Adot and NeuroMesh Enter Partnership to Explore Data Value of Distributed AI Training Protocols

With the rapid development of AI and blockchain technologies, decentralized AI model training becomes possible and has a broad application prospect.NeuroMesh proposes a protocol based on distributed AI model training, which aims to utilize distributed computing resources for efficient model training. In this protocol, the data layer serves as a fundamental component that determines the quality and efficiency of model training. Adot, as a decentralized AI search engine, can provide key support at the data level by virtue of its data aggregation and analysis capabilities in Web3, cryptocurrency, and blockchain domains.

NeuroMesh, a distributed training protocol for large AI models, built by a team of researchers from Oxford, Deepmind, Google, PhDs from Nanyang Technological University, ex-Google, ex-Meta, and ICPC finalists, bridges the gap between the need to train large AI models and distributed GPUs, creating a platform that can be used to anywhere to train AI models of any size. This is AI for everyone.

Adot is a decentralized AI search network built by former Google senior engineers and many experts in AI and search engines. It not only provides users with a more convenient and smarter Web3 content search experience, but also helps developers quickly build personalized search functions.

The collaboration between NeuroMesh and Adot can maximize the value of the data layer to achieve a truly decentralized AI training ecosystem. In this paper, we will dive into Adot’s technical contributions at the data layer and analyze its future value and significance in this agreement.

Data Aggregation and Integration

Adot has a significant advantage in data aggregation by integrating blockchain data, decentralized storage data (e.g., Arweave, Filecoin), and data from centralized platforms (e.g., Twitter, Telegram, Discord, Reddit, etc.) to form a comprehensive and diverse Web3 dataset. This process goes beyond simple data aggregation and involves complex data fusion and de-duplication techniques to ensure data integrity and consistency.

Updating and maintaining the data is also a key aspect, and Adot ensures that the dataset is current and up-to-date through automated scripting and regular manual review. In a rapidly evolving field, timely data updates can significantly improve the training of AI models so that models always make predictions and judgments based on the most up-to-date information.

Data Cleaning and Processing

After data aggregation, data cleansing is a necessary step to ensure the quality of the data. Adot utilizes advanced data cleansing technology to greatly improve the usability and accuracy of the data by removing noise, deleting duplicates and invalid information. Data structuring is another important technology that transforms raw data into a structured, easy-to-analyze format for subsequent model training and analysis.

In addition, Adot combines AI technology for automated data annotation, using machine learning models to initially classify and annotate the data, and then manually reviewing the data through human-computer collaboration to ensure the high quality and accuracy of the annotation. This process not only improves the efficiency of data labeling, but also provides a reliable data foundation for the accurate training of subsequent models.

Data Indexing and Retrieval

Efficient data indexing technology is one of Adot’s major technical advantages. By building a multi-level indexing structure and optimized retrieval algorithms, Adot is able to quickly locate and extract the required information in a huge amount of data. The intelligent search function further improves the accuracy and relevance of data retrieval, ensuring that the training data can be efficiently recalled and utilized.

Efficient data retrieval is especially important in distributed training protocols. In a distributed computing environment, distributed storage and retrieval of data can significantly reduce data transmission delays and improve training speed and efficiency, and Adot’s technological advantages in this area will provide solid technical support for NeuroMesh’s distributed training protocol.

Data Security and Privacy

Data security and privacy protection are particularly important in a decentralized environment, and Adot ensures data security during transmission and storage through encryption and privacy protection. At the same time, it uses decentralized storage technology to store data in different nodes in a distributed manner to further enhance data security and reliability.

This decentralized data management not only improves data security, but also avoids the risks of single point of failure and privacy leakage faced by traditional centralized data storage. For AI model training that requires high security and privacy protection, these technical means of Adot provide a solid guarantee.

Future value and significance of the protocol

Through Adot’s deep participation, NeuroMesh’s distributed training protocol will show great value and significance in the following aspects:

• Decentralized AI training ecosystem: combined with blockchain technology, the protocol can ensure fairness and transparency in the process of sharing and using arithmetic, data and models, avoiding the monopoly and misuse problems brought by traditional centralization. Individuals and organizations around the world can participate in the process, build and share AI models together, and promote the popularization and application of AI technology.
• High-quality data-supported AI models: Rich and diverse data sources and continuously updated datasets enable AI model training based on the most comprehensive and up-to-date data, improving the generalization ability and performance of the models. Through Adot’s efficient data processing and retrieval technology, we optimize the data preprocessing and loading process to improve training efficiency.
• Promote Web3 ecosystem development: High-quality data and AI models will facilitate the development and landing of more Web3 applications, and promote the prosperity of the Web3 ecosystem. Intelligent data support provided by Adot can enhance the data utilization value of Web3 applications and bring more innovation and development opportunities.
• Continuous Innovation and Expansion: The protocol is designed to be flexible and support different scales and types of AI model training, which can adapt to diversified application needs. The open ecosystem will attract more developers and researchers to participate and jointly promote the continuous innovation and progress of the technology.

In conclusion, by deeply integrating Adot’s data aggregation, cleaning, labeling, indexing and retrieval technologies, NeuroMesh’s distributed training protocol will achieve efficient, safe and intelligent AI model training.

In the future, this protocol will not only promote the development of decentralized AI technology, but also inject new vitality and value into the Web3 ecosystem. By attracting the participation of more projects and teams, it will jointly build a diversified and innovation-driven decentralized AI training ecosystem, further promoting technological progress and industry development.

About Adot

Adot is a decentralized search network for AI Internet. It not only provides users with a more convenient and intelligent Web3 content search experience but also assists developers in quickly building their own personalized search functionalities.

Adot’s mission is to build a decentralized search network that all-round surpasses traditional search engines to make all high-quality data openly accessible to every user and developer.

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