SoloAI

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EdisonSoloAI
Project Owner

SoloAI

Funding Requested

$150,000 USD

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Overview

SoloAI is the first data labeling platform designed specifically for AI projects, utilizing decentralized zkHE biometric verification, powered by its own L2 chain. SoloAI guarantees high-quality data labeled by 100% real humans. From a user perspective, our platform empowers users to perform data labeling tasks from anywhere, maintaining personal data privacy and accountable anonymity. From a project perspective, SoloAI ensures clean and reliable data through biometrics and leverages EigenLayer’s intersubjective forking mechanism for enhanced trust. SoloAI's unique approach allows for diverse user groups to be assigned to different tasks, creating the most valuable datasets for AI projects.

Proposal Description

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

SoloAI will enhance the SingularityNET AI platform by providing high-quality, verified human-labeled data essential for training robust AI models. Our decentralized data labeling platform uses zkHE for secure data handling, ensuring data integrity and privacy. By efficiently connecting AI projects with genuine, diverse human inputs, SoloAI will improve the accuracy and applicability of AI solutions, fostering a more trustworthy and scalable AI ecosystem.

Our Team

Our team brings exceptional expertise in AI, blockchain, and data science. CEO Edison Siu, MBA from Yale, leads the team. CTO Dr. Taotao Wang, a zk expert, and Blockchain Scientists Dr. Shengli Zhang and Dr. Qing Yang focus on blockchain architecture. Dr. Sissi Wu specializes in AI. Four of them are professors at Shenzhen University and top experts in their fields. Our team has published seven academic papers related to the project, ensuring SoloAI's innovative edge and technical excellence.

View Team

AI services (New or Existing)

Multi Speaker Separation

How it will be used

We plan to use the SNET AI Service for Multi Speaker Separation to preprocess our audio files for data labeling. This service will help us separate the speakers and cut the audio into smaller pieces, making it easier and more efficient for users to label them.

Company Name (if applicable)

SoloAI

The core problem we are aiming to solve

The core problem SoloAI aims to solve is the critical issue of data quality in AI training. Traditional data labeling solutions face major challenges:

  1. Low-Cost Labor in Developing Countries:

    • Low Quality: Dependence on low-cost labor results in poor data quality due to insufficient worker expertise.
    • Uniformity of Thought: Similar educational backgrounds among workers lead to biased data, lacking diversity.
    • Ethical Concerns: Poor working conditions in developing countries raise ethical issues.
  2. Bots Posing as Humans:

    • Contradiction of Human Labeling: Using bots for tasks intended for humans undermines the purpose of leveraging human intelligence in AI training.
    • Low-Quality Data: Bots generate fake or inaccurate data, compromising AI model training.
    • Lack of Accountability: Ensuring data quality is difficult with bots.

SoloAI addresses these challenges by offering a decentralized data labeling platform using zkHE to ensure high-quality data from verified real humans, improving AI model accuracy and robustness.

Our specific solution to this problem

SoloAI introduces innovative solutions to revolutionize data labeling for AI projects. Our approach ensures data integrity, user privacy, and high-quality results through cutting-edge technologies.

Decentralized zkHE

SoloAI guarantees that data labeling tasks are performed by 100% real humans while maintaining user privacy. This is achieved through our decentralized biometric verification system, employing Pedersen Commitment Homomorphic Encryption (HE) and Zero-Knowledge Proofs (ZKP).

AI Model for Next-Level Data Labeling

SoloAI's AI model clusters users based on platform activities and matches them with tasks aligned with their expertise. This approach enhances data quality and efficiency. Our AI also analyzes behavioral patterns to identify accounts controlled by the same person, ensuring only genuine, active users contribute to the datasets.

EigenLayer for Data Integrity

We use EigenLayer's 'Intersubjective Forking' mechanism to verify data labeling tasks. Data Verification Nodes, operated by EigenLayer stakers, compete for assignments, distribute tasks, and reward verified users, ensuring meticulous data checking and eliminating malicious labelers.

