Get Paid for Your Training Data

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ivan reznikov
Project Owner

Get Paid for Your Training Data

Funding Requested

$125,000 USD

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Overview

Proposal for a Decentralized Data Contribution Platform for LLM Training In recent years, the development of large language models (LLMs) has advanced rapidly, powered by vast amounts of data. However, the current data collection methods often lack transparency and do not reward contributors. This proposal outlines a decentralized platform where users can upload their data for LLM training and receive compensation if their data is selected, providing more control and rewards for contributors.

Proposal Description

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

Our decentralized data contribution platform will drive the growth of the AI ecosystem by providing a diverse, high-quality, and ethically sourced dataset, enhancing the performance and robustness of large language models (LLMs). By empowering users with control over their data and compensating them fairly, the platform will attract a broad range of contributors, ensuring a rich variety of data.

Our Team

Our teams share the division we have for the data and ethics regarding using it.

Out team consists of 2 data engineers, 1 designer, 1 frontend developer, 1 infra/devops, 1 data scientist and 2 backend developers.

View Team

AI services (New or Existing)

LLM Training

Type

New AI service

Purpose

The purpose of LLMs is massive. The problem comes when you train a base or finetune a model - what data to use.

AI inputs

User request

AI outputs

LLM response

The core problem we are aiming to solve

  1. Empower Data Contributors: Give individuals control over their data and reward them for their contributions.
  2. Enhance Transparency: Ensure transparent data usage and model training processes.
  3. Improve Data Diversity and Quality: Source diverse and high-quality data from a wide range of contributors.

Our specific solution to this problem

We propose a decentralized platform where users can upload their data for use in large language model (LLM) training and receive compensation if their data is selected. This platform leverages blockchain technology to ensure transparency, security, and fair compensation. Users can manage their data permissions, participate in governance through token-based voting, and track the use and impact of their contributions. By democratizing data contribution, we aim to enhance data diversity, improve LLM performance, and empower users with control and rewards for their data.

Project details

Platform Features

1. User Registration and Verification

  • Registration: Users register on the platform using secure authentication methods.
  • Verification: Users verify their identity to ensure data integrity and accountability.

2. Data Upload and Management

  • Data Upload: Users can upload various types of data (text, images, audio, etc.) with metadata descriptions.
  • Data Categorization: Uploaded data is categorized for easy indexing and retrieval.
  • Privacy Controls: Users can set privacy levels and usage permissions for their data.

3. Data Quality Assessment

  • Automated Screening: Initial automated checks for data quality, relevance, and compliance with guidelines.
  • Community Review: Community-driven review process where other users can rate and comment on the data quality.

4. Data Selection and Compensation

  • Selection Process: Data is selected based on quality, relevance, and diversity needs of LLM projects.
  • Compensation Model: Contributors receive compensation if their data is selected for training. Payment structures can be based on the amount of data used and its impact on model performance.

5. Decentralized Governance

  • Governance Tokens: Implement a token system where contributors earn governance tokens, allowing them to participate in platform decision-making.
  • Voting Mechanisms: Contributors can vote on important platform policies, data usage guidelines, and compensation rates.

6. Transparency and Auditing

  • Blockchain Ledger: Use blockchain technology to maintain an immutable record of data contributions, selections, and payments.
  • Auditable Logs: Provide auditable logs of data usage in LLM training processes.

7. Security and Compliance

  • Data Encryption: Ensure all data is encrypted in transit and at rest.
  • Compliance: Adhere to data protection regulations (e.g., GDPR, CCPA) and industry standards.

 

Technical Architecture

  1. Frontend

    • User-friendly interface for data upload, management, and interaction.
    • Dashboard for tracking data contributions, selections, and earnings.
  2. Backend

    • Decentralized storage solutions (e.g., IPFS) for storing user data securely.
    • Smart contracts on a blockchain platform (e.g., Ethereum) to manage data transactions, compensation, and governance.
  3. AI Integration

    • Integrate with LLM training frameworks to facilitate seamless data integration.
    • Implement APIs for data retrieval and usage analytics.

