Android生态系统中交互式机器学习应用的工具支持
Towards Tool-Support for Interactive-Machine Learning Applications in the Android Ecosystem
摘要：
消费者应用程序变得越来越智能，其中大多数必须在设备生态系统上运行。潜在的益处是例如实现跨设备交互和无缝的用户体验。机器学习模型对于当今高性能智能解决方案至关重要。然而，这些模型通常由人工智能工程师为一个特定的设备单独开发，并且没有考虑与其模型必须运行的设备生态系统相关的挑战和潜力。我们认为，人工智能工程师需要工具支持，以应对为下一代智能交互式消费应用程序实施、测试和部署机器学习模型的挑战。本文介绍了一系列调查的初步结果，包括与人工智能工程师的访谈和实验的互动机器学习用例与智能手表和智能手机。我们通过采访和实践经验确定了主题，并提出了一些功能，例如从传感器收集数据汇集，以及在目标设备上运行预处理代码的资源消耗的简单测试，这将作为AI工程师的工具支持。
英文摘要：
Consumer applications are becoming increasingly smarter and most of them have to run on device ecosystems. Potential benefits are for example enabling cross-device interaction and seamless user experiences. Essential for today's smart solutions with high performance are machine learning models. However, these models are often developed separately by AI engineers for one specific device and do not consider the challenges and potentials associated with a device ecosystem in which their models have to run. We believe that there is a need for tool-support for AI engineers to address the challenges of implementing, testing, and deploying machine learning models for a next generation of smart interactive consumer applications. This paper presents preliminary results of a series of inquiries, including interviews with AI engineers and experiments for an interactive machine learning use case with a Smartwatch and Smartphone. We identified the themes through interviews and hands-on experience working on our use case and proposed features, such as data collection from sensors and easy testing of the resources consumption of running pre-processing code on the target device, which will serve as tool-support for AI engineers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
