BHouwens

Project Owner

Chimera: Evolving Software

Type SingularityNET RFP
Funding Awarded $25,000 USD
RFP Guidelines Develop interesting demos in MeTTa

Status

Overall Status
⏳ Contract Pending
Funding Transfered
$0_USD
Max Funding Amount
$25,000_USD

Funding Schedule

View Milestones

Milestone Release 1	$6,250 USD	Pending	TBD
Milestone Release 2	$6,250 USD	Pending	TBD
Milestone Release 3	$6,250 USD	Pending	TBD
Milestone Release 4	$6,250 USD	Pending	TBD

Overview

Programming is evolving: from writing solutions to systems that evolve to find their own solutions. The idea behind Chimera is that software of any kind, including agents, can use MeTTa to learn from experiences and evolve through distributed knowledge sharing. AtomSpaces can accelerate learning across multiple instances and agents, allowing each agent or even "dumb" software to benefit from others' evolutionary insights. This proposal's demos will look at self-healing servers, database queries that evolve for performance, and move toward machine "self-consciousness" through reflection and world modelling. The end result will be an SDK/module for use in real projects

RFP Guidelines

Develop interesting demos in MeTTa

Complete & Awarded

Type SingularityNET RFP
Total RFP Funding $100,000 USD
Proposals 21
Awarded Projects 4

SingularityNET

Aug. 12, 2024

Create educational and/or useful demos using SingularityNET's own MeTTa programming language. This RFP aims at bringing more community adoption of MeTTa and engagement within our ecosystem, and to demonstrate and expand the utility of MeTTa. Researchers must maintain demos for a minimum of one year.

Proposal Description

Project details

Chimera is a system where software can use the self-reflective properties of MeTTa and the data source of an AtomSpace to evolve and improve itself. Like a living organism, software in an AGI world should be able to understand its own patterns, learn from experiences, and self-improve through distributed knowledge sharing.

It should keep track of its own state (and perhaps that of the world around it) and when encountering errors or pitfalls, draw from a pool of donor code in an AtomSpace to evolve a solution to its problem. If such an AtomSpace is open to contributions from all, then software could even self-improve through real-time "distributed evolution". Most of all, I'd like to develop this into a reusable SDK/framework, making it easier for new developers to quickly adopt and use.

Demo applications of Chimera can include, as part of the proposal:

HTTP servers that heal themselves from errors
Database query systems that continuously evolve more performant queries
Possibly even translators that can learn how to map NL queries to MeTTa expressions

Chimera is intended to be a stepping stone to more "self-aware" machines, where different types of ML -- including statistical, a posteriori models, reinforcement learning agents, and of course a priori systems like OpenCog -- can coordinate to produce a completely self-sufficient ecosystem.

For this proposal though, I hope it would be enough to show demos of software self-improving by using donor code from AtomSpaces to iterate solutions to its own problems, incorporating them, and then re-sharing them with the community in order for other agents to access and improve. The end result will be the Chimera SDK/module, which will allow developers to use MeTTa's self-reflection and AtomSpace donors for their own projects

Open Source Licensing

MIT - Massachusetts Institute of Technology License

Standard MIT license for Chimera, as well as all SDKs and/or modules and libraries that ease developer adoption and contribution.

Products built on top of Chimera should be allowed to operate as closed source, however

Activity Summary

Milestones

4

Total

Discussion

0

Total Comments

Reviews

3

Total Posted

Project Team

2

Total People

Group Expert Rating (Final)

Overall

5.0

Compliance with RFP requirements 4.0
Solution details and team expertise 3.3
Value for money 3.7

New reviews and ratings are disabled for Awarded Projects

Overall Community

3.3

from 3 reviews

5

1
4

0
3

1
2

1
1

0

Feasibility

4

from 3 reviews

Viability

3.3

from 3 reviews

Desirabilty

3.7

from 3 reviews

Usefulness

0

from 3 reviews

Sort by

3 ratings

Expert Review 1
Overall

5.0
- Compliance with RFP requirements 5.0
- Solution details and team expertise 5.0
- Value for money 0.0
Strong proposal

Promising concept leveraging MeTTa for self-reflective, evolving software. Strong alignment with RFP; practical demos (self-healing servers, query optimization) and SDK deliverables. Concerns: team credentials unclear, ambitious scope for budget. High potential if addressed.

Expert Review 2
Overall

2.0
- Compliance with RFP requirements 3.0
- Solution details and team expertise 2.0
- Value for money 0.0

Expert Review 3
Overall

3.0
- Compliance with RFP requirements 4.0
- Solution details and team expertise 4.0
- Value for money 0.0
This is an interesting direction but seems like maybe a significantly bigger project than the time/budget involved

Posting publicaly as

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Overall

Confidence level that the project is possible at all Feasibility

0
Confidence in a successful outcome considering team, time, and budget Viability

0
Market fit - Balancing needs and benefits against competition Desirabilty

0
To what extent will the project help the AI platform grow? Usefulness

0

Review Headline

0 /50 chars

Review Summary

0 /5000 chars

Expert Review

Overall

Confidence level that the project is possible at all Feasibility

0
Confidence in a successful outcome considering team, time, and budget Viability

0
Market fit - Balancing needs and benefits against competition Desirabilty

0
To what extent will the project help the AI platform grow? Usefulness

0

Review Headline

0 /50 chars

Review Summary

0 /5000 chars

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.

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.

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

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.