SoloAI sets a new standard in data labeling, ensuring AI projects receive high-quality, reliable data essential for effective AI model training.

Project details

Innovations Behind SoloAI's Data Labeling

DECENTRALIZED zkHE

Accountable Anonymity: Users can maintain accountable anonymity, while projects are guaranteed that the labor is performed by 100% real humans. This is achieved through SoloAI’s groundbreaking fully decentralized biometric verification with Pedersen Commitment Homomorphic Encryption (HE) and Zero-Knowledge Proofs (ZKP).

Pedersen Commitment-Based Homomorphic Encryption:

  • Maximizes Data Confidentiality: Utilizing homomorphic encryption, our system ensures that biometric data remains encrypted throughout the verification process, guaranteeing non-disclosure of personal information during any computational steps.
  • Streamlines Off-Chain Processing: The computationally demanding nature of homomorphic encryption is offset by the ability to normalize biometric data formats. This normalization facilitates streamlined ZKP circuit designs, effectively diminishing the computational load required off-chain.

Groth 16 zk-SNARK ZKP:

  • Ensuring Verifiability: ZKP allows for verifiability on blockchain transactions without disclosing user’s private data, enabling secure and private validation of operations.
  • Reduced On-Chain Computation: Leverages recursive ZKP (Pedersen commitment-based homomorphic computation + Groth 16 zk-SNARK) to validate encrypted computations with minimal blockchain overhead, ensuring efficient consensus even with complex operations.
  • Dual-Layered Protection: Adds a robust security layer on top of the homomorphic encryption, using ZKPs to offer a double shield that maintains data privacy while confirming computation integrity without disclosure.

AI MODEL FOR NEXT-LEVEL DATA LABELING

SoloAI's AI model brings data labeling to the next level by strategically clustering users and matching them with tasks that align with their proven areas of expertise. This approach not only enhances data quality and applicability but also ensures that the labeling process is efficient and effective, leveraging the unique strengths and knowledge of each user.

AI-Powered Task Matching: The recommendation system uses AI to analyze the specific skills and expertise of each user. This system matches users with data labeling tasks that align with their proven areas of knowledge, thus enhancing the quality of the output.

Advanced Sybil Detection: Adding an extra security layer to check against behavioral patterns that confirm the user's activities align with those of a genuine, active human user. This ensures that only real, engaged users contribute to your data sets, maintaining the cleanliness and integrity of the labeled data.

Duo-Layer User Analysis:

  • Mission-Specific Analysis: Providing detailed insights into user behaviors and interactions during a specific mission. Focuses on specific user activities and patterns within the application context, allowing for targeted improvements and engagement strategies.
  • Platform-Wide Analysis: Offering a comprehensive overview of user engagement across the SoloMission platform. Extends beyond single missions to encompass a broad analysis across all completed missions, aggregating data to enhance overall strategic planning and understanding of user backgrounds.

EigenLayer FOR DATA INTEGRITY

SoloAI is powered by SoloChain, our underlying EigenLayer AVS Layer-2 chain that serves as the backbone of our protocol, leveraging Ethereum’s security. SoloAI uses EigenLayer's 'Intersubjective Forking' to ensure that data labeling tasks are thoroughly verified. Data Verification Nodes, operated by EigenLayer stakers, compete to receive assignments, distribute tasks, and rewards to verified users. This system ensures that data is meticulously checked and cleaned, identifying and eliminating potential malicious labelers.

Why SoloAI?

User Perspective:

  • User-Friendly Mobile Platform: Allows users to earn extra income anytime, anywhere.
  • Accountable Anonymity: Users can maintain their hidden identity while ensuring accountable anonymity.
  • Gamified Data Labeling: Lowers entry barriers for professionals who want to earn extra income during their spare time through a gamified process.

