Implementing a vision in which intelligent agents perceive and act in an environment while searching for, exchanging, annotating, and improving machine learning models, forming a culture based on experience and interaction.
Our research goal is to develop a knowledge model and an interaction protocol which allow a system formed of multiple actors — human, software, or organizational — to find, use and share improvements on machine learning resources. Such resources can be datasets, models, or experiences.
We propose the development of the AI Folk framework and methodology, at the intersection of machine learning, knowledge management, and multi-agent systems. It comprises tools and methods that allow the management and discovery of ML-related resources in a distributed system. Federated learning has yet to achieve maturity as a field of study and this is a novel approach which assumes an open system and a variety of resources. We believe that this approach will lead to an advance in the state of the art and will help in the development of standards for open and distributed artificial intelligence.
The project result will be an ontology for describing machine learning resources, a methodology for creating searches for resources, an interaction protocol through which actors can search, transfer and update machine learning resources, the implementation of two applications using this approach, and a general methodology for applying the proposed approach to other application domains.