KataData: Synthetic Data Protocol

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Justin Diamond
Project Owner

KataData: Synthetic Data Protocol

Funding Requested

$50,000 USD

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Overview

KataData aims to transform AI and scientific research by introducing a decentralized protocol for generating, managing, and trading synthetic data. By integrating diverse data sources—ranging from individual contributions and algorithmic generation to hardware-based feeds—KataData produces high-quality, versatile synthetic datasets. The project is grounded in the principles of SNet, advocating for decentralized, community-driven data initiatives. Our mission is to enhance data accessibility and innovation within the scientific community, fostering collaborative advancements in technology and research.

Proposal Description

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

KataData will enhance SingularityNET by introducing high-quality synthetic datasets generated from diverse sources, enriching the marketplace. Our AI-driven data synthesis lowers barriers for researchers to generate data and incentive more data creation. By offering tailored datasets, we address data scarcity and ethical concerns, attracting niche users and expanding marketplace appeal. Through community incentives and active participation we hope SNet becomes a leader in synthetic data.

Our Team

Our team’s main strengths lie in our deep technical knowledge and practical experience. Led by a PhD student in machine learning with papers published in ICLR, ICML, and NeurIPS, we bring cutting-edge expertise to our projects. We have 2-3 years of experience developing on SingularityNET and Cardano, ensuring robust, decentralized solutions.

View Team

AI services (New or Existing)

Synthetic Data Generator

Type

New AI service

Purpose

Generate high-quality synthetic datasets from various sources. (more specifics after milestone 1)

AI inputs

Raw data from individual contributions algorithmic models and hardware feeds.

AI outputs

Versatile synthetic datasets tailored to specific research needs.

Fine-tune synthetic datasets for specific domains

Type

New AI service

Purpose

Fine-tune synthetic datasets for specific domains

AI inputs

Generic synthetic datasets domain-specific parameters.

AI outputs

Fine-tuned synthetic datasets optimized for the specified domain.

Dataset Quality Evaluator

Type

New AI service

Purpose

Evaluate the quality and utility of synthetic datasets.

AI inputs

Synthetic datasets quality metrics criteria.

AI outputs

A score determining the quality of datasets for a specific task

Synthetic Data Augmentation

Type

New AI service

Purpose

Enhance existing datasets by generating additional synthetic data.

AI inputs

Existing datasets augmentation parameters.

AI outputs

Augmented datasets with increased diversity and volume.

Simulation-Based Data Synthesizer

Type

New AI service

Purpose

Generate synthetic datasets using physics-based and AI simulations.

AI inputs

Simulation models initial data parameters.

AI outputs

Simulated synthetic datasets for specific scenarios or experiments.

Cross-Domain Data Synthesizer

Type

New AI service

Purpose

Generate synthetic datasets that can be applied across multiple domains.

AI inputs

Multi-domain data sources synthesis parameters.

AI outputs

Versatile synthetic datasets applicable to various fields.

The core problem we are aiming to solve

While data is abundant, the industry lacks a scalable, community-focused platform for synthesizing this data into cohesive, high-value datasets that can benefit various sectors, such as AI research and scientific exploration. Current data systems are fragmented and siloed, making it difficult to access and integrate data effectively. This fragmentation hinders the creation of comprehensive datasets, limiting their utility and slowing innovation. Smaller entities and individual researchers are often unable to compete with larger organizations that have the resources to compile and manage extensive datasets. Furthermore, existing platforms do not incentivize community contributions, resulting in a lack of diverse, high-quality data. This gap in the market affects the data economy, as it relies heavily on centralized, proprietary data sources, which are often expensive and come with privacy concerns. Ethical and legal issues also arise from using real-world data, particularly in sensitive fields like healthcare and finance.

Our specific solution to this problem

KataData addresses the industry's critical need for a scalable, community-focused platform to synthesize cohesive, high-value datasets from diverse sources. Current market demands highlight the necessity for extensive, well-annotated data to drive innovation in AI research, machine learning, and scientific exploration. Existing centralized systems are fragmented, often inaccessible to smaller entities, and lack incentives for community contributions, resulting in a scarcity of diverse, high-quality datasets. KataData employs a decentralized protocol to aggregate data from various sources, including individual contributions, algorithmic generation, and hardware-based feeds, mitigating risks of data silos and fragmentation. Cryptographic methods ensure data integrity and privacy, making it suitable for handling sensitive information. KataData generates high-quality synthetic data tailored to market demands, such as training AI models and conducting scientific simulations. Community involvement is incentivized through smart contracts and dataset bounties, ensuring a steady influx of diverse data and democratizing data governance. This approach addresses ethical and legal concerns, as synthetic datasets comply with privacy regulations. KataData thus enhances the data economy, making high-quality datasets accessible to smaller entities and individual researchers, fostering innovation and meeting the growing demands of the AI and scientific research markets.

