NeuroX Labs

Project Owner

HyperonGen: Advancing Hyperon’s Cognition

Expert Rating

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Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Experiment with concept blending in MeTTa

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Overview

This project aims to develop advanced concept creation techniques within OpenCog Hyperon using MeTTa. By implementing formal concept analysis (FCA) methods, including fuzzy and paraconsistent variations, alongside concept blending, we will create algorithms that generate novel, meaningful concepts. These concepts will be evaluated for creativity and coherence through both human and machine-based assessments, leveraging tools like large language models (LLMs). The project enhances Hyperon’s cognitive synergy, supporting AGI development by refining concept formation processes and integrating them into Hyperon’s Distributed Atomspace.

RFP Guidelines

Experiment with concept blending in MeTTa

Internal Proposal Review

Type SingularityNET RFP
Total RFP Funding $100,000 USD
Proposals 6
Awarded Projects n/a

SingularityNET

Oct. 4, 2024

This RFP seeks proposals that experiment with concept blending techniques and formal concept analysis (including fuzzy and paraconsistent variations) using the MeTTa programming language within OpenCog Hyperon. The goal is to explore methods for generating new concepts from existing data and concepts, and evaluating these processes for creativity and efficiency.

Proposal Description

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Total Milestones
5
Total Budget
$40,000_USD
Last Updated
8 Dec 2024

Milestone 1 - Initial Setup and Planning

Description

Setting up the MeTTa environment and defining the core parameters for algorithm development. This stage involves configuring software researching necessary tools and drafting initial planning documentation.

Deliverables

Fully functional MeTTa environment clear project roadmap defined project parameters and data structure.

Budget

$5,000 USD

Success Criterion

The environment is fully functional, with all the necessary components for algorithm development in place.

Milestone 2 - Core Algorithm Development

Description

Implementing the Formal Concept Analysis (FCA) and concept blending algorithms ensuring they align with the project’s goals of generating novel concepts.

Deliverables

Functional FCA and concept blending algorithms initial testing results and performance evaluation reports

Budget

$12,000 USD

Success Criterion

Algorithms are developed, tested for functionality, and produce meaningful outputs according to predefined criteria

Milestone 3 - Creativity Evaluation Framework

Description

Design and integrate both human and machine-based evaluation methods including LLM tools to assess the creativity and coherence of generated concepts.

Deliverables

A robust evaluation framework with tools for human and machine assessments and a preliminary evaluation of concept generation.

Budget

$8,000 USD

Success Criterion

A functioning creativity evaluation system that gathers feedback from both human and machine assessments, with positive early-stage results.

Milestone 4 - Practical Integration and Demonstration

Description

Integrating the generated concepts into Hyperon’s Distributed Atomspace and demonstrating their real-world use through integration and test cases.

Deliverables

Full integration of concepts into Hyperon including demonstration use cases that show the value of these new abstractions

Budget

$10,000 USD

Success Criterion

Concepts are successfully integrated into Hyperon, and use cases are developed and tested successfully

Milestone 5 - Final Documentation and API Development

Description

Finalizing comprehensive documentation for the algorithms evaluation framework and integration process. This includes developing APIs for future users to extend and use the tools created.

Deliverables

Final documentation (including user guides technical documentation) API for further development.

Budget

$5,000 USD

Success Criterion

All documentation and APIs are complete, user-friendly, and provide clear instructions on how to extend the system.

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The weighted average of the 4 perspectives Overall

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Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

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RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

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The weighted average of the 4 perspectives Overall

0.0
Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

0.0
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

0.0

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.

Overall (Primary)
This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).

Feasibility (secondary)

This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.

Viability (secondary)

This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary)

Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference?

Example:

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

Usefulness (secondary)

This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”

For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
Examples:

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

NeuroX Labs

Project Owner

Manage the HyperonGen project, applying expertise in FCA and concept blending, ensuring collaboration, creativity, documentation, and meeting milestones within budget.

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