Long-Term Memory for Agent Using Knowledge Graphs

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Expert Review🌟
Eyasu Shiferaw
Project Owner

Long-Term Memory for Agent Using Knowledge Graphs

Funding Requested

$80,000 USD

Expert Review
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Overview

This project aims to enhance agents by incorporating long-term memory using knowledge graphs. A robust knowledge graph will enable the persistent storage & retrieval of information over extended periods. Rigorous experimentation with technologies like Atomspace, Neo4j & other knowledge graph frameworks will assess their effectiveness in providing durable memory for LLM agents. The goal is to address current limitations in retaining and accessing information long-term, enabling agents to maintain context and leverage acquired knowledge more effectively. Successful integration could have significant implications for developing more intelligent and capable language models across various domains

Proposal Description

How Our Project Will Contribute To The Growth Of The Decentralized AI Platform

Our project integrates a long-term memory system using knowledge graphs, enhancing the AI platform's capabilities. This improves the LLM agent's response accuracy, user satisfaction, and overall performance, making the AI platform more reliable and efficient. Additionally, leveraging Atomspace will attract more developers by showcasing its robust framework for knowledge management, fostering growth and innovation in the SingularityNET ecosystem.

Our Team

Our team is well-suited for this project due to our deep understanding of the SNET ecosystem and extensive expertise in AI, LLM, deep learning, AWS, blockchain & APIs. With team members actively involved in SingularityNET projects, we possess insider knowledge and insights crucial for success. Additionally, our partnership with MLOps Community & Microsoft Startup Founder Hub, along with proven record of delivering successful, further demonstrates our capabilities and commitment to innovation.

View Team

AI services (New or Existing)

Long-Term Memory Integration for LLM Agents

Type

New AI service

Purpose

The purpose of this AI service is to enhance the capabilities of LLM agents by integrating a long-term memory system using knowledge graphs. This will enable the agents to retain and access information over extended periods improving their accuracy and utility across various applications.

AI inputs

The input includes queries user interactions and new information that needs to be stored in the knowledge graph.

AI outputs

The output consists of relevant accurate responses generated by the LLM agent based on the information retrieved from the knowledge graph as well as updates to the knowledge graph with new data.

The core problem we are aiming to solve

The core problem our project addresses is the limited memory capacity of AI systems, which hinders their ability to retain and utilize information over time. By incorporating a long-term memory system using knowledge graphs, we aim to overcome this limitation. This enhancement will allow AI agents to access and leverage stored information effectively, improving their performance and utility across various tasks and applications.

 

Our specific solution to this problem

Our specific solution involves integrating a long-term memory system into AI models through the use of knowledge graphs. These knowledge graphs act as structured databases, allowing AI agents to store and retrieve information over extended periods. By organizing data into interconnected nodes and edges, the system enables the AI to establish meaningful relationships between pieces of information, facilitating efficient retrieval and utilization. Through continuous learning and updating, the AI model can enhance its knowledge base, adapting to evolving contexts and user needs.

Additionally, our solution incorporates advanced retrieval techniques, such as graph-based algorithms and contextual embeddings, to optimize information retrieval and ensure accuracy.

Overall, this approach empowers AI systems to overcome memory limitations, enabling them to deliver more contextually relevant and insightful responses across a wide range of tasks and domains

Project details

Our project aims to revolutionize AI systems by introducing a long-term memory solution using knowledge graphs. These graphs serve as structured repositories, facilitating the retention and retrieval of information over prolonged periods. By leveraging interconnected nodes and edges, our approach enables AI models to establish meaningful associations between data points, thereby enhancing their ability to provide contextually relevant responses. Through continuous learning and optimization, our system adapts to changing contexts and user requirements, ensuring sustained performance and utility. Additionally, advanced retrieval techniques optimize information access, enhancing accuracy and efficiency.

Project Objectives

  1. Develop Long-Term Memory: Integrate a knowledge graph to provide the LLM agent with long-term memory capabilities.

  2. Experiment with Knowledge Graphs: Evaluate different structures and technologies to find the most effective solution.

  3. Enhance Information Retrieval: Improve the agent’s ability to access and utilize stored information efficiently.

  4. Ensure Persistence and Scalability: Implement a scalable solution that maintains memory persistence over time.

Core Features

  1. Persistent Storage: Utilize knowledge graphs to store information that the LLM agent can retrieve over extended periods.

