Kordjamshidi Parisa, Roth Dan, Kersting Kristian
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, United States.
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States.
Front Artif Intell. 2022 Mar 14;5:755361. doi: 10.3389/frai.2022.755361. eCollection 2022.
Data-driven approaches are becoming increasingly common as problem-solving tools in many areas of science and technology. In most cases, machine learning models are the key component of these solutions. Often, a solution involves multiple learning models, along with significant levels of reasoning with the models' output and input. However, the current tools are cumbersome not only for domain experts who are not fluent in machine learning but also for machine learning experts who evaluate new algorithms and models on real-world data and develop AI systems. We review key efforts made by various AI communities in providing languages for high-level abstractions over learning and reasoning techniques needed for designing complex AI systems. We classify the existing frameworks based on the type of techniques and their data and knowledge representations, compare the ways the current tools address the challenges of programming real-world applications and highlight some shortcomings and future directions. Our comparison is only qualitative and not experimental since the performance of the systems is not a factor in our study.
数据驱动方法作为解决问题的工具在许多科学技术领域正变得越来越普遍。在大多数情况下，机器学习模型是这些解决方案的关键组成部分。通常，一个解决方案涉及多个学习模型，以及对模型输出和输入进行大量的推理。然而，当前的工具不仅对于不精通机器学习的领域专家来说很麻烦，而且对于那些在真实世界数据上评估新算法和模型并开发人工智能系统的机器学习专家来说也很麻烦。我们回顾了各个人工智能社区在为设计复杂人工智能系统所需的学习和推理技术提供高级抽象语言方面所做的主要努力。我们根据技术类型及其数据和知识表示对现有框架进行分类，比较当前工具解决编程实际应用挑战的方式，并突出一些缺点和未来方向。我们的比较只是定性的，而非实验性的，因为系统性能不是我们研究的一个因素。