Project Perspective:

  • Guaranteed Data Quality: Ensures that data labeling is conducted by 100% verified real humans with expertise knowledge.
  • Enhanced Data Quality Control and Transparency: Achieved through EigenLayer integration.
  • Diverse Data Labeling Marketplace: Features users from various backgrounds, providing domain-specialized data labeling.

General Flow of the SoloAI Platform

The SoloAI platform is intricately designed to work seamlessly with our zkHE biometric verification. Here’s how it operates:

  1. AI Companies: AI projects requiring real-person labeled data can collaborate with SoloAI by submitting mission requests and providing a lump sum of tokens to be used as rewards for participants.
  2. SoloAI: Delegates missions to our Data Verification Nodes based on their bids for each mission. For instance, if Project ABC offers 10,000 $ABC as rewards, and Node A bids to allocate 9,000 $ABC to users, while Node B bids to allocate 8,000 $ABC, the mission will be awarded to Node A.
  3. Data Verification Nodes: SoloAI will be one of the first use cases of EigenLayer's 'Intersubjective Forking'. Our Data Verification Nodes, who are EigenLayer operators who have staked $EIGEN, aim to ensure the authenticity of data labeling tasks. These nodes compete to receive assignments from SoloAI, which they then distribute along with rewards to verified users. Given the intersubjective nature of our tasks delegated to the Nodes to identify potential irregularities of individual users via the use of our AI data cleansing framework, $bEIGEN’s intersubjective forking based on social consensus is the best way to ensure that these tasks are duly executed, thus maintaining the cleanliness and integrity of data labeled.
  4. Verified Users: Users verified as real humans via our zkHE framework will perform data labeling tasks and receive corresponding rewards.

Conclusion

SoloAI’s innovative data labeling platform harnesses the power of decentralized zkHE, AI-driven task matching, and EigenLayer’s data integrity mechanisms to ensure high-quality, reliable, and secure data labeling for AI projects. By connecting AI projects with verified real humans, SoloAI creates a fair, transparent, and efficient data labeling ecosystem that drives the growth and effectiveness of AI models, setting a new standard in the data labeling industry.

Competition and USPs

1. Biometrics for Real Human Labelers: SoloAI is the only zkHE biometric verification project that ensures all labelers are real humans, enhancing data authenticity and reliability compared to competitors.

2. Decentralized Data Cleansing with EigenLayer: Leveraging EigenLayer AVS, SoloAI ensures transparent and meticulous data verification, setting it apart from both Web3 and Web2 solutions.

3. AI Labeler Clustering for Domain-Specific Tasks: SoloAI’s AI model clusters users based on their expertise, matching them with relevant tasks to improve data quality and applicability.

4. Proprietary Layer-2 Architecture: Built on SoloChain, SoloAI supports data labeling and allows for future expansion into real user verification for wallets and IDO platforms, offering flexibility and strategic growth potential.

Market Success: SoloAI’s unique combination of features addresses critical issues in traditional and Web3 data labeling, ensuring higher data quality, transparency, and scalability.

Needed resources

We will be hiring these roles after fundraising.

Product Management:

  • 1 Product Manager for the entire project

SoloAI Platform:

  • 1 UI Engineer
  • 3 Front-end Engineers
  • 2 Back-end Engineers

SoloChain:

  • 1 Front-end Engineer
  • 5 Back-end Engineers (focused on FIDO, AA, and L2)

ML Data Analysis:

  • 2 Back-end Engineers/Data Scientists

Test Group:

  • 2 Engineers

Existing resources

We will leverage our team's extensive expertise and the seven academic papers we have previously published, which cover crucial aspects of blockchain, AI, and zk-SNARK technologies. This foundational work provides a robust framework for SoloAI, reducing the need for initial funding in these areas.

