Collaborative Machine Intelligence

Introduction

Machine learning models are growing larger and more complex, making training increasingly resource-demanding. Concurrently, our world, and hence the training data, is perpetually evolving. This requires continual model updating or retraining to address changing training data. Presently, the most reliable course to handle such distribution shifts is to retrain models from scratch on new training data. This results in substantial resource usage, increased CO2 footprint, elevated energy consumption, and limits the decisive ML progress to large-scale industry players.

Vision for Collaborative Learning

Imagine a world in which models help each other learn. When the data distribution changes, a complete retraining of models could be avoided if the new model could learn from the outdated one by using reliable and provably effective methods. Furthermore, the convention of relying on large, versatile monolithic models could then give way to a consortium of smaller specialized models, with each contributing its specific domain knowledge when needed.

By encouraging this form of decentralization, we could reduce resource consumption as the individual components can be updated independently of each other.

Project Goals

Drawing on groundbreaking research in distributed ML model training, CollectiveMinds aspires to design adaptable ML models. These models can effectively manage updates in training data and task modifications while also enabling efficient knowledge exchange across various models, thereby fostering widescale collaborative learning and constructing a sustainable framework for collaborative machine intelligence.

Impact on Various Sectors

This initiative could revolutionize sectors like healthcare, where there is limited training data, and trustworthy AI that demands guarantees on data ownership and control. Furthermore, it could foster improved collaborative research within the realm of science.

CollectiveMinds embodies a significant paradigm shift towards democratizing ML, focusing on cooperative intellectual efforts.