人工智能技术的最新趋势:范围综述

ArXiv Pub Date : 2023-05-08 DOI:10.48550/arXiv.2305.04532
Teemu Niskanen, T. Sipola, Olli Väänänen
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引用次数: 1

摘要

人工智能在多个领域更加普遍。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高其性能的几个应用示例。本研究根据PRISMA框架对当前最先进的人工智能技术进行了范围审查。目标是找到人工智能技术研究中不同领域使用的最先进技术。使用了人工智能和机器学习领域的三种公认期刊:Journal of artificial intelligence Research、Journal of machine learning Research和machine learning,并观察了2022年发表的文章。为技术解决方案设定了某些条件:技术必须针对可比解决方案进行测试,在应用时必须使用普遍批准或其他充分证明的数据集,并且结果必须显示相对可比解决方案的改进。技术发展中最重要的部分之一似乎是如何处理和利用从多个来源收集的数据。数据可以是高度非结构化的,技术解决方案应该能够以最少的人工工作来利用数据。这篇综述的结果表明,创建标记数据集是非常费力的,利用无监督或半监督学习技术的解决方案越来越多的研究。学习算法应该能够有效地更新,并且预测应该是可解释的。在实际应用中使用人工智能技术,在大规模采用之前必须考虑安全性和可解释的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Latest Trends in Artificial Intelligence Technology: A Scoping Review
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.
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