10 crypto projects enabling the data economy for artificial intelligence

Introduction

With the AI narrative now widely accepted, generating alpha is tougher. Terms like GPUs and AI agents echo repetitively, almost like a broken record. Yet, amidst this frenzy, one critical sector is consistently overlooked: data.

As the lifeblood of AI, data is crucial for training algorithms and enabling them to make informed decisions. This oversight has paved the way for a significant opportunity within the crypto space. Recognizing the importance of data in the AI ecosystem, numerous crypto projects have emerged to bolster the data economy. These initiatives aim to provide the infrastructure and frameworks necessary for collecting, sharing, and monetizing data, thus fostering a more efficient and accessible AI development environment. In this article, we'll explore ten crypto projects that are at the forefront of enabling the data economy for artificial intelligence.

Ocean Protocol

Ocean Protocol aims to democratize the field of AI and data management. It provides a platform where businesses and individuals can trade and manage tokenized data assets throughout the AI model life cycle. By enabling data owners to monetize their data through tokenization, Ocean Protocol not only democratizes data access but also fosters a connection between data providers and consumers using blockchain technology.

At the heart of Ocean Protocol is the concept of datatokens, which transform data into tradeable assets. This allows data owners to maintain privacy and control while monetizing their information. The protocol includes a marketplace—Ocean Market app—where these data assets can be bought and sold, making previously inaccessible private data available for consumption. Additionally, the platform supports the use of private data in AI models through a compute-to-data feature, which ensures data privacy and promotes research and development in AI.

Ocean Protocol operates by linking data assets with blockchain and decentralized finance tools through these datatokens, which signify the right to access specific data sets. Data providers can mint and deploy datatokens to publish their data services, while consumers utilize these tokens to gain access. The datatokens can be listed on the Ocean Market either at a fixed price or via an automated market maker mechanism for price discovery, facilitated by Balancer.

Cheqd

Cheqd is a privacy-preserving payment and credential network that empowers users and organizations to have control over their data and identities. It leverages technologies like Decentralised Identity (DID), Self-Sovereign Identity (SSI), and Verifiable Credentials (VCs) to create Trusted Data markets. Cheqd enables individuals to interact directly while maintaining privacy and without the need for a centralized registry. It allows for the issuance and verification of digital credentials, supports payments for these credentials, and ensures regulatory compliance.

Cheqd works by providing a Credential Service that simplifies the process of issuing and managing digital credentials. It utilizes Decentralized Identifiers and Verifiable Credentials to secure data and enable users to control and share their information securely. Through its payment infrastructure and APIs, cheqd facilitates the creation of Trusted Data markets, allowing for the exchange and monetization of verifiable, portable, and privacy-preserving data.

Cheqd enables artificial intelligence by enhancing data integrity and trust through its infrastructure for Trusted Data markets. By allowing users to own and control their data, cheqd ensures the reliability and authenticity of the information used by AI systems. This data ownership and control framework provided by cheqd contributes to building more trustworthy AI models and applications by ensuring the integrity and accuracy of the data used in AI processes.

DataLake

DataLake is a decentralized recruitment platform that facilitates researchers in finding suitable patients for medical studies. It is a leading Decentralized Science project that utilizes blockchain technology for consent and data access management. DataLake operates through its application, employing its native token $LAKE to incentivize healthcare providers and patients, enabling access to its unique consent collection technology. The platform has collaborated with major pharmaceutical companies globally and contributed data to various AI development projects. Founded by two medical doctors and entrepreneurs, DataLake focuses on ethical access to medical data, ensuring high-quality and unbiased information for research purposes.

By leveraging blockchain technology, DataLake establishes a secure and transparent system for individuals to consent to sharing their medical data. This approach empowers patients to actively participate in research studies while maintaining control over their data through blockchain-verified consent. DataLake's system complies with European legislation such as the GDPR, Data Governance Act (DGA), and the European Health Data Space (EHDS), setting a standard for international data donation and democratizing access to medical data.

