A Multiagent Framework for the Asynchronous and Collaborative Extension of Multitask ML Systems

TL;DR

This paper introduces a multiagent framework that facilitates asynchronous and collaborative development of large-scale multitask machine learning systems, aiming to accelerate innovation and broaden accessibility.

Contribution

It proposes a modularized representation of ML models and new abstractions to enable collaborative, asynchronous extension of intelligent systems.

Findings

Framework supports diverse asynchronous extensions

Modular design enhances collaborative development

Potential to accelerate ML innovation

Abstract

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.