The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the self-building AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications.

新兴的信息革命使得必须快速管理大量非结构化数据。可以感知周围环境并相互通信的物联网设备和传感器使世界变得越来越拥挤，因此，已经创建了一个数字环境，其中包含大量易失性和多样化的数据。针对确定性情况设计的传统AI和机器学习技术不适合此类环境。在此数字环境中，每个设备都需要大量参数，因此希望AI具有自适应性和自建性（即自结构，自配置，自学习），而不是结构和参数方式预定义。这项研究探索了在无监督学习的情况下自我构建AI和机器学习对智能城市环境中大数据分析的支持所带来的好处。通过使用不断增长的自组织图，提出了一套新的自构建AI。自我构建的AI克服了传统AI的局限性，并在动态智能城市环境中实现了数据处理。借助云计算平台，自建式AI可以集成当前在孤岛中工作的数据分析应用程序。使用物联网，视频监控和动作识别应用程序演示了自我构建AI的新范例及其价值。自我构建的AI克服了传统AI的局限性，并在动态智能城市环境中实现了数据处理。借助云计算平台，自建式AI可以集成当前在孤岛中运行的数据分析应用程序。使用物联网，视频监控和动作识别应用程序演示了自我构建AI的新范例及其价值。自我构建的AI克服了传统AI的局限性，并在动态智能城市环境中实现了数据处理。借助云计算平台，自建式AI可以集成当前在孤岛中运行的数据分析应用程序。使用物联网，视频监控和动作识别应用程序演示了自我构建AI的新范例及其价值。