Through its $LAKE token on the Ethereum, DataLake enables real-world data transfers and aims to change scientific research and medical studies by providing a secure and efficient platform for data sharing and recruitment.

Federal AI

Federal AI is a project that focuses on reinforcing AI with blockchain technology. The project aims to address the challenges of data integrity, immutability, secure aggregation, and transparent traceability in AI model training environments. Federal AI utilizes the collaborative and decentralized nature of Federated Learning, enabling users to contribute to the training process without the need for expensive hardware. This approach fosters a more privacy-preserving and secure AI model training environment.

By providing data integrity, immutability, secure aggregation, and transparent traceability, Federal AI enables AI applications to access and analyze data more effectively. This is crucial for AI applications that rely on large datasets to train machine learning models and make informed decisions.

Federal AI works by utilizing a decentralized network of users who contribute to the training process. This collaborative approach enables users to participate in the training process without the need for expensive hardware, which can be a barrier for many organizations and individuals. By fostering a more privacy-preserving and secure AI model training environment, Federal AI aims to address the challenges of data privacy and security in AI applications.

Syntropy

Syntropy is focused on transforming the way blockchain data is accessed through its innovative Data Layer protocol. This protocol is designed for efficiency, security, and equitability, enabling a fully decentralized data exchange for developers, organizations, and users. By eliminating reliance on centralized services, Syntropy provides swift and seamless access to both real-time and historical on-chain data. The technology behind Syntropy fosters a dynamic peer-to-peer ecosystem committed to powering the next generation of decentralized applications.

In the context of artificial intelligence, Syntropy's innovative Data Layer protocol plays a crucial role in enabling AI applications. By providing efficient, secure, and decentralized access to blockchain data, Syntropy's platform can support AI algorithms in accessing and analyzing data more effectively. This capability is essential for AI applications that rely on large datasets to train machine learning models and make informed decisions. Therefore, Syntropy's infrastructure can enhance the capabilities of AI systems by facilitating seamless access to the necessary data for training and inference processes.

Synesis One

Synesis One is a platform that connects companies that need quality AI training data with digital workers who can generate the data. It is designed to crowdsource data needed to train a conversational reasoning engine for its sister company, Mind AI, which is building an advanced natural language reasoning engine. The platform uses the blockchain to enable a public voting process, ensuring the provenance of crowdsourced data is fully traceable and auditable. It also uses the Train2Earn model, where contributors are paid for their contributions, and the Train2Earn mobile app is available for download on various platforms.

Synesis One operates by providing a platform where users can contribute their expertise in natural language to train AI models. Users are rewarded in SNS tokens for their valid submissions. The platform offers three roles for users: contributors, validators, and architects. Depending on their chosen role, users stake their SNS tokens or Canon NFTs to participate in the ecosystem.

The platform aims to democratize AI training and make it accessible to everyone, addressing the significant gap in the market where AI needs to be trained by real humans. It also generates real income through AI customers, allowing users to be properly compensated for their work.Synesis One uses NFTs, such as Canon NFTs, which represent unique keywords in the AI training ecosystem. Users can purchase these NFTs on the Canon Marketplace and earn SNS tokens based on the frequency of AI companies accessing Canon NFT holders' keywords, providing passive income opportunities. The platform's goal is to create a fairer, more open, and more cost-effective ecosystem, as it recognizes the importance of teaching AI how to understand humans effectively. It aims to become fully decentralized and governed by a DAO, offering a permissionless, secure, and censorship-resistant platform.

Get grass

Get Grass is a decentralized network that serves as the data layer of AI, providing access to the public web and the necessary data to train AI models. It allows users to earn rewards by sharing their unused network resources, contributing to the training of AI models. Grass is designed to combat data poisoning, empower open-source AI, and make publicly available the origin of data used to train AI.

Grass aims to combat data poisoning, empower open-source AI, and ensure the transparency of the data used to train AI models. By utilizing a layer 2 blockchain, particularly on Solana, Grass strives to create a fair and equitable platform for AI training data. Users can earn points that will eventually be converted into network ownership of Grass, aligning with the platform's commitment to full decentralization.

