Implement clustering heuristics in MeTTa

Longer description

Context And Background: 

SingularityNET Foundation, in collaboration with other partners such as the OpenCog Foundation and TrueAGI, is working toward a scalable implementation of the Hyperon AGI framework running on decentralized infrastructure, and toward implementation of the PRIMUS cognitive architecture within this framework.

Hyperon and PRIMUS are complex systems involving multiple components, which need to demonstrate appropriate functionalities both individually and in combination. 

This RFP aims to address a portion of this overall need, via funding the initial iteration of one significant component of PRIMUS within Hyperon: “Implement clustering heuristics in MeTTa.”

Clustering is one of the most common unsupervised learning tasks used to group objects of similar characteristics and assign them a common group label. It has applications in various fields such as pattern recognition, image segmentation, genomics, marketing analysis etc. As a language geared towards developing AGI systems, MeTTa needs to include standard libraries that perform common machine learning tasks such as clustering.

MeTTa (Meta Type Talk) is a multi-paradigm language for declarative and functional computations over knowledge metagraphs, designed specifically to meet the needs of Artificial General Intelligence (AGI). It is an innovative and relatively new language, and might sometimes come with a learning curve for starters. While there are also plenty of resources and tutorials that one can reference for his/her needs, they may not be enough to fully cover all possible unique usages. Therefore it will be beneficial to have a coding assistant for MeTTa that can instantly help people to create what they want with MeTTa.

Collaboration:

This RFP will be followed by subsequent RFPs for applications that leverage Hyperon/PRIMUS to carry out various applications, and that aim to guide Hyperon/PRIMUS systems in cognitive development toward beneficial AGI.

RFP Expected Outcomes:

• Initial Deployment: Deployment of demo use cases that make use of the clustering library
• OSS code: Provision of code underlying the initial deployment in an open code repository with an appropriate OSS license (what license should we use?)
• Thorough Documentation: Provision of an accompanying comprehensive documentation detailing each clustering algorithm included in the library along with examples and tutorials showcasing how to use them. The documentation should also describe the structure of the codebase to allow other teams to work on extending the library.
• Technical Report: Provide a technical report summarizing experiments run with the clustering algorithms implemented in the library on well-known benchmark datasets and along with results obtained. The data and code used in the experiments should be publicly available and be shared to allow others to replicate the work.