PAIDX

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saint david
Project Owner

PAIDX

Expert Rating

1.0

Overview

We propose the **PaidX** project for the $1.25M grant from the SingularityNET Foundation. PaidX aims to create an AI-driven predictive model and decision support system to address flooding caused by the Lagdo Dam in Cameroon, impacting Nigeria. Our team, with expertise in AI and environmental science, will enhance predictive accuracy using **Large Language Models (LLMs)** to analyze socio-economic data. We will conduct economic impact analyses and develop a user-friendly decision support framework. PaidX seeks to improve flood management, increase economic resilience, and provide actionable, data-driven policy recommendations for local authorities.

RFP Guidelines

Utilize LLMs for modeling within MOSES

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $150,000 USD
  • Proposals 10
  • Awarded Projects 1
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SingularityNET
Oct. 9, 2024

This RFP invites proposals to explore the integration of LLMs into the MOSES evolutionary algorithm. Researchers can pursue one of several approaches, including generation modeling, fitness function learning, fitness estimation, investigation into domain-independent “cognitively motivated” fitness functions, or propose new innovative ways to leverage LLMs to enhance MOSES's capabilities within the OpenCog Hyperon framework.

Proposal Description

Company Name (if applicable)

PAIDX

Project details

# Proposal for Advancing Decentralized Beneficial AGI Research through the PaidX Project

## Introduction

We are excited to propose the **PaidX** project for the $1.25M grant offered by the SingularityNET Foundation. PaidX is an AI-Driven Predictive Model and Decision Support System designed to forecast and manage the economic impacts of cross-border dam water releases, specifically addressing the recurrent flooding caused by the Lagdo Dam in Cameroon, which has significant consequences for communities in Nigeria.

## Project Overview: PaidX

The PaidX initiative aims to harness advanced artificial intelligence techniques to develop a holistic solution that integrates predictive modeling, economic impact assessments, and a robust decision support system. Our objective is to empower local authorities and communities with the tools necessary to mitigate flooding’s adverse effects, enhance preparedness, and build resilience against climate-related challenges.

## Who Should Apply?

Our interdisciplinary team comprises researchers with advanced degrees (PhDs) in AI, Computer Science, and Environmental Science, each possessing extensive experience in:

- **Large Language Models (LLMs)**: Employing LLMs to analyze socio-economic data related to flooding events.
- **Algorithms and Evolutionary Methods**: Designing adaptive algorithms to enhance predictive accuracy through continuous learning.
- **Heuristic Techniques**: Utilizing heuristic approaches to optimize flood response strategies.
- **Agent-Based Modeling**: Constructing simulations to model interactions between water releases and socio-economic impacts.
- **Formal Logic**: Applying formal logic to strengthen decision-making frameworks for flood management.

## Grant Objectives

Our proposed research aims to achieve the following objectives:

1. **Enhance Predictive Accuracy**: Develop and validate a predictive model that precisely forecasts water releases and potential flooding events.
2. **Conduct Economic Impact Analysis**: Analyze the socio-economic ramifications of flooding, providing actionable insights for local authorities and stakeholders.
3. **Create a Decision Support Framework**: Develop a user-friendly interface that facilitates real-time decision-making based on predictive analytics.

## Methodology

1. **Data Collection**: Gather historical data on water releases, rainfall patterns, and economic impacts from affected regions, leveraging both local and international data sources.
2. **Model Development**: Utilize state-of-the-art machine learning algorithms to construct a predictive model, incorporating LLMs for comprehensive data interpretation and analysis.
3. **System Integration**: Develop a fully integrated decision support system that combines predictive analytics with real-time data monitoring and user feedback.
4. **Community Engagement**: Actively collaborate with local stakeholders to ensure the system meets practical needs and enhances community resilience.

## Timeline

- **Phase 1 (Months 1-3)**: Comprehensive Data Collection and Initial Model Development
- **Phase 2 (Months 4-6)**: Model Validation, Refinement, and Preliminary Testing
- **Phase 3 (Months 7-9)**: Development of the Decision Support System and User Interface
- **Phase 4 (Months 10-12)**: Community Testing, Feedback Integration, and Final Adjustments

## Budget

The grant funds will be allocated as follows:

- **Personnel**: $600,000 for research staff, project management, and collaboration efforts.
- **Technology**: $300,000 for data acquisition, software development, and computational resources.
- **Community Engagement**: $200,000 for outreach initiatives and stakeholder collaboration.
- **Miscellaneous**: $150,000 for administrative costs and contingency expenses.

## Expected Outcomes

- **Improved Flood Management**: The PaidX project will deliver actionable insights to enhance preparedness and response to flooding events, ultimately reducing economic losses.
- **Increased Economic Resilience**: By minimizing the economic impacts of flooding, we aim to support the sustainable development of affected communities and strengthen local economies.
- **Data-Driven Policy Recommendations**: Generate evidence-based recommendations for local and regional authorities on effective water resource management and disaster response strategies.

## Conclusion

The PaidX project aligns seamlessly with the mission of the SingularityNET Foundation to promote decentralized and beneficial AGI. By advancing our predictive modeling and decision support systems, we can significantly contribute to addressing pressing environmental challenges while enhancing community resilience in Nigeria. We are eager to collaborate with the SingularityNET Foundation and drive this innovative research forward for the benefit of vulnerable populations. Thank you for considering our proposal.

Proposal Video

Not Avaliable Yet

Check back later during the Feedback & Selection period for the RFP that is proposal is applied to.

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Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

1.0

  • Feasibility 1.7
  • Desirabilty 1.7
  • Usefulness 1.0
  • Expert Review 1

    Overall

    1.0

    • Compliance with RFP requirements 1.0
    • Solution details and team expertise 3.0
    • Value for money 1.0
    This doesn't really address what the RFP is supposed to be about

    I would like to support some version of this project, but it doesn't really slot into these Hyperon RFPs. Maybe if we do a DF round for social benefit oriented AI it would fit. My question would be if there is actually enough data to support application of AI to this particular problem? Would be interested to discuss w/ the proposer but it's outside the context of this particular RFP process.

  • Expert Review 2

    Overall

    1.0

    • Compliance with RFP requirements 3.0
    • Solution details and team expertise 1.0
    • Value for money 1.0
    Not really on topic

    There is a practical application (producing predictive models of water releases and flooding events), which is good. I don't personally see how LLM would be useful in this application, though I certainly suppose it could. The authors made no effort to explain how LLMs would interact with evolutionary programming, in general or in particular for that application. I am confused by the Budget section which exceeds the funding request of $75K.

  • Expert Review 3

    Overall

    1.0

    • Compliance with RFP requirements 1.0
    • Solution details and team expertise 1.0
    • Value for money 1.0
    RFP mismatch

    While an interesting and important project, it is not targeted at the specific nature of the RFP. It also has no milestones, no budget, and no team information.

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