Users can earn points and later cash rewards by participating in the network, if you want to learn more about the Get Gras points system, read more here. Grass ensures user privacy and security by only scraping publicly available data and not accessing personal information. The platform fosters a community where individuals can actively engage in AI development while earning rewards, making AI training more accessible and inclusive.

Oasis Network

Oasis Network, also known as Rose, is a privacy-enabled blockchain platform that aims to change open finance and a responsible data economy. It combines high throughput and secure architecture to provide a next-generation foundation for Web3. Oasis Network is committed to privacy and data security, ensuring that sensitive information is securely stored and processed. It is designed to enable data tokenization, which can unlock new use cases for blockchain and enable users to take control of their data.

Oasis Network incorporates AI into its platform through partnerships with AI companies like Personal AI and Meta. These collaborations aim to enhance the capabilities of the network while addressing privacy and data protection concerns. The integration of AI and NFTs in Oasis Network holds potential for enhanced data privacy and control.

The platform is built on a distinctive architecture that enables secure data handling, particularly through the use of confidential computing technology such as secure enclaves. Oasis Network's privacy features can redefine DeFi and create a new type of digital asset called Tokenized Data, which can enable users to control the data they share. In terms of artificial intelligence, Oasis Network addresses privacy concerns by developing responsible AI systems that incorporate principles of justice, privacy, and transparency. By creating an infrastructure that supports responsible AI, Oasis Network aims to ensure fair and secure utilization of technology for the benefit of society.

Matrix AI

Matrix AI is a blockchain platform that combines artificial intelligence (AI) and blockchain technology to create a decentralized AI ecosystem. The platform aims to address the challenges of slow transaction speed, lack of security, and limited scalability in traditional blockchain platforms.

In terms of what Matrix AI does, it offers a suite of AI algorithms for data analysis and insights, such as financial fraud detection and business process optimization. It also provides a hybrid consensus mechanism that combines proof of work, proof of stake, and proof of reputation to ensure both security and efficiency.

Matrix AI works by utilizing a decentralized network of users who contribute to the training process. This collaborative approach enables users to participate in the training process without the need for expensive hardware, which can be a barrier for many organizations and individuals. By fostering a more privacy-preserving and secure AI model training environment, Matrix AI aims to address the challenges of data privacy and security in AI applications.

Nimble network

Nimble Network is a decentralized AI protocol that aims to leverage data in a privacy-preserving manner and provide public large-scale AI models to the general public. It builds the fundamental AI infrastructure for fully decentralized data, model, and network ownership. The network achieves this by building a marketplace in which participants—data contributors and model builders—are rewarded to share their data and expertise. Nimble Network uses a composable architecture, enabling ML models and data to be combined and used by AI agents, data providers, and compute resources. It employs a permissionless innovation model, where data ownership is given back to users, and AI capacities are maintained by the network.

The network uses zero-knowledge proofs and privacy-preserving machine learning to build the privacy layer. Nimble Network aims to provide a decentralized framework for data-driven applications, replacing centralized data management with a decentralized setup. This setup allows for the sharing of AI technologies and data across a network of interconnected AI agents, data providers, and compute resources. The network also enables the creation of decentralized DApps, such as decentralized social networks, decentralized social messaging, personalized recommenders, and more.

Conclusion

In the rapidly evolving landscape of artificial intelligence and data management, a new breed of crypto projects is emerging to tackle the challenges of data accessibility, privacy, and monetization. From Ocean Protocol's mission to democratize AI through the tokenization of data, to Cheqd's focus on privacy-preserving credentials and DataLake's innovation in decentralized medical research, these projects represent a significant leap towards a more equitable and efficient digital future.

Each project, with its unique approach and technology, contributes to the overarching goal of creating a data economy that is not only more accessible and secure but also more aligned with the principles of decentralized governance and user control.