Long 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: Exploring and Demonstrating the use of evolutionary methods (EMs) such as Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for training various Deep Neural Networks (DNNs) including transformer networks. 

The primary objectives of this EM exploration and demonstration RFP are to determine the effectiveness of:

• EMs for updating node weights;
• EMs for evolving DNN architectures.

Experimental code produced should be wrapped in a Hyperon “Neural Atomspace”, a relatively simple step on which the SingularityNET/Hyperon team can provide guidance.

Ideas/Questions

• Explore the use of evolutionary algorithms for training transformers and other DNNs
• What are the strengths and weaknesses of the selected algorithms compared to backpropagation for neural net training?  How do these vary based on the network architecture?
• For what DNN problem domain(s) would the selected evolutionary algorithms likely work best?
• Evolutionary algorithm strategies for DNNs (e.g. evolution of weights, architectures, both?)

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 of “evolutionary DNNs” in a demonstration “neural Hyperon Atomspace” hosted by SingularityNET 
• Provision of code underlying the initial deployment in an open code repository with an appropriate OSS license.
• Provide comprehensive documentation detailing the structure and functionality of the evolutionary DNNs to allow other teams to work with and to work on the evolutionary DNNs.
• Provide a technical report summarizing experiments run with evolutionary DNNs and results obtained. This should be sufficient to allow others to replicate the work, leveraging the open source code made. The report must include a description of the strengths and weaknesses of each evolutionary DNN, comparisons to standard DNNs, and identification of problem domains in which evolutionary DNNs perform best.