A Multiagent Framework for the Asynchronous and Collaborative Extension of Multitask ML Systems
This addresses the need for a more accessible and innovative ML development methodology, though it appears incremental as it builds on existing modular and multitask approaches.
The paper tackles the problem of enabling many contributors with distinct objectives to collaboratively extend shared ML systems, proposing a multiagent framework with modularized model representations and novel abstractions for asynchronous execution.
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.