Is it AI? (AI text Detector)

Project Owner

Is it AI? (AI text Detector)

Funding Requested

$88,500 USD

Expert Review
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Our proposal outlines the development of an advanced AI text detection system for SingularityNET, leveraging transformer-based models for high accuracy across multiple industries. The system will integrate seamlessly with other platform services, feature scalable architecture, and offer a user-friendly API. Committed to open-source principles, the project aims to enhance the platform's capabilities and foster community collaboration, aligning with SingularityNET's mission to democratize AI technology.

Proposal Description

Company Name (if applicable)


How our project will contribute to the growth of the decentralized AI platform

Our AI text detection project can enhance SingularityNET by introducing innovative technology that is currently very sought after due to the boom of Generative AI. Our project addresses urgent needs for efficient information processing and compliance, especially when it comes to textual AI detection.

The core problem we are aiming to solve

The core problem addressed by the proposal is the growing challenge of efficiently processing and analyzing vast amounts of textual data across various sectors. As data generation continues to accelerate, there is a critical need for robust AI-driven text detection technologies that can identify, interpret, and categorize text data swiftly and accurately. This solution not only enhances data usability but also improves decision-making, and supports personalized content delivery. The project aims to meet this need by developing an advanced AI text detection system on the SingularityNET platform, making it accessible to a wide range of users and industries.

Our specific solution to this problem

The proposed solution involves developing an advanced AI-driven text detection system on the SingularityNET platform, leveraging state-of-the-art machine learning models and natural language processing techniques. This system will be capable of accurately detecting, categorizing, and analyzing text from various sources. It will integrate seamlessly with existing AI services on SingularityNET, enhancing the platform's interoperability and utility.

The system will feature a user-friendly interface allowing easy access and customization according to user needs, making it applicable across different sectors such as healthcare, finance, and education. By utilizing decentralized computing resources of SingularityNET, the system will ensure scalability and efficiency, handling growing data volumes as adoption increases.

Furthermore, the project commits to open-source principles, encouraging community collaboration and continuous improvement of the technology. This not only aligns with SingularityNET’s vision of democratizing AI but also fosters an innovative ecosystem where developers can build upon the foundational AI text detection technology, creating specialized applications and services. This approach ensures the platform remains at the forefront of AI technology, driving both economic and technological growth.

Project details

The project aims to develop a comprehensive AI-driven text detection system on the SingularityNET platform. This system will utilize advanced machine learning algorithms and natural language processing (NLP) techniques to detect, interpret, and categorize textual data across diverse applications.

Technical Details:

  1. Machine Learning Models:

    • The core of the system will be trained around deep learning models, particularly transformer-based architectures like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer). These models excel in understanding context and semantics in text, providing a robust foundation for accurate text detection and analysis.
    • We will be using cutting-edge research based on papers that we cite in this proposal and other techniques that are pending publishing.
  2. Natural Language Processing:

    • Advanced NLP techniques will be employed to enhance text parsing, sentiment analysis, keyword extraction, and named entity recognition (NER). This will allow the system to extract meaningful information from raw text data efficiently.
    • Language models will be fine-tuned to handle specific jargon and terminology of various industries, enhancing the system’s adaptability and accuracy.
  3. Integration and Interoperability:

    • The AI text detection system will be designed to integrate seamlessly with other AI services on the SingularityNET platform. APIs will be developed to facilitate data exchange and service interoperability, allowing users to combine text detection with other AI functionalities like image recognition or data analytics.
    • A plugin architecture will be incorporated, enabling third-party developers to extend the system’s capabilities or integrate it into their applications.
  4. Scalability and Performance:

    • Optimization techniques such as quantization and pruning will be applied to models to enhance inference speed without compromising accuracy, making the system suitable for real-time applications.


  5. User Interface and Experience:

    • We will provide a public collab file where any curious community member can test and use the service without having to worry about implementation complexity.
  6. Open-Source and Community Collaboration:

    • The project will adopt an open-source model, publishing all code, documentation, and training datasets. 
    • Regular workshops will be organized to engage the community, gather feedback, and encourage the creation of new applications based on the text detection system.