Total Milestones
4
Total Budget
$25,000_USD
Last Updated
3 Feb 2025

Milestone 1 - Core Donor System

Status

😐 Not Started

Description

Milestone/Month 1 will focus on building the core system and integrations with MeTTa and an AtomSpace. It will broken down as: WEEK 1-2: - Core MeTTa/AtomSpace integration setup - Basic pattern storage mechanism - Simple evolution algorithms - Test environment setup WEEK 3-4: - Pattern fitness evaluation system - Initial donor pattern collection - Simple pattern sharing between instances - Metrics collection system

Deliverables

- Working MeTTa/AtomSpace environment with pattern storage capabilities - Basic evolutionary system that can: 1. Store successful patterns in AtomSpace 2. Combine existing patterns to generate new ones 3. Evaluate pattern fitness 4. Share patterns between instances - Test suite demonstrating pattern evolution - Initial metrics dashboard for pattern success rates - Documentation of core system architecture

Budget

$6,250 USD

Success Criterion

success_criteria_1

Link URL

Milestone 2 - PoC: Self-Healing Server

Status

😐 Not Started

Description

Milestone/Month 2 will focus on the first comprehensive proof of concept: a self-healing HTTP server in Python. It will be broken down as: WEEK 1-2: - FastAPI server monitoring implementation - Basic error pattern recognition - Simple healing pattern evolution - Performance metrics tracking WEEK 3-4: - Demo: Self-healing HTTP server - Error injection testing - Pattern effectiveness measurement - Basic documentation of findings

Deliverables

- Working FastAPI server with self-healing capabilities: 1. Error detection and classification system 2. Pattern-based healing responses 3. Performance monitoring dashboard - Demo showing automatic recovery from: 1. Memory leaks 2. High CPU utilization 3. Connection pool issues 4. Request handling failures - Documentation of error patterns and healing strategies - Metrics on healing success rates

Budget

$6,250 USD

Success Criterion

success_criteria_1

Link URL

Milestone 3 - PoCs: Database Query Optimizer & NL Query Mapper

Status

😐 Not Started

Description

Milestone/Month 3 will focus on the second and third PoCs: a database query optimizer and a mapper of NL queries to MeTTa expressions. It will be broken down as: WEEK 1-2 (Query Optimization): - PostgreSQL query monitoring - Query pattern recognition - Evolution of query optimizations - Performance benchmarking WEEK 3-4 (NL to MeTTa): - NL pattern recognition system - Basic MeTTa expression generation - Pattern evolution for translations - Accuracy testing framework

Deliverables

Query Optimization System: - PostgreSQL query monitoring interface - Pattern-based query optimization - Performance comparison metrics - Demo showing automatic query improvement NL to MeTTa Translation System: - Basic natural language parser - Pattern-based MeTTa expression generator - Translation accuracy metrics - Demo showing automated translation learning

Budget

$6,250 USD

Success Criterion

success_criteria_1

Link URL

Milestone 4 - SDKs Modules & Documentation

Status

😐 Not Started

Description

Milestone/Month 4 will focus on generalising the learnings of the previous milestones into an SDK module and/or framework that allows it be reused and expanded upon by other developers. It will also lay plans for future development. It will be broken down as: WEEK 1-2: - Integration of all three demos - Initial SDK/module development based on findings - Comprehensive testing - Pattern sharing improvements WEEK 3-4: - Complete core documentation - Research/design for LLM integration for new pattern/solution generation - Future development roadmap - Documentation

Deliverables

- Documentation: 1. System architecture overview 2. Implementation details 3. Installation and setup guides 4. Demo guides and examples - Future Development: 1. LLM integration design document (possible demo) 2. Preliminary SDK/module 3. Proposed roadmap for future features 4. Identified areas for improvement

Budget

$6,250 USD

Success Criterion

success_criteria_1

Link URL

Join the Discussion (0)

Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

5.0

Compliance with RFP requirements 4.0
Solution details and team expertise 3.3
Value for money 3.7

New reviews and ratings are disabled for Awarded Projects

Expert Review 1
Overall

5.0
- Compliance with RFP requirements 5.0
- Solution details and team expertise 5.0
- Value for money 0.0
Strong proposal

Promising concept leveraging MeTTa for self-reflective, evolving software. Strong alignment with RFP; practical demos (self-healing servers, query optimization) and SDK deliverables. Concerns: team credentials unclear, ambitious scope for budget. High potential if addressed.

Expert Review 2
Overall

2.0
- Compliance with RFP requirements 3.0
- Solution details and team expertise 2.0
- Value for money 0.0

Expert Review 3
Overall

3.0
- Compliance with RFP requirements 4.0
- Solution details and team expertise 4.0
- Value for money 0.0
This is an interesting direction but seems like maybe a significantly bigger project than the time/budget involved

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

Review Headline

0 /50 chars

Review Summary

0 /5000 chars

The weighted average of the 4 perspectives Overall

5.0
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

3.7
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

4.0
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

3.3

Review Headline

0 /50 chars

0 /5000 chars

Warning: Adding final group rating for this project will prevent expert users from adding new or editing existing reviews

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.

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

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.

BHouwens

Project Owner

Byron, the technical lead for Chimera, with a background that spans computer science, probability, design, economics, blockchain and AI. I want to bring these fields together to push AGI ahead

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kasia_pm

Project Manager

Kasia, the project manager for Chimera, bringing organisational skills to ensure smooth project implementation and a timely delivery

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