  2. Enhanced Retrieval: Implement advanced retrieval methods to allow the agent to access relevant information quickly and accurately.

  3. Continuous Learning: Enable the LLM agent to update its knowledge graph with new information continuously.

  4. Scalability: Design a scalable architecture to handle growing amounts of data without compromising performance.

  5. Security: Ensure robust security measures to protect stored information.

Key Performance Indicators (KPIs)

  1. Memory Retention Rate: Measure the accuracy and relevance of information retrieved from the knowledge graph.

  2. Response Time: Track the average time taken by the LLM agent to retrieve information.

  3. User Satisfaction: Collect feedback from users regarding the effectiveness and utility of the long-term memory system.

  4. System Scalability: Monitor the system’s performance as the volume of stored data increases.

Pilot Testing

  1. Beta Testing Group: Conduct initial testing with a diverse group of users.

  2. Feedback Collection: Gather detailed feedback on usability and performance.

  3. Iterative Improvements: Refine the system based on feedback and performance data.

Qualitative and Quantitative Metrics

  1. Qualitative Metrics:

    • User feedback on ease of use and satisfaction.

    • Case studies and testimonials detailing specific user experiences.

  2. Quantitative Metrics:

    • Interaction volume and accuracy rate.

    • Usage frequency and system downtime.

Overall, our project represents a significant advancement in AI technology, promising to unlock new capabilities and applications across various domains.



Competition and USPs

Our solution distinguishes itself by its comprehensive approach to long-term memory integration in AI systems. Unlike other solutions that may focus solely on specific memory mechanisms or retrieval techniques, our project encompasses a holistic framework that incorporates knowledge graphs, advanced retrieval algorithms, and continuous learning mechanisms. This comprehensive approach ensures not only efficient information storage and retrieval but also adaptability to evolving contexts and user needs.

Moreover, our solution's scalability and versatility make it well-suited for a wide range of applications and domains. Whether deployed in conversational AI, data analysis, or personalized recommendations, our system can seamlessly adapt to different use cases and environments, thereby maximizing its market potential.

Open Source Licensing

MIT - Massachusetts Institute of Technology License

The project outputs are open source, licensed under the MIT license. The MIT license is a permissive free software license that allows for commercial use, modification, distribution, and private use, with the only requirement being that the copyright and license notices must be included in all copies or substantial portions of the software.

All elements of the code will be open source under the MIT license. There are no portions that will remain closed-source.

Proposal Video

DF Spotlight Day - DFR4 - Eyasu Shiferaw - Long Term Memory For Agent Using Knowledge Graphs

4 June 2024
  • Total Milestones

    6

  • Total Budget

    $80,000 USD

  • Last Updated

    4 Jun 2024

Milestone 1 - API Calls & Hostings

Description

This milestone represents the required reservation of 25% of your total requested budget for API calls or hosting costs. Because it is required we have prefilled it for you and it cannot be removed or adapted.

Deliverables

You can use this amount for payment of API calls on our platform. Use it to call other services or use it as a marketing instrument to have other parties try out your service. Alternatively you can use it to pay for hosting and computing costs.

Budget

$20,000 USD

Milestone 2 - Knowledge Graph API Design and Development

Description

Develop a comprehensive API features and Documentation for the knowledge graph that includes CRUD operations (Create Read Update Delete) along with detailed documentation requirements analysis and design specifications. This API will serve as the interface for interacting with the knowledge graph enabling seamless data storage and retrieval.

Deliverables

1. API Documentation and Development: API for the knowledge graph with necessary endpoints and functionality developments. 2. Documentation: Detailed API documentation including usage instructions endpoint descriptions example requests and responses requirements analysis and design specifications. 3. Security Implementation: Security measures to ensure safe and secure API access such as authentication and authorization mechanism preparations

Budget

$10,000 USD

Milestone 3 - Create an Agent Based on LLM

Description

Develop an intelligent agent based on a Large Language Model (LLM) that can process and generate human-like text. The agent will be designed to leverage the capabilities of the LLM for various tasks including natural language understanding and generation.