 

Distinctiveness of Our Solution

1. Decentralization and User Control

Most current data collection methods for LLM training are centralized, often controlled by large corporations, which limits user control over their data. Our platform is decentralized, leveraging blockchain technology to provide users with full control over their data, including permissions and privacy settings. This decentralized approach ensures that data contributors are directly involved in the governance and decision-making processes.

2. Transparent and Fair Compensation

Unlike traditional data collection methods where contributors often receive little to no compensation, our platform implements a transparent compensation model. Contributors are rewarded based on the quality, relevance, and impact of their data on LLM training. The use of blockchain ensures that all transactions are transparent and immutable, building trust among users.

3. Community-Driven Quality Assessment

Our platform integrates a community review process for assessing data quality, which helps in maintaining high standards and relevance. This community-driven approach encourages active participation and fosters a sense of ownership among contributors, distinguishing our solution from more opaque, top-down quality assessment methods used by other platforms.

4. Governance Tokens and Voting Mechanisms

By introducing governance tokens, our platform allows contributors to have a say in important decisions, such as data usage policies and compensation rates. This participatory governance model is unique and empowers users to influence the platform's direction, enhancing user engagement and satisfaction.

5. Enhanced Security and Compliance

Our solution prioritizes security and compliance, using advanced encryption techniques and adhering to data protection regulations like GDPR and CCPA. This focus on security and regulatory compliance sets our platform apart from others that may not prioritize these aspects, thereby attracting users who are concerned about data privacy and legal issues

Competition and USPs

1. Growing Demand for Ethical Data Sourcing

There is a growing demand for ethical data sourcing practices in the AI industry. Our platform's transparent and fair compensation model meets this demand, attracting contributors who are looking for ethical ways to monetize their data.

2. Increased Awareness of Data Privacy

With rising awareness of data privacy issues, users are becoming more cautious about how their data is used. Our platform's emphasis on user control and data security addresses these concerns, making it an attractive option for privacy-conscious individuals.

3. Collaborative and Participatory Ecosystem

4. Technological Advancements

Leveraging the latest blockchain and AI technologies positions our platform as a cutting-edge solution in the market. This technological edge can attract early adopters and tech-savvy users, helping us gain a foothold in the competitive landscape.

5. Strategic Partnerships

Proposal Video

Placeholder for Spotlight Day Pitch-presentations. Video's will be added by the DF team when available.

  • Total Milestones

    4

  • Total Budget

    $125,000 USD

  • Last Updated

    18 May 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

$31,250 USD

Milestone 2 - Platform Development

Description

This phase focuses on establishing the foundational elements of the platform including user registration data upload capabilities and initial community engagement features. The goal is to build a secure user-friendly interface that allows contributors to upload and manage their data efficiently.

Deliverables

User Registration and Verification System: - Develop a secure user authentication system. - Implement identity verification to ensure data integrity. Data Upload and Management Interface: - Create an intuitive interface for users to upload various types of data (text images audio). - Implement data categorization and tagging features for easy indexing and retrieval. Privacy Controls and Permissions: - Allow users to set privacy levels and usage permissions for their data. - Ensure compliance with data protection regulations (e.g. GDPR CCPA). Automated Data Quality Screening: - Develop initial automated checks for data quality relevance and compliance. - Integrate basic community review mechanisms for user feedback on data quality. Basic Compensation Model: - Establish a preliminary compensation framework for data contributions. - Develop a payment system to reward users for selected data.

Budget

$40,000 USD

Milestone 3 - Decentralized Governance

Description

This phase introduces decentralized governance features allowing users to participate in platform decision-making through governance tokens. It also enhances the compensation model and integrates the platform with various LLM projects.

Deliverables

Governance Token System: - Implement a token system where users earn tokens for data contributions and platform activities. - Develop voting mechanisms for token holders to participate in decision-making. Enhanced Compensation Models: - Refine the compensation framework based on user feedback and initial data usage. - Introduce tiered rewards based on data quality relevance and impact on LLM performance. Integration with LLM Projects: - Establish partnerships with AI research institutions and technology companies. - Integrate APIs for seamless data retrieval and usage analytics in LLM training. Community Engagement and Support: - Launch community forums and support channels to facilitate user interaction and feedback. - Organize webinars and tutorials to educate users about platform features and governance participation.