Links and references

https://postman511.github.io/
https://scholar.google.com/citations?user=vjujlkoAAAAJ&hl=en
https://eesissi.com/
https://scholar.google.com.hk/citations?user=VlEwkc8AAAAJ&hl=en

Revenue Sharing Model

Token Allocation

We are planning for a token launch in:

2024-Q4

If awarded by Deep Funding, we will allocate this percentage of the total token supply to SNET / Deep Funding:

5

Token Description (type, value, utility):

If awarded by Deep Funding, we will allocate a percentage of the total token supply to SNET/Deep Funding. The exact tokenomics will be discussed with the lead of our pre-seed round after successful fundraising. We are also open to further discussion to ensure a mutually beneficial agreement.

Token Emission
Users can earn $SOLO in the following ways:
1/ Account Opening: During the initial promotion period, each verified real human will receive a certain amount of $SOLO.
2/ Task Completion: By completing a specified number of tasks within a given timeframe (weekly or monthly), users can earn additional $SOLO tokens on top of those distributed by individual projects for each task.


Utility of $SOLO
1/ By staking $SOLO, users can participate in our revenue-sharing scheme and enjoy a share of SoloAI’s profits, which come from the following sources:
- Data Sales
- Commission from Each Mission
- Any Future Income Streams
2/ Additionally, a user's level will increase with the amount of $SOLO staked. Higher-level users will have access to high-income missions in the future.

Proposal Video

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

  • Total Milestones

    3

  • Total Budget

    $150,000 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

$37,500 USD

Milestone 2 - Frontend Development

Description

This milestone focuses on the development of the SoloAI platform's frontend. It includes designing and implementing the user interface, ensuring a seamless user experience, and integrating key features necessary for the platform's functionality. The goal is to create an intuitive, user-friendly interface that allows users to easily navigate and perform data labeling tasks.

Deliverables

- Development of the user interface (UI) and user experience (UX) design. - Implementation of core frontend functionalities, including user registration, task assignment, and data labeling interfaces. - Integration with backend services for real-time data processing and user interaction. - Testing and debugging to ensure a smooth, bug-free user experience. - Deployment of the frontend on the SoloAI platform for initial user access and feedback.

Budget

$30,000 USD

Milestone 3 - Hiring Key Personnel

Description

This milestone focuses on hiring key personnel essential for the successful development and operation of SoloAI. The roles include frontend and backend developers, AI specialists, and project managers. These hires will bring the necessary expertise and manpower to ensure the project progresses efficiently and effectively.

Deliverables

- Recruitment of qualified engineers, AI specialists, and project managers. - Onboarding and training of new hires to align them with SoloAI’s goals and methodologies. - Establishment of a collaborative workflow and communication channels to facilitate efficient project development. - Setting up performance metrics and regular review processes to ensure team productivity and project milestones are met.

Budget

$82,500 USD

Join the Discussion (3)

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3 Comments
  • 0
    commentator-avatar
    Emotublockchain
    Jun 9, 2024 | 4:14 PM

    This is a good one!!! I would like you to elaborate on team experience on Ai, colective data labeling as it seems to be a sophisticated project. I would like to get more details on both the budget and the team experience to this.

  • 1
    commentator-avatar
    Gombilla
    Jun 6, 2024 | 10:32 AM

    I would comment that this is an innovative approach to data labeling addresses key challenges in AI project development, such as data quality, privacy, and trust. However, encouraging users to participate in data labeling tasks may be challenging, especially if they are not adequately incentivized or if the platform is perceived as complex or difficult to use. Providing user-friendly interfaces, fair compensation mechanisms, and transparent governance structures will be essential to drive user adoption and engagement. Good luck with this !

    • 0
      commentator-avatar
      Emotublockchain
      Jun 9, 2024 | 4:20 PM

      Yes, you are right, UI design should be simplified for users so there isn't any complications on user of app. This is a very complex project. Yet, I believed it complexicity should be the most difficult part when it comes to simplicity.