Deliverables

1. LLM Agent Development: Implementation of the LLM-based agent with core functionalities. 2. Feature Implementation: Key features such as context understanding dialogue management and response generation. 3. Documentation: Comprehensive documentation detailing the agent’s functionalities usage guidelines and integration points.

Budget

$15,000 USD

Milestone 4 - Integrate the Knowledge Graph and LLM with APIs

Description

Integrate the developed knowledge graph API with the LLM-based agent enabling the agent to access and utilize long-term memory stored in the knowledge graph. This integration will enhance the agent’s ability to retain and retrieve information over extended periods.

Deliverables

1. Integration Implementation: Seamless integration of the knowledge graph with the LLM agent. 2. Fully Functional API: development of fully functional API endpoint developments 3.Functionality Testing: Initial testing to ensure the integrated system operates as expected. 4.Integration Documentation: Detailed documentation covering the integration process including how the LLM agent interacts with the knowledge graph.

Budget

$20,000 USD

Milestone 5 - Testing and Performance Optimization

Description

Conduct thorough testing of the integrated system to validate its performance reliability and scalability. This includes functional testing performance testing and user acceptance testing to ensure the system meets all requirements and performs well under various conditions. Additionally optimize system performance based on testing results.

Deliverables

1. Test Plans and Cases: Comprehensive test plans and test cases covering all aspects of the system. 2. Testing Execution: Execution of all planned tests including functional performance and user acceptance tests. 3. Optimization Recommendations: Recommendations for performance optimization based on test results

Budget

$10,000 USD

Milestone 6 - Final Report and Roadmap

Description

Prepare a final report summarizing the project including achievements challenges and lessons learned. Additionally develop a roadmap for future enhancements and potential next steps for the project.

Deliverables

1.Final Project Report: Comprehensive report detailing the project’s journey outcomes and key takeaways. 2.Future Roadmap: Strategic roadmap outlining potential future enhancements next steps and long-term goals. 3.Presentation Materials: Presentation slides and materials summarizing the project for stakeholders and potential investors.

Budget

$5,000 USD

Join the Discussion (8)

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8 Comments
  • 0
    commentator-avatar
    Emotublockchain
    May 29, 2024 | 8:28 PM

    I will appreciate your response 

    • 0
      commentator-avatar
      Eyasu Shiferaw
      May 30, 2024 | 2:34 PM

      Thank you for reaching out. I appreciate your patience and am happy to assist with any further questions or concerns you may have.

  • 0
    commentator-avatar
    Emotublockchain
    May 29, 2024 | 8:27 PM

    And Why is the budget so much $80k ?

    • 0
      commentator-avatar
      Eyasu Shiferaw
      May 30, 2024 | 2:33 PM

      It will take us 6-7 months to complete, as we have costs for service, LLM APIs, and generating a synthetic dataset to fine-tune the LLM model if it shows better results. Additionally, we need to cover the salaries for the developers.

  • 0
    commentator-avatar
    Emotublockchain
    May 29, 2024 | 8:27 PM

    So it is an MVP or it's in very early stage?

  • 0
    commentator-avatar
    Emotublockchain
    May 29, 2024 | 8:26 PM

    Interesting 🤔 .. love the concept "memory graph" sounds interesting 

Expert Review

Overall

4

user-icon
  • Feasibility 4
  • Viability 3
  • Desirabilty 5
  • Usefulness 4
Highly desirable to overcome limited AI memory.

Feasibility: This had comments about human short and long-term memory. The feasibility was rated high with clear KPIs and metrics.

Viability: There was concern about the Knowledge Graphs and time for development. While AI was mentioned there was concern over the lack of discussion and completion targets.

Desirability: It was assessed as highly desirable to overcome limited memory in AI models.

Usefulness: With an open source in the AI ecos the Usefulness was rated high.