Outcome and Impact: The successful implementation of this system will not only enhance text data processing capabilities across multiple sectors but also contribute significantly to the AI community by providing a versatile, scalable, and accessible AI tool on SingularityNET, promoting further research and development in AI technologies.

The competition and our USPs


Describe how your solution distinguishes itself from other solutions (if exist) and how it will succeed in the market.

Our AI text detection solution on SingularityNET stands out by leveraging cutting-edge research, ensuring unparalleled accuracy and context sensitivity. It integrates seamlessly with other AI services, enhancing platform interoperability. Unique to our approach is its scalability, utilizing decentralized computing to manage large data volumes. Additionally, our commitment to open-source principles fosters community collaboration, driving continuous innovation. This holistic approach not only advances text detection technology but also aligns with SingularityNET's vision of democratizing AI access and utility.

Our team

Our team members consist of a balanced and pretty diverse group of individuals due to the complexity of the project.

  1. Rojo Kaboti (Project lead): PhD Researcher, Data Scientist, SingularityNet ambassador.
  2. Gledis Zeneli: Backend engineer, Low-level programming.
  3. Safaa Anour: Data Scientist, AI engineer, Back-end engineer.
  4. Rana Meltem: Front-end Engineer
  5. Mohamed Amine Amir: Full-stack engineer.
View Team

What we still need besides budget?


Existing resources we will leverage for this project


Open Source Licensing


AI services (New or Existing)

Is it an AI text?


New AI service


The purpose of this service is to determine whether a certain text is AI-generated or not.

AI inputs

A text

AI outputs

A percentage represents the probability that the text is AI generated and a small paragraph explaining why that percentage was given.

Proposal Video

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

  • Total Milestones


  • Total Budget

    $88,500 USD

  • Last Updated

    18 May 2024

Milestone 1 - API Calls & Hostings


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.


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.


$22,125 USD

Milestone 2 - Initial phase


In this phase of the project we aim to present a literature review of the problem including all the most novel methods published recently including our own. We will also make a report that includes our detailed action plan design patterns data to be used and test benchmarks.


Initial report including all details mentioned in the description.


$13,500 USD

Milestone 3 - MVP development (v0.1)


In this phase we will work on implementing our MVP based on the design and details from the initial report. The MVP can be considered a pre-release or a v0.1. We will also share the MVP with a selected few from the community to get their feedback.


- Code base (privately shared through GitHub). - Public Jupyter notebook for public tests. - Development report.


$20,000 USD

Milestone 4 - MVP development (v0.2)


In this phase we will work on fixing all issues based on the feedback from the previous milestone. We will also work on optimizing the solution for preparation to deploy. We will also share the MVP with a selected few from the community to get their feedback.


- Code base (privately shared through GitHub). - Public Jupyter notebook for public tests. - Development report.


$17,000 USD

Milestone 5 - SingularityNet integration


In this phase we will be making our final tests before integration into the platform. Then we finally integrate with the platform


- Code base (privately shared through GitHub). - Public link and details of the service on the platform. - Development report.


$15,875 USD

Join the Discussion (2)

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  • 1
    May 15, 2024 | 1:34 PM

    Much needed service definitely. This is slightly off topic to this proposal but do you have any experience of how clustering based on diversity of the text content might work when there is AI generated content and human written content around the same topic? As humans are currently much better at coming up with genuine insights in open domains, most of the AI created content tends to center around the same perspectives even if their linguistic expression may be rich. The marginal benefit of a diverse insight is higher than in the case if the insight was not different from others.

    Upvoted by Project Owner
    • 0
      May 16, 2024 | 2:58 PM

      Clustering AI and human-written texts around the same topic is tricky because AI often repeats the same old ideas. To really capture unique insights, we can use smart NLP tools to dig deeper than just the words used and find the real themes. Plus, adding a way to spot the really unique stuff helps us spot fresh, human-like insights in the mix. This approach lets us blend AI's quick processing with the genuine creativity of human thoughts.