Reviews & Rating

Sort by

10 ratings
  • 0
    user-icon
    Joseph Gastoni
    May 23, 2024 | 4:48 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    SoloAI, a decentralized data labeling platform

    This proposal outlines SoloAI, a decentralized data labeling platform that uses zkHE for biometric verification and EigenLayer for data integrity. Here's a breakdown of its strengths and weaknesses:

    Feasibility:

    • Moderate-High: The project leverages existing technologies (zkHE, EigenLayer) but needs to demonstrate the scalability and efficiency of its zkHE implementation for real-world use.
      • Strengths: The proposal builds upon established concepts and integrates them in a novel way for data labeling.
      • Weaknesses: The proposal lacks details on the technical complexity of zkHE integration for biometric verification at scale.

    Viability:

    • Moderate: Success depends on user adoption, platform adoption by AI projects, and the overall cost-effectiveness of the solution compared to alternatives.
      • Strengths: The proposal addresses a critical need for high-quality data in AI training and offers a unique value proposition.
      • Weaknesses: The proposal needs a clearer strategy for user acquisition and engagement, as well as cost comparisons with existing data labeling solutions.

    Desirability:

    • High (for a specific audience): For AI project developers seeking high-quality labeled data, this could be highly desirable.
      • Strengths: The proposal caters to a specific need within the AI development community.
      • Weaknesses: The proposal needs to demonstrate the value proposition for users beyond just earning income, especially for attracting domain experts.

    Usefulness:

    • High Potential: The project has the potential to improve the quality of data for AI training, but hinges on successful implementation, user/project adoption, and cost-effectiveness.
      • Strengths: The proposal offers a framework for a secure and verifiable data labeling platform with real human labelers.
      • Weaknesses: The proposal lacks details on how SoloAI integrates with existing AI development workflows and tools.

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

    • Technical Details: Provide more details on the scalability and efficiency of the zkHE implementation for biometric verification.
    • User Acquisition Strategy: Develop a clear plan for attracting both data labelers and AI projects to the platform.
    • Cost-Effectiveness Analysis: Compare the cost of using SoloAI for data labeling with existing solutions.
    • Integration with AI Development Tools: Explain how SoloAI integrates with existing AI development workflows and tools.

    Strengths:

    • Addresses a critical challenge in AI training (data quality).
    • Uses zkHE for secure and verifiable biometric authentication.
    • Leverages EigenLayer for data integrity and decentralized verification.
    • Offers AI-powered task matching for improved data quality.

    Weaknesses:

    • Lacks details on scalability and efficiency of zkHE implementation.
    • Needs a clearer user acquisition and engagement strategy.
    • Needs to demonstrate cost-effectiveness compared to alternatives.
    • Lacks explanation on integration with existing AI development tools.

  • 0
    user-icon
    CLEMENT
    May 31, 2024 | 1:22 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Addresses concerns surrounding data quality

    I love the fact that this project is geared at enhancing privacy, and accountability in AI projects. Interestingly the proposers have made known their intent at utilizing decentralized zkHE biometric verification. This will ensure that their platform ensures high-quality data labeled by real humans while maintaining user privacy and accountable anonymity. This not only enhances the reliability of AI models but also safeguards sensitive data, contributing to trust and transparency in the AI industry. Overall, SoloAI\'s innovative approach to data labeling has the potential to drive advancements in AI research and development while promoting trust, privacy, and accountability in the broader AI industry.

    Kudos to the proposing team !

  • 0
    user-icon
    Max1524
    Jun 8, 2024 | 9:21 AM

    Overall

    4

    • Feasibility 3
    • Viability 4
    • Desirabilty 4
    • Usefulness 3
    Consider the final result to evaluate Usefulness

    Is it really possible for Bots to act like humans? I think time will tell once the proposal is put into practice. Although the team is making efforts to prove the ultimate effectiveness of this proposal - I acknowledge it but cannot give an absolute number of stars for feasibility & usefulness. The implementation process for Bots to be able to completely replace humans is still a long way off.

