论文标题
多功能框架，用于多任务ML系统的异步和协作扩展
A Multiagent Framework for the Asynchronous and Collaborative Extension of Multitask ML Systems
论文作者
论文摘要
传统的ML开发方法并不能使大量贡献者（每个目标都具有不同的目标）共同致力于共享智能系统的创建和扩展。实现这种协作方法可以加快创新速率，提高ML技术的可访问性并实现新型能力的出现。我们认为，这种新颖的ML开发方法可以通过ML模型的模块化表示以及新颖的抽象的定义来证明，从而可以实施和执行多种方法，以实施和扩展模块化智能系统的异步使用和扩展。我们提出了一个多基因框架，用于动态大规模多任务系统的协作和异步扩展。
The traditional ML development methodology does not enable a large number of contributors, each with distinct objectives, to work collectively on the creation and extension of a shared intelligent system. Enabling such a collaborative methodology can accelerate the rate of innovation, increase ML technologies accessibility and enable the emergence of novel capabilities. We believe that this novel methodology for ML development can be demonstrated through a modularized representation of ML models and the definition of novel abstractions allowing to implement and execute diverse methods for the asynchronous use and extension of modular intelligent systems. We present a multiagent framework for the collaborative and asynchronous extension of dynamic large-scale multitask systems.
