The Foundation of Decentralized AI: A Data Provisioning Layer

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AI Data Provisioning introduce by The Grass project is a decentralized network that aims to revolutionize the way AI datasets are collected and monetized. Co-founded by 0xdrej, the project has grown rapidly, boasting over 500,000 users and positioning itself as a major player in the crypto protocol space. This article dives into the Grass project's background, its mission and vision, and its unique approach to data provisioning for decentralized AI.

I.

Traditionally, access to large, high-quality datasets has been limited. Often, big tech companies monopolize this resource, hindering innovation and creating data wars. Decentralized data provisioning offers a solution to this problem.

How it Works

Imagine a network of individuals contributing data through web extensions. These extensions, used by hundreds of thousands of users, could crawl the public internet and upload snapshots of websites to a shared database. This collaborative approach allows for the creation of unique and valuable datasets that wouldn't be possible through traditional methods.

Benefits of Decentralization

Democratization: Everyone has the opportunity to contribute to and benefit from AI datasets.
Fairness and Transparency: A level playing field is created, fostering innovation and ensuring AI benefits society as a whole.
Unique Datasets: The decentralized approach allows for the creation of datasets not possible elsewhere.

To better understand AI landscape. You can learn more here in our article on this topic: New Paradigm: Understanding the Intersection of AI and Crypto.

II. AI Provisioning product: The Grass Network:

Grass operates as a network of web extensions, with over half a million users crawling the public internet and uploading snapshots of websites to a database. This decentralized approach allows for the creation of datasets that are not possible to create in other repositories like Common Crawl. The Grass project aims to provide a level playing field for users to contribute to and monetize AI datasets, thereby addressing the issue of data wars and monopolization by big tech companies.

What is Grass and How it Works?:

Grass is a decentralized network that allows users to contribute to and monetize AI datasets. The project operates as a network of web extensions, with over half a million users crawling the public internet and uploading snapshots of websites to a database. This decentralized approach allows for the creation of datasets that are not possible to create in other repositories like Common Crawl.

The Data Provisioning Layer for Decentralized AI:

The Grass project aims to provide a data provisioning layer for decentralized AI. This means that users can contribute to and monetize AI datasets in a fair and transparent manner. The project's mission is to democratize access to AI datasets and provide a level playing field for users to participate in the AI ecosystem.

The Mission and Vision of Grass:

The mission of the Grass project is to democratize access to AI datasets and provide a fair and transparent platform for users to contribute to and monetize these datasets. The project's vision is to create a decentralized network that empowers individuals to participate in the AI ecosystem, thereby driving innovation and ensuring that AI benefits society as a whole.

source: Grass.io

III. Monetization and Challenges:

Grass experiments with different business models, focusing on compensating users for their contributions to the network. The project's value pooling mechanism ensures that users are fairly compensated for their contributions, including bandwidth usage and data insights. Grass also addresses challenges like poisoned datasets, where malicious actors manipulate data to influence AI models, by implementing zk-TLS to verify the authenticity of datasets.

Business Models in Decentralized AI:

The Grass project experiments with different business models to compensate users for their contributions to the network. One approach is to pay users for unused bandwidth, which can be used to scrape massive datasets, draw insights from them, and monetize those insights. This model captures a little bit of margin at each step, from the scraping layer to the dataset layer to the insight layer. Another approach is to compensate users for their contributions to AI models, ensuring that they are fairly compensated for their work.

Grass's Approach to Value Pooling:

Grass's value pooling mechanism ensures that users are fairly compensated for their contributions to the network. This mechanism compensates users for the entire vertical, from the scraping layer to the dataset layer to the insight layer. This ensures that users are fairly compensated for their contributions, including bandwidth usage and data insights.

Addressing Challenges like Poisoned Datasets:

Grass addresses challenges like poisoned datasets, where malicious actors manipulate data to influence AI models, by implementing zk-TLS to verify the authenticity of datasets. This mechanism allows users to submit a proof of request, which is then verified by the network's sequencers. This ensures that the data is authentic and has not been tampered with by malicious actors.

User Involvement and Rewards:

Getting involved with Grass is simple, requiring users to download a web extension or the Saga-phone app and create an account. Users can earn rewards by contributing to the network, with the potential for additional rewards as the network grows. The onboarding process is designed to be user-friendly, ensuring a seamless experience for new participants.

IV. Future Plans and Impact:

The Data Provisioning Layer has ambitious plans for 2024, including fully launching onto the network and implementing the proof of request using zk-TLS. The project also plans to decentralize its sequencers and explore the possibility of offering hardware devices for users who want to contribute more to the network. Grass's growth and scalability are key priorities, as the project aims to create a decentralized network that empowers individuals to participate in the AI ecosystem.

The 2024 Vision and Roadmap:

Grass's vision for 2024 is to fully launch onto the network and implement the proof of request using zk-TLS. The project also plans to decentralize its sequencers and explore the possibility of offering hardware devices for users who want to contribute more to the network.

Growth and Scalability:

The Data Provisioning Layer's growth and scalability are key priorities for the project. The network is currently in beta testing, and the project aims to iron out any kinks before fully launching onto the network. The project also plans to explore the possibility of offering hardware devices for users who want to contribute more to the network.

Impact on Decentralized AI:

The Data Provisioning Layer aims to make a significant impact on the future of decentralized AI. The project's mission is to democratize access to AI datasets and provide a fair and transparent platform for users to contribute to and monetize these datasets. By empowering individuals to participate in the AI ecosystem, Grass aims to drive innovation and ensure that AI benefits society as a whole.

Conclusion

About Orochi Network

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