  • 0
    user-icon
    Emotublockchain
    Jun 9, 2024 | 4:50 PM

    Overall

    4

    • Feasibility 3
    • Viability 3
    • Desirabilty 4
    • Usefulness 5
    Data labelling

    The provision for high-quality, verified human-labeled data are essential for training robust AI models. The decentralized data labeling platform uses zkHE for secure data handling, ensuring data integrity and privacy. This is beneficial and efficient while connecting AI projects with genuine, diverse human inputs, The application of AI solutions, shows trust for the future.

     

     

  • 0
    user-icon
    Tu Nguyen
    May 22, 2024 | 10:15 AM

    Overall

    4

    • Feasibility 3
    • Viability 4
    • Desirabilty 3
    • Usefulness 4
    SoloAI

    This proposal will solve two main problems: cheap labor in developing countries, and Bots playing the role of humans. These are very real problems. This proposal provides an innovative solution to revolutionize data recording for AI projects. This is a reasonable solution to the problems they have raised. The project has innovations such as: responsible anonymity, homomorphic encryption based on Pedersen commitment, Groth 16 zkSNARK ZKP.
    Their members have expertise in AI, Blockchain, and data science. However, the information about the members should be clearer. In the team section, members have not updated their profile pictures yet. Furthermore, they should share members\' social media links.
    Regarding milestones, they should identify the start and end dates of each milestone. They should also clearly define metrics to measure project outputs. They should also allocate more detailed budgets based on milestones.

  • 0
    user-icon
    Tarran
    May 29, 2024 | 5:32 AM

    Overall

    4

    • Feasibility 3
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    SoloAI proposal presents an innovative solution

    The SoloAI proposal presents an innovative and technically sophisticated solution for decentralized data labeling, leveraging advanced technologies such as zkHE biometric verification and AI-driven task matching. While the concept is highly appealing and addresses significant issues in the data labeling industry, the proposal lacks detailed execution plans, verified team member information, and a transparent budget breakdown, which impacts its overall feasibility and viability.

    Feasibility

    The utilization of innovative technologies like EigenLayer for data integrity and zkHE biometric verification is essential to SoloAI's viability. These are creative and theoretically solid ways to guarantee high-quality data from actual people while protecting privacy. The project's practical execution within the suggested timetable and budget is called into question due to its technical complexity and lack of clear implementation stages.


    Viability

    The viability of the project is currently undermined by the lack of verified team member information and specific details about their expertise and roles. While the named team members possess impressive credentials, the consent and active participation of all key personnel need confirmation. Furthermore, the budget allocation is vague and lacks transparency, which further reduces confidence in the project’s successful completion.

    Desirability

    There is a high desire for a solution that addresses the critical issues of data quality and integrity in AI training. SoloAI's approach to using verified human labelers and leveraging decentralized technologies to ensure data quality is compelling and meets a significant market need. The proposal's emphasis on privacy and ethical data labeling also adds to its attractiveness in the current AI landscape.

    Usefulness

    The usefulness of SoloAI to the decentralized AI platform is notable. By providing high-quality, reliable data, SoloAI can significantly enhance the training and accuracy of AI models. The platform's potential to connect AI projects with diverse human inputs and ensure data integrity through advanced verification mechanisms aligns well with the goals of fostering a robust and scalable AI ecosystem.

  • 0
    user-icon
    BlackCoffee
    Jun 10, 2024 | 12:24 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 3
    • Usefulness 3
    Rationality in using money?

    I see milestone number 3 as the one that attracts the most attention when the cost is up to $82,500. This is a large amount of money and needs more explanation from the team regarding hiring key personnel. I think this is a very good thing to do to prove the reasonableness in using money.

  • 0
    user-icon
    Devbasrahtop
    May 25, 2024 | 3:40 AM

    Overall

    3

    • Feasibility 3
    • Viability 2
    • Desirabilty 4
    • Usefulness 3
    Needs Clearer Execution Plan and Team Verification

    Overall

    The SoloAI proposal presents an innovative and technically sophisticated solution for decentralized data labeling, leveraging advanced technologies such as zkHE biometric verification and AI-driven task matching. While the concept is highly appealing and addresses significant issues in the data labeling industry, the proposal lacks detailed execution plans, verified team member information, and a transparent budget breakdown, which impacts its overall feasibility and viability.