Budget

$33,750 USD

Milestone 4 - Full Transparency and Security

Description

This phase focuses on achieving full transparency and enhancing security measures. Blockchain technology will be integrated to maintain an immutable record of data contributions selections and payments. Additionally this phase ensures full compliance with data protection regulations and establishes robust auditing mechanisms.

Deliverables

Blockchain Integration: - Implement blockchain technology to record data contributions and transactions. - Ensure all data interactions are transparent and immutable. Advanced Security Features: - Enhance data encryption protocols for secure storage and transmission. - Conduct regular security audits and vulnerability assessments. Regulatory Compliance: - Ensure full compliance with GDPR CCPA and other relevant data protection regulations. - Develop a comprehensive data protection impact assessment (DPIA) framework. Auditing and Transparency Tools: - Provide users with access to auditable logs of data usage in LLM training processes. - Develop transparency dashboards to display data contribution metrics and compensation details. Expansion and Scalability: - Scale the platform to handle increased data volume and user base. - Optimize infrastructure for performance and reliability.

Budget

$20,000 USD

Join the Discussion (3)

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3 Comments
  • 0
    commentator-avatar
    HenriqC
    Jun 8, 2024 | 4:09 PM

    I may be talking slightly past this proposal. Nevertheless, I’ll share my short view of the future around this topic. Any time when one puts any information on the internet it will (have an opportunity to) include certain metadata that specifies the identity (or should I say agency) behind the contribution. Whenever the AI creates value that is based on a certain piece of training data, the agency behind that valuable action includes the identities who created that piece of training data. Surely there are tradeoffs in any design choices but in a grand scheme of things the AIs understanding of its own cognition and epistemology is not about morality but about intelligence.      When we talk about data that is to be kept very strictly private, it won’t be uploaded anywhere but stays locally in the owner’s control. The technology of state proofs evolves rapidly. If I now think about this proposed service it sounds basically like a storage solution with optionality to grant different levels of access to different pieces of data (which is highly valuable in itself). But as stated, for many types of data the content is not allowed to leave the user’s local storage. That is to be analyzed (and the value traded) by using private smart contracts or some corresponding emerging technology. My view is that the science and tooling here are in explosive progress right now. In this sense you are definitely working on the super crucial yet complex future topic.      Finally I just want to mention that what comes to privacy controls in general, my deepest hope is that this field would not follow the DeFi space with all the irresponsibilities, insecurities, hacks etc. If there is a single most important app domain for reviewed science, formal methods, strong audits and other such best practices, this is it. Good luck with the project, I hope it will become reality and succeed!

  • 0
    commentator-avatar
    Gombilla
    Jun 2, 2024 | 5:38 PM

    Hi there. Great work and team set up. I feel there may be concerns about the scalability and efficiency of this platform in managing and processing large volumes of user-contributed data. It will also be important to address regulatory compliance and legal considerations related to data ownership, usage rights, and compensation. Thanks

    • 0
      commentator-avatar
      ivan reznikov
      Jun 3, 2024 | 6:44 PM

      Hi. Thanks for the wonderful question. Most datasets are currently stored in centralized repos: huggingface, kaggle, etc. That's great for tabular, less great for other formats. The issue is you have limited control over the data being used elsewhere. What we're trying to achive is a down the road a decentralized storage, that motivates creating high quality datasets, that you can get paid for. We understand the legal considerations and we have a plan for that :)

Reviews & Rating

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9 ratings
  • 0
    user-icon
    BlackCoffee
    Jun 10, 2024 | 12:01 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    What is the most important milestone?

    I try to find the milestone that is considered the most important but has not been clearly demonstrated by the team. In my opinion, that is milestone number 3: Decentralized governance. The reality is that Decentralized Governance is always difficult to implement successfully at the present time. The team should reconsider and consider doing decentralized governance more carefully.

  • 0
    user-icon
    Ayo OluAyoola
    Jun 9, 2024 | 3:39 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 5
    • Usefulness 4
    AI for AI Training Data Monetizaton

    This project aims to create a platform for ethical AI development by letting users contribute data and control its use.

    Feasible but challenging: Building the platform requires expertise and resources. It might be difficult to attract users, ensure data quality, and work with various AI frameworks.

    Moderately viable: Success depends on getting people to contribute data, offering a good reward system, and convincing AI developers to use the platform. The proposal could be stronger with a clearer financial plan.