Sort by

17 ratings
  • 0
    user-icon
    Onize Olie
    Jun 10, 2024 | 7:36 AM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 5
    • Usefulness 5
    Robust Long-Term Memory Integration for AI

    This project stands out due to its innovative approach to addressing the significant challenge of limited memory capacity in AI systems through the integration of knowledge graphs. The feasibility is enhanced by the use of established technologies such as Atomspace and Neo4j, which provide a robust framework for knowledge management. The project's viability is further supported by its potential to significantly improve AI performance and user satisfaction by enabling long-term information retention and retrieval. The well-qualified team, with deep expertise in AI, blockchain, and cloud services, bolstered by strong industry partnerships, underscores the project's capability to deliver on its ambitious goals. Moreover, the focus on scalability and continuous learning ensures that the solution can adapt and grow with evolving data and user needs, making it a highly promising venture for the future of AI development.

  • 0
    user-icon
    BlackCoffee
    Jun 10, 2024 | 12:20 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Record information retrieval

    The product's biggest effect is to help retrieve information, thereby improving the operator's accuracy. From there, it attracts developers. That is, effects come with effects. I am pleased that the team has analyzed this factor and considered it a unique and noteworthy point. Of course, this proposal has other unique points related to Viability, but that's enough for me to give a relatively good rating to the entire proposal.

  • 0
    user-icon
    TrucTrixie
    Jun 9, 2024 | 1:27 PM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 3
    • Usefulness 4
    The downside of integrated technology

    The question I have not been able to answer myself is whether updating information will encounter any obstacles when the knowledge graph has been integrated into the AI ​​system. Actually, from my perspective, there is always a downside to technology integration - and this is an example. Otherwise I am satisfied with this proposal.

  • 0
    user-icon
    markilan
    Jun 9, 2024 | 9:28 AM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 5
    • Usefulness 5
    Long term memory for agent

    well as i read from the proposal this project have a promissing value for the ecosystem and the tam capability is high plus there past expriance and ther knowledge with the singularitynet make this project feasibility high and also the the ecosystem that will benefit from this project is high i believe, so i support this propsal with a good viability, desirablility, feasibility and usefulness.

  • 0
    user-icon
    ZeroTwo
    Jun 8, 2024 | 10:40 AM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 4
    • Usefulness 4
    Long-Term Memory For Agent Using Knowledge Graphs

    This proposal has several prominent strengths and positive aspects. The clear goal of improving the long-term memory of AI agents using knowledge graphs is highly relevant and innovative. An experienced team with a deep understanding of the SNET ecosystem and extensive expertise in AI, LLM and other related technologies demonstrated strong capabilities to execute this project. A holistic approach that includes long-term memory integration, enhanced information retrieval, continuous learning, and scalability demonstrates a commitment to a comprehensive solution. Partnerships with the MLOps community and Microsoft Startup Founder Hub, as well as the MIT open source license, strengthen the project's potential to have a significant positive impact.

    However, I think there are some weaknesses that need to be noted and addressed. Although the technical description is strong, the proposal is slightly lacking in specific implementation details and experiment methodology. The risks and challenges that may be faced have not been discussed in detail. To overcome this weakness, it is a good idea to add a more detailed implementation plan, including specific steps and timelines, identifying key risks and mitigation plans. Additionally, more details regarding pilot trials, including feedback collection methods and clear success metrics, are also needed.

  • 0
    user-icon
    Max1524
    Jun 8, 2024 | 8:25 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Explain technical factors clearly

    Overall, the proposal is an easy-to-understand description of the topic of long-term memory for agents using knowledge graphs. I like the team's expression. And I want to emphasize that the team needs to explain more with milestone number 4 which is Integrating Knowledge Graph and LLM with API. This is a very technical milestone, if not for readers with enough majors, it will be difficult to understand. And when the common ground is difficult for readers to understand, the feasibility will not be appreciated by the majority.

  • 0
    user-icon
    Nicolad2008
    Jun 8, 2024 | 3:32 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Synchronization and application

    I realize that the use of knowledge diagrams helps store and retrieve sustainable information, improve the accuracy and effectiveness of the operator in maintaining context and take advantage of the learned knowledge. This project is also beneficial for the development of SingularityNet by attracting developers through the introduction of strong Atomspace technology frames. However, I also noticed some significant challenges. The integration of knowledge diagrams into the AI ​​system requires high technology and may have difficulty synchronizing and continuous information updates. Besides, ensuring data security and privacy of users is an important issue that needs to be carefully considered. However, with a team of experienced and extensive knowledge about the Snet ecosystem and related fields, I believe that the project is likely to achieve success and bring practical value to users.

