Innovations Labs

Project Owner

Cognitively Integrated AI

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n/a

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

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Overview

This proposal introduces a groundbreaking AGI framework termed "Cognitively Integrated AI" (CIAI). This innovative approach uniquely blends the principles of information theory with the cognitive mechanism of conceptual blending within a sophisticated distributed multi-agent system. By harnessing the power of information theory to guide and optimize the creation of novel concepts, CIAI directly addresses the current limitations in AGI research, particularly the challenges of achieving genuine creativity, generating truly novel ideas, and ensuring the coherent integration of knowledge from disparate sources.

RFP Guidelines

Experiment with concept blending in MeTTa

Proposal Submission (5 days left)

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

SingularityNET

Apr. 14, 2025

SingularityNET

rfp owner

Experiment with concept blending in MeTTa

Proposal Submission (5 days left)

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. Bids are expected to range from $30,000 - $60,000.

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Proposal Description

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Total Milestones
3
Total Budget
$55,000_USD
Last Updated
20 May 2025

Milestone 1 - Research & Prototyping Phase

Description

Milestone 1: Research & Prototyping Phase Funding Allocation: $11,000

Deliverables

Deliverable Description: Completion of detailed system architecture and design documentation Prototype modules for: Information-theoretic blending engine in MeTTa FFCA integration in Atomspace Initial implementation of core multi-agent roles (Retriever, Blender)

Budget

$11,000 USD

Success Criterion

Success Criteria: Approved system architecture and technical design by project reviewers Blending prototype demonstrates entropy/mutual information computation FFCA module successfully models at least 5 fuzzy concept sets Agents communicate successfully within OpenCog Hyperon test environment

Milestone 2 - System Integration & Knowledge Graph Framework

Description

Milestone 2: System Integration & Knowledge Graph Framework Funding Allocation: $22,000

Deliverables

Deliverable Description: Integration of FFCA knowledge representation with Atomspace Implementation of all core agents (Retriever, Blender, Evaluator, Checker, Orchestrator) Full pipeline for concept generation and evaluation APIs for agent communication and prompt-based input/output processing

Budget

$22,000 USD

Success Criterion

Success Criteria: All agents can execute concept blending collaboratively on test cases Atomspace reflects dynamic updates post-blending API endpoints return valid concept blends given defined prompts LLM-based Creativity Evaluator functional using OpenAI or Gemini

Milestone 3 - Evaluation, Optimization, and Open Source Release

Description

Milestone 3: Evaluation, Optimization, and Open Source Release Funding Allocation: $20,000

Deliverables

Deliverable Description: Evaluation framework using LLM scoring, semantic similarity, and rule-based coherence checking Performance benchmarking against baseline creative AI systems Open-source release of codebase with documentation, reproducible test cases, and user instructions Community demo video and project report

Budget

$22,000 USD

Success Criterion

Success Criteria: Generated concepts rated as novel and coherent by LLM and human evaluators Framework shows measurable improvement in concept diversity vs. baseline GitHub repo is publicly accessible with installation/setup guide Live demo video and final project report submitted to funders and stakeholders

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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

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

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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.

Innovations Labs

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Obongofon Udombat

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Project Lead

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Joseph Iyanda

AI Researcher

Project Researcher

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Victor Jacob Asuquo

ML Developer

LLMs Finetuning

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ubyjerome

Backend Developer

Backend Infrastructure

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Godspower Ufot

Frontend Dev.

Frontend Software Designer

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DannyCodes

Software Dev.

API and NLP Processing

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