Reviews & Rating

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3 ratings
  • -1
    May 19, 2024 | 10:03 AM



    • Feasibility 5
    • Viability 1
    • Desirabilty 2
    • Usefulness 2
    Many similar systems already exist

    The market is jam-packed with AI Text detection services. This service does not highlight any ways in which it would be different than tools that are already out there. This type of technology is already outdated, and the key to success is having some advantage over the others and a viable business model. It is not clear that there is any differentiation of this model.

    May 19, 2024 | 12:49 PM
    Project Owner

    Our clear differentiation is that we are implementing research that we are actively working on and hence is more advanced than most services currently provided. Furthermore, to our knowledge there are no open-source text AI detections out there, most models are closed-source behind a pay-wall with no way to test them (unless you jump through hoops). Furthermore, after our research, most services tested did horribly with LLM-based text besides two services that benchmarked above 50%.

  • 1
    May 18, 2024 | 12:26 PM



    • Feasibility 5
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    Transparent team- possible both implement & test?

    Rojokaboti and colleagues have reported engaging in other proposals. I'm quite impressed with the presentation team even though they seem to be anonymous. I would like to see their full identities when the proposal is finalized.
    I realize that this proposal requires quite high qualifications from the participants because it involves a lot of github programming, Jupyter notebooks, machine learning, NLP,... and when implementing, we must pay close attention to the following factors: technical factors. Is there any possibility for both implementation and testing?

  • 1
    Joseph Gastoni
    May 15, 2024 | 8:55 AM



    • Feasibility 5
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    An AI text detection system for SNET has potential

    Developing an AI text detection system for SingularityNET has potential to be a valuable tool. Careful planning and execution are required to ensure the system's accuracy, scalability, user-friendliness, and clear differentiation from existing solutions. Building a strong community around the open-source project can be key to its long-term success.

    This project proposes developing an AI text detection system on SingularityNET. Here's a breakdown of its strengths and weaknesses:


    • Moderate: The project requires expertise in machine learning and NLP, along with familiarity with SingularityNET development.
    • Strengths: The concept leverages existing techniques and platforms, and the team's research experience is a plus.
    • Weaknesses: Developing a highly accurate and scalable system requires significant resources and expertise.


    • Moderate: Success depends on the system's accuracy, user-friendliness, and competition within the AI text detection landscape.
    • Strengths: The project addresses a growing need and integrates with SingularityNET's existing services.
    • Weaknesses: The open-source approach may make it difficult to capture significant commercial value.


    • High: An accurate, scalable, and user-friendly AI text detection system is desirable across various sectors.
    • Strengths: The project emphasizes cutting-edge research, open-source access, and alignment with SingularityNET's vision.
    • Weaknesses: The specific functionalities and user benefits need to be clearly communicated to target audiences.


    • High: The project has the potential to improve information processing, compliance, and decision-making across various fields.
    • Strengths: The system's interoperability with SingularityNET services and scalability for large data volumes are valuable features.
    • Weaknesses: The long-term impact on user adoption and real-world applications depends on ongoing development and community engagement.

    Besides, the project should consider:

    • Focusing on a specific target audience and demonstrating clear use cases for the text detection system is crucial.
    • Comparing the system's capabilities to existing solutions and highlighting its unique advantages (research focus, open-source, scalability) is important.
    • Developing a user-friendly interface, clear documentation, and tutorials can encourage user adoption and community contributions.

    Here are some strengths of this project:

    • Addresses a growing need for efficient text processing and analysis across various industries.
    • Leverages cutting-edge research and open-source principles, aligning with SingularityNET's vision.
    • Offers a unique combination of accuracy, scalability (decentralized computing), and user-friendliness.

    Here are some challenges to address:

    • The technical complexity of developing a highly accurate and scalable AI text detection system.
    • Competition within the AI text detection landscape and differentiating the project's value proposition.
    • Balancing the open-source approach with potential commercial viability and capturing value from the project.

    By focusing on cutting-edge research, clear differentiation, a user-centered approach, and active community engagement, this project can become a valuable addition to the SingularityNET ecosystem and the broader AI text detection landscape.


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