    High potential for desirability: This project could be attractive to people who care about data privacy and fair rewards, and to AI developers who need high-quality ethical data. However, it needs to show AI developers the benefits beyond just more data (like better data quality and easier use).

    Potentially very useful: This project has the potential to improve AI development by providing diverse, ethical data while giving users more control. It aligns with trends in ethical AI. However, the proposal needs to explain how it will measure success (data quality, AI performance, user engagement).

    Here's what the project needs to focus on:

    • Getting enough people to use the platform
    • Making sure data quality is high
    • Partnering with AI developers
    • Having a clear financial plan
    • Measuring how well the project is working 

    I hope you get funded. We can partner.
    Please do check us out at  MarketIn API. We are developing the first-ever Marketing API that you can integrate into your solution at no cost, helping you achieve widespread user adoption.

    You are about to create a great solution, you must ensure the world knows about it. Our API is designed to help you reach the critical mass needed for successful adoption.

    https://deepfunding.ai/proposal/marketing-api-by-an-agi/  

     

  • 0
    user-icon
    TrucTrixie
    Jun 9, 2024 | 1:09 PM

    Overall

    4

    • Feasibility 3
    • Viability 4
    • Desirabilty 4
    • Usefulness 3
    Is it possible further develop token-based voting?

    Token-based voting also represents progress on the path towards Decentralized Governance. I encourage the team to develop the future in this direction. In fact, there are not many proposals to do this, so I highly encourage the team to implement it successfully even though this is not an easy task

  • 0
    user-icon
    Max1524
    Jun 8, 2024 | 12:24 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 3
    • Usefulness 3
    A good way to get motivated

    The solution offered is a decentralized platform for users to upload their own data for specific purposes and also provide rewards. I respond to this approach because rewards are considered a way to motivate.
    The solution of this proposal is not new but the details of creating motivation by receiving rewards are new. It contributes to strengthening feasibility and viability.
    I hope that there will be many proposals to apply such a template model.

  • 0
    user-icon
    Nicolad2008
    Jun 7, 2024 | 6:58 AM

    Overall

    4

    • Feasibility 3
    • Viability 4
    • Desirabilty 3
    • Usefulness 4
    Challenge and limit liquidity

    The project offers a creative decentralization platform, allowing users to upload their data to train large language models (LLMS) and receive bonuses if their data is selected. I like this because this idea promises to enhance transparency and control for contributors, and provide a diverse and high quality data set, but there are also many challenges to overcome. Ensuring the authenticity and quality of the contribution data is not a simple task, requiring a complex censorship and verification system to avoid false or unreliable data. Moreover, establishing a transparent and fair payment mechanism is a difficult problem, requiring accuracy and clearness in evaluating and allocating bonuses. Although the project has the potential to improve the LLMS training process, issues related to data authentication and payment mechanism should be thoroughly solved to ensure long -term success and sustainability.

  • 0
    user-icon
    CLEMENT
    Jun 2, 2024 | 5:44 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Addresses limitations in data collection for LLMs

    Kudos to the team. I believe this project has the potential to make a significant impact by addressing the current limitations and challenges in data collection for LLM training. Also, this project's objective of providing a decentralized platform where users can upload their data and receive compensation for their contributions, will democratize data contribution, enhance data diversity, and improve LLM performance. 

    As regards contribution to the SingularityNET AI community, I also see that this will enable users to have greater control over their data and participate in governance through token-based voting.

  • 0
    user-icon
    Tu Nguyen
    May 23, 2024 | 7:03 AM

    Overall

    4

    • Feasibility 3
    • Viability 4
    • Desirabilty 3
    • Usefulness 4
    Get Paid For Your Training Data

    The problem this proposal would address is the lack of transparency of current data collection methods, and contributors are often not rewarded. This is a problem that has happened in reality. The solution of this proposal: they will create a decentralized platform where users can upload their data for use in training large language models and receive compensation if their data is select. To me, this is not a novel problem and solution. However, the positive point is that this proposal clearly defines the difference of their solution. 
    They talked about introducing governance tokens. I think they should share more clearly about this Token. In addition, information about the team should also be more detailed, for example they should send members' social network links. Finally, they should determine the start and end times of milestones.

