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.

翻译：人工智能在多个领域中日益普及。智能手机、社交媒体平台、搜索引擎和自动驾驶汽车只是利用人工智能技术提升性能的若干应用实例。本研究遵循PRISMA框架，对当前最先进的人工智能技术进行了范围综述，旨在发现人工智能技术研究不同领域中使用的最先进技术。研究选取了人工智能与机器学习领域的三本权威期刊：《人工智能研究杂志》、《机器学习研究杂志》和《机器学习》，并观察了2022年发表的文章。对技术解决方案设定了特定条件：技术必须经过与同类解决方案的对比测试；应用时需使用普遍认可或充分论证的数据集；结果须显示出相较于同类解决方案的改进。技术开发中最关键的环节之一，似乎是如何处理并利用从多源采集的数据。这些数据可能高度非结构化，技术解决方案应能通过最少的人工操作利用这些数据。本综述结果表明，创建标注数据集非常耗时，因此利用无监督或半监督学习技术的解决方案正受到越来越多研究。学习算法应能高效更新，且预测结果应具有可解释性。在将人工智能技术应用于实际场景时，安全性和可解释预测是大众化应用前必须考虑的前提条件。