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等应用，均利用了人工智能技术来提升其性能。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。其目标是寻找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家知名期刊——《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。针对技术解决方案设定了若干资格条件：该技术必须与同类解决方案进行对比测试，应用时必须使用普遍认可或充分论证的数据集，且结果必须显示出相对于同类解决方案的改进。技术开发中最重要的环节之一，似乎是处理和利用从多个来源收集的数据。这些数据可能高度非结构化，而技术解决方案应能以最少的人工工作量利用这些数据。本综述结果表明，创建带标签数据集非常费力，而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的研究。学习算法应能高效更新，预测结果应具有可解释性。在真实世界应用中使用人工智能技术时，安全性和可解释性的预测是大众化普及前必须考虑的因素。