SubThought

Project Owner

Sentient Systems

Expert Rating

n/a

Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Neuro-symbolic DNN architectures

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Overview

Build out Open Source Mechanisms and Memory Framework to support Sentient Systems using Neural Proposition / Scheme knowledge representation.

RFP Guidelines

Neuro-symbolic DNN architectures

Proposal Submission (24 days left)

Type SingularityNET RFP
Total RFP Funding $160,000 USD
Proposals 2
Awarded Projects n/a

SingularityNET

Apr. 14, 2025

SingularityNET

rfp owner

Neuro-symbolic DNN architectures

Proposal Submission (24 days left)

This RFP invites proposals to explore and demonstrate the use of neuro-symbolic deep neural networks (DNNs), such as PyNeuraLogic and Kolmogorov Arnold Networks (KANs), for experiential learning and/or higher-order reasoning. The goal is to investigate how these architectures can embed logic rules derived from experiential systems like AIRIS or user-supplied higher-order logic, and apply them to improve reasoning in graph neural networks (GNNs), LLMs, or other DNNs. Bids are expected to range from $40,000 - $100,000.

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Proposal Description

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Total Milestones
9
Total Budget
$100,000_USD
Last Updated
18 Apr 2025

Milestone 1 - Initiation

Description

Project Start

Deliverables

Commencement fee.

Budget

$5,000 USD

Success Criterion

Project contracts signed.

Milestone 2 - Design Draft

Description

Project Plan and Detailed Design Documents

Deliverables

Detailed Design Documents and project plan

Budget

$5,000 USD

Success Criterion

Documents delivered and Approved.

Milestone 3 - Hardware and APIs

Description

Need devices with working / testable APIs to connect our software to. (Third Party provided).

Deliverables

We will utilize the third party hardware and APIs with which to test our system.

Budget

$50,000 USD

Success Criterion

Testable third party hardware & APIs on production system up and running.

Milestone 4 - CI/CD Environment

Description

Bare metal test and deployment environment

Deliverables

Installed and running bare metal servers.

Budget

$5,000 USD

Success Criterion

In production.

Milestone 5 - Foundation Language

Description

Open Source Language for Coding Mechanisms and Memory

Deliverables

Working Foundation Language

Budget

$10,000 USD

Success Criterion

Working Foundation Language

Milestone 6 - Sentient System (Alpha)

Description

Alpha version of Sentient System

Deliverables

Alpha version of Sentient System

Budget

$10,000 USD

Success Criterion

Working in Production

Milestone 7 - Sentient System Beta

Description

Working Beta version of Sentient System

Deliverables

Working Beta version of Sentient System

Budget

$5,000 USD

Success Criterion

Complete interaction with devices, mechanisms, and memory.

Milestone 8 - Sentient System Assessment

Description

Documented Assessment Methodology for Sentient Systems

Deliverables

Documented Assessment Methodology

Budget

$5,000 USD

Success Criterion

Applied assessment plus ranking by Assessor.

Milestone 9 - Sentient System (Production)

Description

Last iteration deployed in production

Deliverables

System working in production.

Budget

$5,000 USD

Success Criterion

Assessments can be made on an ongoing basis and system is functioning in production.

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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

Review Headline

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0 /5000 chars

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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.

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Cognitive System Architect

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