摘要:人工智能技术因其强大的学习和泛化能力已被广泛应用于各种真实场景中.然而，现有的人工智能技术仍然面临着三大挑战：第一，现有的AI技术使用门槛高，依赖于AI从业者选择合适模型、设计合理参数、编写程序，因此很难被广泛应用到非计算机领域；第二，现有的AI算法训练效率低，造成了大量计算资源的浪费，甚至延误决策时机；第三，现有的AI技术非常强地依赖于高质量数据，如果数据质量较低，可能带来计算结果的错误.数据库技术可以有效解决这3个难题，因此目前，面向AI的数据管理得到了广泛关注.首先给出AI中数据管理的整体框架，然后详细综述基于声明式语言模型的AI系统、面向AI优化的计算引擎、执行引擎和面向AI的数据治理引擎这4个方面，最后展望未来的研究方向和所面临的挑战.



Abstract:Artificial intelligence has been widely used in various scenarios due to its powerful learning and generalization ability. However, most of the existing AI techniques are facing three major challenges. First, existing AI techniques are hard to use for ordinary users, which depends on AI experts to select appropriate models, choose reasonable parameters and write programs, so it is difficult to be widely used in non-IT fields. Second, the training efficiency of existing AI algorithms is low, resulting in a lot of waste of computing resources, even delaying decision-making opportunities. Third, existing AI techniques are strongly dependent on high-quality data. If the data quality is low, it will make error decisions. The database technology can effectively solve these three problems, and AI-oriented data management has been widely studied. Firstly, this paper gives the overall framework of data management in AI. Then, it presents a detailed overview of AI-oriented declarative language model, AI-oriented optimization, AI-oriented execution engine, and AI-oriented data governance. Finally, the future research directions and challenges are provided.



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