  • 0
    user-icon
    Christian
    May 28, 2024 | 4:00 PM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 5
    • Usefulness 5
    Improving AI Capabilities with Long-Term Memory.

    Overall Rating

    This proposal presents a comprehensive and innovative approach to enhancing AI systems through the integration of long-term memory using knowledge graphs. The project addresses a critical limitation in current AI models and offers a holistic solution that promises significant advancements in performance and utility. The proposal is well-structured, detailed, and backed by a competent team with relevant expertise. It aligns closely with the goals of the decentralized AI platform and demonstrates a clear path to success.

    Feasibility

    The project's realistic objectives, detailed plan, and team's expertise in deep learning, knowledge graph, and artificial intelligence technologies all attest to its feasibility. The recommended benchmarks are achievable and unambiguously expressed, emphasising extensive testing and optimisation. Using well-known frameworks like Atomspace and Neo4j, which utilise pre-existing tools and resources, increases the project's feasibility.

    Viability

    The project's strong connection with the objectives of the decentralised AI platform contributes to its viability. The initiative directly contributes to the growth and innovation of the platform by improving AI capabilities through long-term memory integration. The team's credibility and prospects for success are further enhanced by its connection with Microsoft Startup Founder Hub and MLOps Community. The project's viability across multiple domains and applications is ensured by its complete approach to memory integration and its emphasis on scalability and versatility.

    Desirability

    The approach tackles the shortcomings of short-term memory in existing models, filling a major gap in the field of AI research. Knowledge graph-based integration of long-term memory holds the potential to open up new possibilities and applications in a variety of fields. The initiative raises user satisfaction, response accuracy, and overall performance by strengthening AI agents' capacity to store and retrieve information over long periods of time. Its emphasis on adaptability and scalability, which accommodate a variety of use cases and contexts, further increases its attractiveness.

    Usefulness

    The approach tackles the shortcomings of short-term memory in existing models, filling a major gap in the field of AI research. Knowledge graph-based integration of long-term memory holds the potential to open up new possibilities and applications in a variety of fields. The initiative raises user satisfaction, response accuracy, and overall performance by strengthening AI agents' capacity to store and retrieve information over long periods of time. Its emphasis on adaptability and scalability, which accommodate a variety of use cases and contexts, further increases its attractiveness.

    user-icon
    Eyasu Shiferaw
    May 29, 2024 | 1:38 PM
    Project Owner



    Thank you for your detailed and positive feedback on our proposal, "Long-Term Memory For Agent Using Knowledge Graphs " We appreciate your recognition of its innovation and potential.

    We're pleased that our comprehensive approach and team expertise are acknowledged. Addressing AI's current limitations with long-term memory integration promises significant advancements.

  • 0
    user-icon
    Ese Williams
    May 26, 2024 | 8:57 PM

    Overall

    5

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Long-Term Memory For Agent Using Knowledge Graphs

    I think this initiative would be super impactful and help with creating more useful tools that would be more relevant to the general AI platform.

     

    Feasibility: Great feasibility, the team has ample experience in executing this proposal successfully. 

    Viability: This project proposal outlines the success of this project and it's efficiency and how clearly the budget would be used thus. 

     

    Desirability: This is a tool that would be desirable to developers because it is something that would enable more models to become more effective. Thus might expand into something super bigger

     

    Usefulness: It clearly outlines its contribution to the SingularityNet and it aligns to SingularityNet vision and Mission. 

    user-icon
    Eyasu Shiferaw
    May 29, 2024 | 1:40 PM
    Project Owner

    Thank you for your positive feedback on our proposal, "Long-Term Memory For Agent Using Knowledge Graphs." We appreciate your confidence in our team's experience and the project's feasibility. Your recognition of our clear project outline, efficient budget use, and the value this tool brings to developers is encouraging. We're excited about the potential for significant growth and are pleased that our project aligns well with SingularityNet’s vision and mission. Thank you again for your support! 

