论文标题
组成复合和混合AI解决方案
Composing Complex and Hybrid AI Solutions
论文作者
论文摘要
通过舒适，有效的实验手段，清晰的界面和可互换的组件，例如使用OpenCV进行计算机视觉或ROS作为机器人技术，可以实现计算机科学的多个领域的进展。我们描述了Acumos系统的扩展，以启用上述特征作为一般AI应用程序。最初，Acumos是为电信目的而创建的，主要用于创建机器学习组件的线性管道。我们的扩展包括支持具有GRPC/ProtoBuf接口的更多通用组件，自动编排图形组装解决方案，包括控制循环，子组件拓扑以及基于事件的通信以及用于组装包含用户接口和共享存储区域的解决方案的规定。我们提供可部署解决方案及其界面的示例。该框架在http://aiexp.ai4europe.eu/上部署，其源代码被视为开源Eclipse项目。
Progress in several areas of computer science has been enabled by comfortable and efficient means of experimentation, clear interfaces, and interchangable components, for example using OpenCV for computer vision or ROS for robotics. We describe an extension of the Acumos system towards enabling the above features for general AI applications. Originally, Acumos was created for telecommunication purposes, mainly for creating linear pipelines of machine learning components. Our extensions include support for more generic components with gRPC/Protobuf interfaces, automatic orchestration of graphically assembled solutions including control loops, sub-component topologies, and event-based communication,and provisions for assembling solutions which contain user interfaces and shared storage areas. We provide examples of deployable solutions and their interfaces. The framework is deployed at http://aiexp.ai4europe.eu/ and its source code is managed as an open source Eclipse project.