 

Project details

The Contemporary Landscape of AI and Scientific Research: The Role of KataData

The contemporary landscape of Artificial Intelligence (AI) and scientific research is brimming with data, yet it suffers from a significant gap—a lack of scalable, community-oriented platforms for transforming this abundant resource into valuable synthetic datasets. KataData, a pioneering project, is designed to address this gap by revolutionizing how we generate, manage, and trade synthetic data. Leveraging a decentralized protocol, KataData aligns with the principles of Web 3.0, advocating for a community-driven, decentralized approach.

The Data Challenge in the Digital Age

Data has been deemed the 'oil' of the digital age, but unlike oil, it is plentiful. The real challenge lies in refining it into a usable or reusable (synthetic) form. Conventional centralized data management systems often suffer from limitations such as data silos, lack of interoperability, and the need for trusted intermediaries. KataData addresses these limitations by employing a decentralized protocol that amalgamates disparate data sources—including individual contributions, algorithmically generated data, and hardware-based feeds—into versatile synthetic datasets. This approach offers significant advantages for applications across AI, machine learning, large language models (LLMs), and complex scientific simulations.

Addressing Data Scarcity and Heterogeneity in AI and Machine Learning

Prevailing AI models, including deep neural networks, generative adversarial networks (GANs), and reinforcement learning agents, are inherently data-hungry. These models require massive, well-annotated datasets for training, a resource often out of reach for individual researchers and small organizations. KataData aims to democratize access to high-quality synthetic datasets that are both diverse and reliable. This democratization opens new research avenues and enhances model robustness and interpretability, thereby fostering innovation and progress in AI and machine learning.

Fine-Tuning Large Language Models (LLMs) with Decentralized Protocols

Large Language Models (LLMs) such as the GPT series are increasingly being fine-tuned for specialized tasks, including legal and medical text analysis. Fine-tuning these models often demands access to domain-specific, high-quality datasets, which are sensitive and proprietary. KataData's decentralized protocol, coupled with privacy-preserving techniques, provides a groundbreaking solution by generating synthetic datasets tailored for these specialized domains. This not only enhances model performance but also alleviates the ethical and legal complexities associated with using actual sensitive data for fine-tuning.

Enabling Scientific Exploration and Simulations

The scientific community has long struggled with the lack of specialized datasets, especially in burgeoning fields like quantum computing, genomics, and climate modeling. KataData's protocol harmonizes data from diverse sources and imbues them with synthetic properties conducive to complex scientific simulations. This capability accelerates research and development, enabling significant advancements in these critical areas of study.

Community-Centric Data Handling

Conventional centralized systems tend to restrict data governance to a limited set of stakeholders, leading to potential misuse and restricted access. In stark contrast, KataData's decentralized protocol promotes a community-driven approach, democratizing data governance and usage. Smart contracts and decentralized governance mechanisms incentivize data contributions and quality curation, ensuring that the datasets are both diverse and of high quality.

Competition and USPs

KataData distinguishes itself by moving beyond the typical reliance on data aggregation for large language modeling, which dominates current methods. While existing solutions focus primarily on collecting vast amounts of data, KataData employs a more general, modular approach. We incentivize data aggregation as a foundational step but go further by integrating physics-based simulations and AI-driven models. This enables the creation of novel and finely-tuned datasets tailored to specific needs, ensuring versatility and relevance across various applications. By leveraging a decentralized protocol, advanced cryptographic security, and community-driven incentives, KataData not only democratizes data access but also enhances data quality and applicability. Our method ensures that datasets are not only extensive but also contextually rich and highly specific, meeting the diverse demands of AI research, scientific exploration, and beyond.

Existing resources

We will be working with our tech stack developed with Hetzerk and our partners. 

Open Source Licensing

Custom

Our open-source licensing method employs a time-lagged approach, allowing us to maintain a competitive advantage while contributing to the advancement of the field. By initially keeping our cutting-edge developments proprietary, we ensure that we can fully capitalize on our innovations. After a defined period, we release these advancements to the open-source community, fostering collaboration and furthering research. This strategy balances the need for competitive differentiation with a commitment to pushing the boundaries of AI and synthetic data forward.

 

Links and references

hetzerk.com/katadata

Revenue Sharing Model

Custom Model

Custom Description:

Our revenue sharing model is a token allocation approach contingent on SNet partnering with us as a spinoff in some manner (details to be discussed after or during voting)

Proposal Video

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

  • Total Milestones

    5

  • Total Budget

    $50,000 USD

  • Last Updated

    20 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

$12,500 USD

Milestone 2 - Contract Signing

Description

Formalizes the partnership between KataData and SingularityNET

Deliverables

Contract signed with SingularityNET (SNet).