  • 0
    user-icon
    Tarran
    May 25, 2024 | 10:42 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 3
    • Usefulness 4
    a promising solution for a critical challenge

    This proposal tackles a critical challenge in AI development – the lack of long-term memory in AI agents. By leveraging knowledge graphs, the project outlines a compelling strategy to equip agents with this crucial capability. This has the potential to significantly advance the SingularityNET platform.

    Strengths of the Proposal:

    • Feasibility: The proposal demonstrates strong feasibility with a well-structured development approach, clear milestones, and a team with expertise in AI, deep learning, and blockchain technology. Additionally, the allocated budget ensures adequate resources for implementation.

    • Viability: The project offers clear viability through its potential to improve AI agent capabilities, leading to increased response accuracy and customer satisfaction. The combination of knowledge graphs and sophisticated retrieval algorithms ensures scalability and flexibility across various domains. The team's partnerships and past performance further strengthen the project's viability.

    • Desirability: This approach addresses a major requirement in AI development: the ability of agents to retain and utilize knowledge over time. This strategy has the potential to significantly enhance agent performance and utility, making the project highly desirable for both developers and consumers.

    • Usefulness: The project unlocks new capabilities for AI systems, enabling them to deliver more contextually relevant and insightful responses. By integrating long-term memory, agents can adapt to evolving contexts and user needs, leading to a more impactful and revolutionary approach to AI technology on the SingularityNET platform.

    Overall:

    This proposal offers a promising solution for a critical challenge in AI development. The clear focus on feasibility, viability, desirability, and usefulness strengthens the case for this project. It has the potential to be a significant advancement for SingularityNET and the field of AI as a whole.

    user-icon
    Eyasu Shiferaw
    May 26, 2024 | 8:47 AM
    Project Owner

    Thank you for recognizing our proposal's feasibility, viability, desirability, and usefulness. Your feedback strengthens our resolve to tackle AI's critical challenges effectively. 

  • 0
    user-icon
    Ayo OluAyoola
    May 24, 2024 | 9:30 AM

    Overall

    4

    • Feasibility 3
    • Viability 3
    • Desirabilty 4
    • Usefulness 4
    Long-Term AI Memory using Knowledge Graphs

    Desirability
    Integrating long-term memory into AI agents is desirable. Enhancing AI's ability to retain and utilize information over extended periods is crucial for more sophisticated applications. This capability would make AI more intuitive and responsive, improving user experiences.

     

    Usefulness
    The solution is handy as it addresses a critical limitation in current AI models—short-term memory. By incorporating long-term memory, AI agents can maintain context and build upon previously acquired knowledge, leading to more coherent and context-aware responses. This usefulness spans multiple industries, from customer service to advanced research applications.

     

    Feasibility:
    The feasibility of this project depends on the effectiveness of knowledge graph technologies like Atomspace and Neo4j in providing durable memory. While these technologies are promising, rigorous experimentation is needed to confirm their suitability. Challenges may include the complexity of integrating these technologies and ensuring they scale effectively with the growing data.

    Viability
    The viability of this project hinges on the successful integration and performance of knowledge graphs in real-world applications. And thus very experimentation intensive.

    I hope the technical and practical challenges can be overcome; the project has the potential to be a game-changer. 

    Summary-in-Review

    This project presents a compelling and innovative solution with the potential for significant advancements in AI capabilities. While there are challenges related to feasibility and viability, the high desirability and usefulness make it a promising endeavour.

    user-icon
    Eyasu Shiferaw
    May 26, 2024 | 8:49 AM
    Project Owner

    Thank you for your insightful feedback. We appreciate your recognition of the desirability and usefulness of our project in enhancing AI capabilities. We are committed to addressing the feasibility and viability challenges to realize its potential impact. 

  • 0
    user-icon
    Joseph Gastoni
    May 23, 2024 | 4:45 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    proposal outlines a project to enhance AI agents

    This proposal outlines a project to enhance AI agents with long-term memory using knowledge graphs. Here's a breakdown of its strengths and weaknesses:

    Feasibility:

    • Moderate-High: The project leverages existing technologies (knowledge graphs) but requires experimentation to identify the most effective approach for LLMs.
      • Strengths: The proposal builds upon established knowledge graph technologies for information storage and retrieval.
      • Weaknesses: The proposal lacks details on the complexity of integrating knowledge graphs with LLMs and the resources required for experimentation.

