI models—high-cost manual annotation, poor interpretability, and difficulty in continuous adaptation—when processing multi-source, heterogeneous (especially unstructured) data. Following the PRISMA framework, we conducted a scoping review of top-tier AI papers published in 2022 across three leading journals. Our work is the first AI survey to systematically characterize the paradigm shift from supervised to unsupervised/semi-supervised learning as a viable alternative to manual labeling, while explicitly positioning safety and interpretability as prerequisites for large-scale adoption. We identify three core trends: (1) data-efficient learning (e.g., self-supervised and continual learning), (2) lightweight model adaptation via dynamic updating, and (3) safe, interpretable prediction through integration of eXplainable AI (XAI) and robust reasoning. Results confirm that unsupervised/semi-supervised approaches are becoming mainstream, substantially reducing annotation dependency and enhancing industrial deployability—marking a strategic transition in AI research from performance-centric to trust- and utility-oriented paradigms.
📝 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 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.
