探索人工智能技术的最新趋势:全面回顾

Jeff Shuford, Md.mafiqul Islam
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摘要

人工智能(AI)已日益渗透到各个领域,包括智能手机、社交媒体平台、搜索引擎和自动驾驶汽车等。本研究按照 PRISMA 框架,对当前的人工智能技术进行了范围审查,旨在确定人工智能研究的不同领域所使用的最先进技术。本次综述选择了人工智能和机器学习领域的三本著名期刊,即《人工智能研究期刊》、《机器学习研究期刊》和《机器学习》。对 2022 年发表的文章按照一定的标准进行了审查:技术必须经过可比解决方案的测试,采用普遍认可或合理的数据集,并证明比可比解决方案有所改进。本次审查确定的技术开发的一个重要方面是处理和利用从不同来源收集的数据。鉴于数据的高度非结构化性质,技术解决方案应尽量减少人工干预的需要。综述表明,创建标记数据集是一个劳动密集型过程,因此研究重点越来越多地放在利用无监督或半监督学习技术的解决方案上。学习算法的高效更新和预测的可解释性成为开发人工智能技术的关键考虑因素。此外,在现实世界的应用中,确保安全性和提供可解释的预测是实现广泛应用之前的当务之急。因此,本综述强调了解决这些因素的重要性,以促进人工智能技术负责任地、有效地融入各个领域。
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Exploring the Latest Trends in Artificial Intelligence Technology: A Comprehensive Review
Artificial intelligence (AI) has become increasingly pervasive across various domains, including smartphones, social media platforms, search engines, and autonomous vehicles, among others. This study undertakes a scoping review of the current landscape of AI technologies, following the PRISMA framework, with the aim of identifying the most advanced technologies utilized in different domains of AI research. Three reputable journals within the artificial intelligence and machine learning domain, namely the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, and Machine Learning, were selected for this review. Articles published in 2022 were scrutinized against certain criteria: the technology must be tested against comparable solutions, employ commonly approved or well-justified datasets, and demonstrate improvements over comparable solutions. A crucial aspect of technology development identified in this review is the processing and exploitation of data collected from diverse sources. Given the highly unstructured nature of data, technological solutions should minimize the need for manual intervention by humans. The review indicates that creating labeled datasets is a labor-intensive process, leading to increased research focus on solutions leveraging unsupervised or semi-supervised learning technologies. Efficient updating of learning algorithms and the interpretability of predictions emerge as key considerations in the development of AI technologies. Moreover, in real-world applications, ensuring safety and providing explainable predictions are imperative before widespread adoption can be achieved. Thus, this review underscores the importance of addressing these factors to facilitate the responsible and effective integration of AI technologies into various domains.
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