    Viability:

    • Moderate: Success depends on successful integration, user adoption, and potential commercial applications.
      • Strengths: The proposal addresses a significant limitation of LLMs and has the potential to improve their capabilities.
      • Weaknesses: The proposal needs a clearer strategy for demonstrating the commercial value of the solution and attracting users or developers.

    Desirability:

    • High (for a specific audience): For developers and researchers interested in improving LLM capabilities, this could be highly desirable.
      • Strengths: The proposal caters to a specific need within the AI research community.
      • Weaknesses: The proposal needs to demonstrate the broader benefits beyond research and explore potential user applications.

    Usefulness:

    • High Potential: The project has the potential to significantly improve LLM performance, but hinges on successful integration, demonstration of practical use cases, and user adoption.
      • Strengths: The proposal offers a framework for enhancing LLM capabilities with long-term memory.
      • Weaknesses: The proposal lacks details on how the improved LLMs will be integrated into existing applications or workflows.

    Overall, the proposal has a valuable goal, but focus on:

    • Technical Details: Provide more details on the planned experimentation process for integrating knowledge graphs with LLMs.
    • Commercial Viability: Develop a clearer strategy for demonstrating the commercial value of the improved LLMs and identifying potential target markets or applications.
    • User Adoption: Outline a plan for engaging potential users or developers and demonstrating the practical benefits of the enhanced LLMs.

    Strengths:

    • Addresses a significant limitation of current LLMs (limited memory).
    • Leverages established knowledge graph technologies for information storage and retrieval.
    • Focuses on a comprehensive approach including information retrieval and continuous learning.

    Weaknesses:

    • Lacks details on the technical complexity of integration and experimentation costs.
    • Needs a stronger focus on commercial viability and user adoption strategy.

    By addressing these considerations, the Long-Term Memory for LLM Agents project can be strengthened and increase its chances of creating a more capable and user-centric LLM technology.

  • 0
    user-icon
    Nicolas Rodriguez
    May 22, 2024 | 8:09 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    Empowering AI

    Dear Eyasu.

    I've thoroughly reviewed your project proposal for enhancing AI agents with long-term memory using knowledge graphs. It's an ambitious and promising endeavor, and I commend your team for tackling such a significant challenge in the field of AI.

    Feasibility

    Your project appears highly feasible. The proposed use of knowledge graphs as a long-term memory solution is a well-established concept with proven success in various applications. Your team's experience and the availability of existing knowledge graph frameworks like Atomspace and Neo4j further strengthen the feasibility of your approach.

    Viability

    The viability of your project is also strong. There is a clear demand for AI systems with improved memory and contextual understanding. By addressing this limitation, your project has the potential to significantly enhance the capabilities of SingularityNET's AI platform, making it more attractive to developers and users alike.

    Desirability

    Your project is highly desirable. The ability to provide AI agents with long-term memory is a much-sought-after feature in the field of AI. The improved context awareness, personalization, and decision-making capabilities that your solution promises will be highly appealing to a wide range of users and industries.

    Usefulness

    The usefulness of your project is undeniable. The applications for AI agents with long-term memory are vast and span across various domains, from customer service and healthcare to education and research. By enabling AI agents to learn from past experiences and retain information over extended periods, your solution has the potential to unlock new possibilities and create significant value.

    In summary, your project is highly feasible, viable, desirable, and useful. It holds immense potential to transform the capabilities of AI agents and contribute to the growth and success of the SingularityNET ecosystem. I encourage you to pursue this project with enthusiasm and confidence, as it addresses a critical need in the AI landscape.

    I am the designer of Transcendence Platform. We made a proposal for AI-enabled holograms, aiming to create a holographic virtual assistant for various applications, such as customer care, emotional support, advertising, and entertainment. By combining advanced AI with holographic technology, we enable natural and empathetic interactions that enhance the user experience. I invite you to watch it and give me your opinion. Thank you very much. Check it out here: https://deepfunding.ai/proposal/revolutionizing-assistance-3d-holographic-ai/

  • 0
    user-icon
    Tu Nguyen
    May 22, 2024 | 9:12 AM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 4
    • Usefulness 5
    Long-Term Memory For Agent Using Knowledge Graphs