Budget

$1,000 USD

Milestone 3 - Protocol Design and Data Security

Description

Develops the technical architecture and the necessary research of KataData's decentralized protocol focusing on data storage retrieval sharing mechanics and synthetic generation. Additionally it establishes protocols for data security and drafts an initial whitepaper. This milestone is crucial for defining the framework that will enable KataData's vision of creating high-quality versatile synthetic datasets. It also ensures that the system will be secure modular and community incentivized setting KataData apart from other solutions.

Deliverables

Detailed design document for the decentralized protocol specifying data storage retrieval and sharing mechanics. Protocols for data security including encryption standards and privacy-preserving techniques. Initial whitepaper draft highlighting the novel aspects of KataData in contrast to existing solutions. Ethical guidelines for data usage and protocol governance.

Budget

$15,000 USD

Milestone 4 - Data and Algorithmic Synthesis development

Description

Produces a proof-of-concept that demonstrates data fusion/synthetic capabilities and develops beta versions of algorithmic models for synthetic data generation. It also includes limited-scale pilot testing and technical reporting when necessary. This milestone is where the KataData vision starts becoming a reality. It provides the initial demonstrations and technical validations needed to show that the system can indeed produce valuable high-quality synthetic data from disparate sources.

Deliverables

Proof-of-concept showcasing the data fusion capabilities with metrics to measure effectiveness. Beta version of algorithmic models for synthetic data generation benchmarked against select real-world datasets. Limited-scale pilot testing to demonstrate protocol's scalability and reliability. Technical report summarizing insights challenges and potential improvements.

Budget

$17,500 USD

Milestone 5 - Smart Contract Mechanics

Description

Develops smart contracts logic and prototypes for community-driven incentives. Whether through smart contracts or modular backend design this milestone lays the groundwork for incentivizing community involvement in data contribution and curation thereby fostering a decentralized ecosystem that aligns with SNet’s principles.

Deliverables

Development of smart contracts logic for dataset bounties. Prototypes for community-driven incentives and rewards systems.

Budget

$4,000 USD

Join the Discussion (1)

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1 Comment
  • 0
    commentator-avatar
    CLEMENT
    Jun 1, 2024 | 4:23 PM

    Kudos to the proposing team. However, I am concerned about challenges related to data quality, privacy, and security. I hope the team intends to look into this as they are essential considerations for the success and trustworthiness of KataData's Synthetic Data Protocol.

Reviews & Rating

Sort by

7 ratings
  • 0
    user-icon
    BlackCoffee
    Jun 10, 2024 | 1:19 AM

    Overall

    3

    • Feasibility 4
    • Viability 3
    • Desirabilty 3
    • Usefulness 3
    Just Justin Diamond, I don\'t think is enough

    Justin Diamond's experience and expertise are necessary but not sufficient conditions to implement this proposal well. Reality shows that to do this proposal well, we need more abundant human resources, especially with brainpower in blockchain technology, SNET, and AI. The team should add members that the team thinks are missing.

  • 0
    user-icon
    TrucTrixie
    Jun 9, 2024 | 2:20 PM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 4
    • Usefulness 4
    Rigor is demonstrated through implementation time

    Milestones with separate work schedules are what I would like to see in this proposal. At the very least, the team should estimate the time this proposal will be completed so that the public is aware of the rigor in implementing the proposal. Please pay attention to these comments.

  • 0
    user-icon
    Max1524
    Jun 9, 2024 | 5:07 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 3
    • Usefulness 4
    Team\'s expertise & experience is a requirement

    KataData's purpose is to transform AI and scientific research in a way that introduces a decentralized protocol for creating, managing, and trading data. I look forward to Katadata's ability to produce high-quality flexible synthetic datasets. But I know this is not easy. Although it is based on SNET principles, there are still obstacles in practice. One of those obstacles is the requirement for high quality human resources with good expertise and experience. I'm quite confident that the team has 2-3 years of development experience on SNET and Cardano.

  • 0
    user-icon
    Nicolad2008
    Jun 8, 2024 | 3:25 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    The current data system is fragmented

    The project has high implementation potential thanks to the integration of diverse data sources, from individual contributions to data generation using algorithms and hardware sensors, creating flexible, high-quality synthetic datasets . This helps reduce barriers for researchers to generate data and encourages new data creation. The project also addresses data shortages and ethical concerns, attracts specialized users, and broadens market appeal. However, the project also faces some challenges. Current data systems are fragmented and isolated, making it difficult to access and integrate data effectively. This hinders the creation of comprehensive data sets, limits usability, and slows innovation. Additionally, existing platforms do not encourage community contributions, leading to a lack of high-quality, diverse data. This gap affects the data economy, as it relies heavily on centralized, proprietary data sources, which are often expensive and have privacy issues. Ethical and legal issues also arise from the use of real-world data, especially in sensitive areas such as healthcare and finance.

