Over the past few years, ubiquitous, or pervasive computing has gained popularity as the primary approach for a wide range of applications, including enterprise-grade systems, consumer applications, and gaming systems. Ubiquitous computing refers to the integration of computing technologies into everyday objects and environments, creating a network of interconnected devices that can communicate with each other and with humans. By using ubiquitous computing technologies, communities can become more connected and efficient, with members able to communicate and collaborate more easily. This enabled interconnectedness and collaboration can lead to a more successful and sustainable community. The spread of ubiquitous computing, however, has emphasized the importance of automated learning and smart applications in general. Even though there have been significant strides in Artificial Intelligence and Deep Learning, large scale adoption has been hesitant due to mounting pressure on expensive and highly complex cloud numerical-compute infrastructures. Adopting, and even developing, practical machine learning systems can come with prohibitive costs, not only in terms of complex infrastructures but also of solid expertise in Data Science and Machine Learning. In this paper we present an innovative approach for low-code development and deployment of end-to-end AI cooperative application pipelines. We address infrastructure allocation, costs, and secure job distribution in a fully decentralized global cooperative community based on tokenized economics.

翻译：过去几年间，泛在计算（或称普适计算）已成为企业级系统、消费者应用及游戏系统等多种场景的主要计算范式。泛在计算指将计算技术融入日常物品与环境之中，构建起互联设备网络，实现设备间及设备与人类之间的通信。借助泛在计算技术，社区成员可更便捷地沟通协作，从而提升社区连通性与运行效率，推动社区向更成功、更可持续的方向发展。然而，泛在计算的普及也凸显了自动化学习与智能应用的普遍重要性。尽管人工智能和深度学习已取得显著进展，但由于高昂且高度复杂的云端数值计算基础设施带来的压力，大规模应用仍面临阻碍。采用乃至开发实用的机器学习系统不仅需要复杂的基础设施，还需扎实的数据科学与机器学习专业知识，其成本令人望而却步。本文提出一种创新方法，用于低代码开发与部署端到端AI协作应用流水线。我们基于代币化经济模型，在完全去中心化的全球协作社区中解决基础设施分配、成本控制及安全任务分发问题。