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：过去几年中，普适计算作为企业级系统、消费级应用和游戏系统等广泛领域的主要实现方式日益普及。普适计算指将计算技术融入日常物品与环境，构建能够相互通信并与人类交互的互联设备网络。通过采用普适计算技术，社区成员能够更便捷地进行沟通协作，从而建立更紧密高效的社会联结。这种互联协作机制有助于构建更具成功潜力与可持续性的社区生态。然而，普适计算的普及也凸显了自动化学习与智能应用的重要性。尽管人工智能与深度学习领域已取得显著进展，但由于昂贵且高度复杂的云端数值计算基础设施面临日益增长的压力，大规模应用推广仍显迟滞。构建实用机器学习系统不仅需要复杂的基础设施支撑，更要求具备数据科学与机器学习的专业能力，导致其开发与应用成本居高不下。本文提出一种创新的低代码开发与部署方案，用于构建端到端AI协同应用流水线。我们基于通证经济模型，在完全去中心化的全球协作社区中，系统解决了基础设施分配、成本控制与安全作业分发等核心问题。