    Feasibility

    The utilization of innovative technologies like EigenLayer for data integrity and zkHE biometric verification is essential to SoloAI's viability. These are creative and theoretically solid ways to guarantee high-quality data from actual people while protecting privacy. The project's practical execution within the suggested timetable and budget is called into question due to its technical complexity and lack of clear implementation stages.


    Viability

    The viability of the project is currently undermined by the lack of verified team member information and specific details about their expertise and roles. While the named team members possess impressive credentials, the consent and active participation of all key personnel need confirmation. Furthermore, the budget allocation is vague and lacks transparency, which further reduces confidence in the project’s successful completion.

    Desirability

    There is a high desire for a solution that addresses the critical issues of data quality and integrity in AI training. SoloAI's approach to using verified human labelers and leveraging decentralized technologies to ensure data quality is compelling and meets a significant market need. The proposal's emphasis on privacy and ethical data labeling also adds to its attractiveness in the current AI landscape.

    Usefulness

    The usefulness of SoloAI to the decentralized AI platform is notable. By providing high-quality, reliable data, SoloAI can significantly enhance the training and accuracy of AI models. The platform's potential to connect AI projects with diverse human inputs and ensure data integrity through advanced verification mechanisms aligns well with the goals of fostering a robust and scalable AI ecosystem.

     

  • 0
    user-icon
    TrucTrixie
    Jun 9, 2024 | 1:37 PM

    Overall

    3

    • Feasibility 4
    • Viability 2
    • Desirabilty 3
    • Usefulness 3
    SoloAI\'s unique feature create valuable product?

    SoloAI is making its mark as the first data labeling platform designed for AI projects. The excitement is that the platform can empower users to get work done, it is true that this cannot happen without AI integration. I commend the team for this intelligence. But creating the most valuable data set for AI is not certain, because it also depends on many other objective factors (accuracy in input data,...).

  • 0
    user-icon
    HenriqC
    Jun 9, 2024 | 2:43 PM

    Overall

    3

    • Feasibility 3
    • Viability 2
    • Desirabilty 4
    • Usefulness 3
    Specialized service with quite complex components

    Usefulness

    Having the described resource in the SNET ecosystem would be useful. However I don’t see it being quite at the top of the platform’s current growth needs. It might help several projects in their service development but not significantly accelerate the widening of the service diversity on the platform. In addition, it will probably take a relatively long time before the service of this proposal will launch. So it is not an essential driver of rapid platform growth right now.

     

    Desirability

    There are definitely many situations where availability of verified human labeling would benefit the process of building AI systems. The project is an insightful way of combining different technologies to solve a long standing pinpoint in the field of AI. The described technology would also contribute to creating fair markets for the task and potentially lead to other useful applications of the same design principles. 

     

    Feasibility

    There are no fundamental hurdles for building this kind of solution. The technological principles mentioned in the proposal are evolving rapidly and enable one to implement the project. However, even though the overview explains the high level implementation plan, after that, I would expect the milestones to focus on the detailed building process of the ultimate product. The implementation roadmap is basically completely missing. Also, there will be high quality turnkey zero knowledge solutions for application builders to be used but I don’t think we are there quite yet.

     

    Viability

    As we are talking about mission critical infrastructure there should be an exceptionally comprehensive and open description on both technological architecture and the team information to demonstrate the viable path to top level security and functionality. With the level of information in this proposal, there is a lot of room for improvement.

     

Summary

Overall Community

3.7

from 10 reviews
  • 5
    0
  • 4
    7
  • 3
    3
  • 2
    0
  • 1
    0

Feasibility

3.4

from 10 reviews

Viability

3.3

from 10 reviews

Desirabilty

3.7

from 10 reviews

Usefulness

3.7

from 10 reviews