aounjafri

Project Owner

EmpathyCore: AI-Powered Peace Building

Expert Rating

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Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Develop a framework for AGI motivation systems

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Overview

EmpathiCore is a motivation aware AGI system designed to resolve conflicts between people or agents by deeply understanding their emotions, values, and goals. Instead of polarizing debates or biased decisions, it acts as a neutral, emotionally intelligent mediator. From political divides to personal disagreements, EmpathiCore listens, empathizes, and suggests fair, balanced resolutions. It promotes mutual understanding, reduces hostility, and enables peaceful dialogue in an increasingly divided world powered by advanced cognitive architectures like ECAN or Hyperon.

RFP Guidelines

Develop a framework for AGI motivation systems

Internal Proposal Review

Type SingularityNET RFP
Total RFP Funding $40,000 USD
Proposals 21
Awarded Projects n/a

SingularityNET

Apr. 14, 2025

Develop a modular and extensible framework for integrating various motivational systems into AGI architectures, supporting both human-like and "alien digital" intelligences. This could be done as a highly detailed and precise specification, or as a relatively simple software prototype with suggestions for generalization and extension. Bids are expected to range from $15,000 - $30,000.

Proposal Description

Proposal Details Locked…

In order to protect this proposal from being copied, all details are hidden until the end of the submission period. Please come back later to see all details.

Total Milestones
3
Total Budget
$28,000_USD
Last Updated
20 May 2025

Milestone 1 - Research and System Design Phase

Description

This milestone focuses on laying the foundation for EmpathiCore through deep research, strategic planning, and initial system architecture. The team will explore the intersection of AGI, affective computing, and ethical AI to shape the direction of the platform. It will include identification of key emotional modeling frameworks, privacy-preserving methods, and prompt strategies for empathetic alignment. The output will form a clear, research-backed roadmap for building the platform.

Deliverables

-A comprehensive research plan detailing technical approach and ethical guidelines. -A system architecture blueprint covering key modules and data flow. -An agile development plan with clear tasks, sprints, and timeline. -List of initial benchmarks and testing criteria.

Budget

$5,600 USD

Success Criterion

-The research plan clearly aligns with the goals outlined in the RFP. -All proposed features and concepts are justified with literature or precedent. -The architectural design supports modular and privacy-safe implementation. -Agile roadmap provides practical, testable, and measurable development steps.

Milestone 2 - Core Development and Preliminary Testing

Description

In this stage, we will develop the core functionalities of the EmpathyCore platform, including the AGI-powered emotional interaction engine, basic user interfaces, and backend structure. Emphasis will be on building the conceptual framework into a working prototype that demonstrates AGI-based emotional support capabilities. Preliminary testing will be conducted to validate technical viability and emotional response quality.

Deliverables

-Working prototype of the EmpathyCore platform with core emotional response engine. -Backend system capable of capturing and processing emotional cues. -Initial user interface (chat and/or voice interface). -Testing results using sample emotional datasets. -Analysis against basic empathy and emotional response benchmarks.

Budget

$11,200 USD

Success Criterion

-Demonstrated interaction scenarios where the system responds empathetically. -Functioning backend with real-time data processing capability. -Usability of interface for non-technical users. -Performance meets or exceeds basic AGI emotional response expectations.

Milestone 3 - Final Implementation and Delivery

Description

This phase focuses on finalizing the platform, enhancing features, refining the emotional intelligence engine, and preparing for public-facing deployment. The team will integrate all feedback from testing, polish the user experience, and complete documentation. A comprehensive final report will analyze system performance and outcomes. The platform will be made publicly accessible (as a demo or open source) with recommendations for future scaling or research.

Deliverables

-Fully functional EmpathyCore platform with improved AGI emotional support features. -Final documentation covering architecture, usage, and technical details. -Public demonstration or access to the deployed system (e.g., web interface or GitHub). -Final report including performance metrics, user testing insights, and improvement areas.

Budget

$11,200 USD

Success Criterion

-Platform meets goals set out in the proposal with a stable, usable interface. -Documentation enables third-party understanding and contribution. -Public demo or code access encourages external interest or potential adoption. -Clear evidence of impact or potential in AGI for emotional support.

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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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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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Review Headline

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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.

aounjafri

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