  • 0
    user-icon
    CLEMENT
    Jun 1, 2024 | 4:18 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    KataData will empower researchers and developers

    As an AI researcher myself, I believe this would be a useful tool for me and other researchers alike. I believe that by providing a decentralized platform for generating, managing, and trading synthetic data, KataData empowers researchers and developers to access and utilize datasets that may otherwise be inaccessible or limited in availability. Indeed, this describe this project desirability and usefulness.

    Additionally, I also believe KataData will enrich the SNET ecosystem by offering a novel data protocol that aligns with the principles of decentralization and community-driven initiatives. The availability of high-quality synthetic datasets on the marketplace enhances the range of resources accessible to AI developers and researchers, facilitating the creation of more robust and diverse AI models and applications.

    Kudos to the team !

  • 0
    user-icon
    Tu Nguyen
    May 23, 2024 | 2:33 AM

    Overall

    4

    • Feasibility 3
    • Viability 4
    • Desirabilty 3
    • Usefulness 4
    Synthetic Data Protocol

    The problem this proposal addresses is the fragmentation and isolation of current data systems, making it difficult to access and integrate data effectively. This fragmentation hinders the creation of comprehensive datasets, limits their utility, and slows innovation. This proposal will create a scalable, community-focused platform for aggregating cohesive, high-value data sets from diverse sources. They will address data scarcity and heterogeneity in AI and Machine Learning, refine large language models (LLMs) with decentralized protocols, enable scientific discovery and simulation, and process Community-centered data management. This is quite a useful solution in practice.
    The project team has only 1 member. This creates the risk of them not completing tasks on schedule. In my opinion, they should look for some more members suitable for the project. Additionally, they should identify the start and end times of each milestone.

  • 0
    user-icon
    Joseph Gastoni
    May 22, 2024 | 9:29 AM

    Overall

    4

    • Feasibility 4
    • Viability 3
    • Desirabilty 3
    • Usefulness 4
    a decentralized protocol for generating, managing

    This proposal outlines a decentralized protocol for generating, managing, and trading synthetic data (KataData) to address challenges in AI and scientific research. Here's a breakdown of its strengths and weaknesses:

    Feasibility:

    • Moderate-High: The core functionalities (data aggregation, AI-driven synthesis, smart contracts) leverage existing technologies.
      • Strengths: The concept builds on established decentralized protocols and AI techniques for data generation.
      • Weaknesses: Technical challenges might arise in ensuring the quality and integrity of synthetic data generated from diverse sources.

    Viability:

    • Moderate: Success depends on user adoption, the value proposition for data providers and consumers, and the overall growth of the decentralized data marketplace.
      • Strengths: The proposal addresses a growing need for high-quality synthetic data in AI and research.
      • Weaknesses: The proposal lacks details on the economic model for incentivizing data contributions and trading within the platform.

    Desirability:

    • Moderate-High: For researchers and developers seeking access to diverse, ethical synthetic data, this could be desirable.
      • Strengths: The proposal offers a potentially valuable solution for data scarcity and ethical concerns in AI development.
      • Weaknesses: The proposal needs to clearly demonstrate the advantages of KataData synthetic data compared to existing data sources.

    Usefulness:

    • Moderate-High: The project has the potential to improve data accessibility and innovation in AI and research, but its impact depends on the quality and adoption of the synthetic data generated.
      • Strengths: The proposal offers a scalable and potentially cost-effective way to generate synthetic data for various applications.
      • Weaknesses: The proposal lacks details on how the platform will ensure the validity and reliability of synthetic data for scientific research.

    Overall, this KataData project has a promising approach, but focus on:

    • Data Quality and Validation: Clearly outlining the mechanisms for ensuring the quality, consistency, and validity of synthetic data generated through the platform.
    • Economic Model Design: Developing a clear economic model that incentivizes data contribution, curation, and trading while ensuring data accessibility for researchers.
    • Competitive Advantage: Demonstrating the clear advantages of KataData synthetic data compared to existing synthetic data generation methods and data marketplaces.
    • Scientific Validation: Establishing methods for validating the scientific rigor and accuracy of synthetic data for research purposes.

    By addressing these considerations, this KataData project can increase its chances of success and become a valuable platform for synthetic data generation in AI and scientific research.

    Here are some strengths of this project:

    • Addresses the challenge of data scarcity and ethical concerns in AI development.
    • Leverages decentralized protocols and AI for scalable synthetic data generation.
    • Promotes community-driven data governance and incentivizes data contributions.

Summary

Overall Community

3.9

from 7 reviews
  • 5
    0
  • 4
    6
  • 3
    1
  • 2
    0
  • 1
    0

Feasibility

3.9

from 7 reviews

Viability

3.6

from 7 reviews

Desirabilty

3.4

from 7 reviews

Usefulness

4

from 7 reviews

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