LEF Ai

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Zontonnia Moore
Project Owner

LEF Ai

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Overview

LEF AI is an open neuro-symbolic framework that fuses deep learning with symbolic reasoning to model human consciousness and emotional intelligence. By leveraging PyNeuraLogic and Kolmogorov-Arnold Networks, LEF AI enables interpretable, recursive self-regulated learning for adaptive, transparent, and ethical AGI. This project will demonstrate LEF AI in real-world applications-such as collective intelligence and trauma-informed healthcare-advancing explainable, human-aligned AI.

RFP Guidelines

Neural-symbolic DNN architectures

Internal Proposal Review
  • Type SingularityNET RFP
  • Total RFP Funding $160,000 USD
  • Proposals 19
  • Awarded Projects n/a
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SingularityNET
Apr. 14, 2025

This RFP invites proposals to explore and demonstrate the use of neural-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.

Proposal Description

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Proposal Video

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  • Total Milestones

    3

  • Total Budget

    $60,000 USD

  • Last Updated

    15 May 2025

Milestone 1 - Framework Design and Initial Development

Description

This milestone focuses on designing the LEF AI framework establishing the neuro-symbolic architecture and initiating development. We will define the recursive self-awareness module and integrate initial emotional intelligence components aligning with SingularityNET’s RFP requirements.

Deliverables

- Detailed architecture design document for LEF AI. - Prototype of the recursive self-awareness module. - Initial emotional intelligence integration report.

Budget

$30,000 USD

Success Criterion

- Approval of the architecture design by the project team. - Functional prototype of the self-awareness module, validated through internal testing.

Milestone 2 - Neuro-Symbolic Integration and Testing

Description

In this phase we will integrate the neural and symbolic components of LEF AI focusing on dynamic decision-making capabilities. Extensive testing will be conducted to ensure the system’s performance in simulated community resilience scenarios.

Deliverables

- Fully integrated neuro-symbolic AI system. - Test results from simulated scenarios. - Documentation of integration challenges and solutions.

Budget

$15,000 USD

Success Criterion

- Successful integration of neural and symbolic components, achieving 85% accuracy in decision-making tests. - Positive feedback from internal stakeholders on system performance.

Milestone 3 - Final Deployment and Ethical Validation

Description

The final milestone involves deploying LEF AI in a pilot environment conducting ethical validation per Nevada DOE AI Ethics standards and preparing a final report. We will ensure the system is ready for real-world applications in sustainability.

Deliverables

- Deployed LEF AI system in a pilot environment. - Ethical validation report aligned with Nevada DOE standards. - Final project report with performance metrics.

Budget

$15,000 USD

Success Criterion

- Successful deployment in a pilot environment with no critical issues. - Ethical validation report approved by an independent reviewer.

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