  • 0
    user-icon
    Joseph Gastoni
    May 20, 2024 | 3:53 PM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 3
    • Usefulness 4
    a platform for users to contribute data

    This proposal outlines a platform for users to contribute data for LLM training and receive compensation. Here's a breakdown of its strengths and weaknesses:

    Feasibility:

    • Moderate: The concept is feasible, but the technical development of the decentralized platform and integration with LLM training frameworks requires expertise and resources.
    • Strengths: The focus on leveraging existing blockchain technologies and AI integration reduces complexity.
    • Weaknesses: Building a user base, ensuring data quality through community review, and integrating with various LLM training frameworks might pose challenges.

    Viability:

    • Moderate: Success depends on attracting contributors, developing a sustainable compensation model, and convincing LLM developers to use the platform.
    • Strengths: The focus on ethical data sourcing, user control, and transparent compensation addresses current concerns.
    • Weaknesses: The proposal lacks details on the specific compensation model and its long-term financial sustainability.

    Desirability:

    • High (potential): For data contributors concerned about privacy and control, and LLM developers seeking high-quality ethical data, this project can be desirable.
    • Strengths: The focus on user empowerment, data privacy, and fair compensation addresses key concerns.
    • Weaknesses: The proposal needs to clearly articulate the value proposition for LLM developers beyond just access to a wider data pool (e.g., ensuring data quality, ease of integration).

    Usefulness:

    • High (potential): This project has the potential to improve LLM development by providing diverse, ethical data while empowering users and promoting responsible AI practices.
    • Strengths: The focus on decentralized data contribution, transparency, and user control aligns with important trends in ethical AI development.
    • Weaknesses: The proposal lacks details on how the project will measure its impact on data quality, LLM performance, and user engagement.

    Additional Points:

    • Developing a clear strategy for user acquisition and onboarding is crucial for building a critical mass of contributors.
    • Designing a robust and scalable data quality assessment process that leverages both automation and community review is essential.
    • Establishing clear partnerships and integration protocols with LLM developers is necessary to ensure platform adoption.

    Overall, the decentralized data contribution platform has a strong potential to be a valuable tool for ethical LLM development. Focusing on user acquisition, data quality assurance, and partnerships with LLM developers can increase its effectiveness. By outlining a sustainable financial model and impact measurement strategy, this proposal can become even more compelling.

    Here are some strengths of this project:

    • Addresses critical concerns in AI development - ethical data sourcing, user privacy, and transparent compensation for data contributions.
    • Emphasizes a decentralized approach with user control over data and participation in platform governance.
    • Proposes a multi-layered data quality assessment process combining automation and community review.

  • 0
    user-icon
    GraceDAO
    May 19, 2024 | 9:47 AM

    Overall

    3

    • Feasibility 3
    • Viability 3
    • Desirabilty 4
    • Usefulness 4
    Worldcoin has shown the problems with this

    Selling your data is approximately what WorldCoin is doing and we can already see the ethics and safety issues. Once centralized organization collecting people's private information, and the disadvantaged people being willing to sell their identities for small amounts of money. A human's data is typically worth $2-100 annually, so the only people who are truly interested in selling this information (especially because it doesn't include Google and Facebook data) are the most underpriviledged people. Therefore, you get skewed data from people who are already disadvantaged.

    user-icon
    ivan reznikov
    May 19, 2024 | 10:42 AM
    Project Owner

    Thanks for the response. I see your point, but our proposed platform distinguishes itself by addressing WorldCoin (Sam Altman's, btw) ethical and safety issues. Unlike WorldCoin's centralized collection of data, our platform is decentralized, providing full transparency and user control over data usage. Users can set permissions and maintain privacy, ensuring data is used ethically and securely. Compensation is transparent and fair, avoiding the exploitation of vulnerable populations. Basically, if you're an entity, that want's to train/finetune a model - you can request for data.

    Enron Email Dataset was ok to train first Siri iterration, but the models weren't acting as the enron employees

Summary

Overall Community

3.9

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

3.6

from 9 reviews

Viability

3.8

from 9 reviews

Desirabilty

3.7

from 9 reviews

Usefulness

3.8

from 9 reviews

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