    This proposal addresses the problem of limited capacity of AI systems, which hinders their ability to store and use information over time. This proposal's solution involves integrating long-term memory systems into AI models through the use of knowledge graphs. This proposal is in line with Pool New Projects. The information they give is very detailed, clearly showing the feasibility of the project. 
    This project aims to revolutionize AI systems by introducing a long-term memory solution using knowledge graphs. These charts serve as structured storage, facilitating long-term retention and retrieval of information. By leveraging interconnected nodes and edges, their approach enables AI models to establish meaningful associations between data points, thereby enhancing their ability to provide feedback. contextually appropriate.
    I have a few more comments for this proposal: they should share more details about the proposal's budget based on milestones. Additionally, they should identify the start and end times of each milestone.

  • 0
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    salim_vitalik
    May 21, 2024 | 12:46 PM

    Overall

    5

    • Feasibility 5
    • Viability 4
    • Desirabilty 5
    • Usefulness 5
    enhance AI agent capabilities

    i belive this project is effectively addressing the challenge of information retention. The comprehensive approach, including experimentation with various technologies and clear milestones, demonstrates a solid conceptual framework. The budget allocations are sensible, and the inclusion of metrics and KPIs to measure impact is impressive. Achieving the proposed goals will significantly enhance AI agent capabilities, making them more intelligent and versatile across various applications. The team's expertise and track record further reinforce the project's feasibility and potential for significant impact.

  • 0
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    pindiyaa
    May 21, 2024 | 12:42 PM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 5
    • Usefulness 5
    LTM for AI agent

    This LTM for Agent project propsal offers a well-defined plan to enhance AI agents with long-term memory using knowledge graphs, addressing the critical limitation of information retention. the structured approach  including experimentation with various technologies and detailed milestones, shows a strong conceptual framework. the budget allocations are reasonable and the integration of metrics and KPIs to measure impact is commendable. with the voters power If successful this project will significantly improve the capabilities of AI agents, making them more intelligent and useful across various applications. The team's expertise and past successes further support the project's feasibility and potential for substantial impact.
    this is what SNET and other ai system needs right now.

  • 0
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    Devbasrahtop
    May 20, 2024 | 11:32 AM

    Overall

    5

    • Feasibility 5
    • Viability 5
    • Desirabilty 5
    • Usefulness 5
    Enhancing AI Agents with Long-Term Memory

    Overall

    This proposal addresses a crucial gap in AI development by outlining a thorough strategy for utilizing knowledge graphs to include long-term memory into AI agents. The project is well-positioned to significantly advance the decentralized AI platform since it has a capable staff, well-defined goals, and a roadmap.

    Feasibility

    The proposal demonstrates high feasibility, with a structured approach to development and clear milestones. The team's expertise in AI, deep learning, and blockchain technology enhances the project's feasibility. Additionally, the allocated budget for API calls, hosting, and development activities ensures adequate resources for implementation.

    Viability

    The project's potential to improve AI agent capabilities, which would increase response accuracy and customer happiness, makes it clearly viable. Scalability and flexibility across a range of areas are guaranteed by the combination of knowledge graphs and sophisticated retrieval algorithms. The project's viability is further strengthened by the team's partnerships and performance history.

    Desirability

    The approach provides a strategy that can greatly increase agent performance and utility, addressing a critical requirement in AI development. The project increases the relevance and effectiveness of AI agents across a wide range of jobs and applications by enabling them to keep and retrieve knowledge over extended periods. This makes the project extremely attractive for both developers and consumers.

    Usefulness

    The project promises to unlock new capabilities in AI systems, enabling them to deliver more contextually relevant and insightful responses. By integrating long-term memory using knowledge graphs, AI agents can adapt to evolving contexts and user needs, enhancing their overall utility and impact. The project's potential to revolutionize AI technology makes it incredibly useful for advancing the decentralized AI platform.

Summary

Overall Community

4.5

from 17 reviews
  • 5
    8
  • 4
    9
  • 3
    0
  • 2
    0
  • 1
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Feasibility

4.4

from 17 reviews

Viability

4.2

from 17 reviews

Desirabilty

4.3

from 17 reviews

Usefulness

4.5

from 17 reviews

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