Leading the AI Data Revolution, How MEMO Opens a New Era of Universal Data Gains
I. Introduction
In recent years, AI technology has been developing rapidly, and the application field has been expanding, extending from Web2 to Web3.In the process of AI development, the core position of data is becoming more and more significant, and it is the key driving force for AI progress. Generative AI, such as ChatGPT, Claude, Gemini, etc., need massive text and image data to learn language and image knowledge in order to generate realistic content.ChatGPT relies on a large amount of Internet text data to realize natural and smooth language interaction; DALL-E relies on millions of image data to generate high-quality images based on text descriptions. Intelligent bodies such as Agents, which make decisions in complex scenarios such as autonomous driving, also rely on learning massive environment, task, and behavioral data to improve the accuracy and efficiency of decision-making. High-quality data is like high-quality raw material, providing key information for AI model training, and its scale and quality directly affect model performance, with large-scale data enhancing generalization capability and high-quality data guaranteeing accuracy and reliability.
II. The AI data track’s hundreds of billions of dollars and Crypto’s opportunities
The booming development of AI has made the AI data track show a huge market size and growth potential. From the perspective of industry dynamics, the value of data in the AI field is becoming increasingly prominent.Reddit’s data licensing agreements with Google, OpenAI and other companies, with a total revenue of $203 million, fully proves the importance of high-quality data to the training of AI models, and also reflects the strong demand for AI data in the market.Twitter is no longer providing a free tweets data API, and has set up different chargeable versions, which shows that it realizes the commercial value of data. This initiative shows that it realizes the commercial value of data, and side by side reflects the huge potential of the AI data market. From the macro market scale, ScaleAI, which focuses on data annotation, has an annual revenue of nearly $1 billion and a valuation of up to $13.8 billion, showing that the economic value of the AI data track is extremely high. Although automated labeling has replaced some manual work, professional personnel are still needed to ensure the quality of labeling, and ScaleAI has succeeded in the field of data labeling by reducing costs through unique business models such as “geographic arbitrage”, which demonstrates the broad prospects of this track. As AI technology continues to develop, the demand for data continues to rise. According to Epoch, AI training will exhaust the Internet’s high-quality audio and video data in the next two years, and the increase in data scarcity will further expand the market size of the AI data track, which is expected to become a key force in the development of the AI industry.
1.Crypto+AI, data network complete body
In recent years, in addition to AI, Crypto is not to be underestimated, after years of industry development, many projects have gradually explored the possibility of Crypto + AI, which is the most leading and close to the actual application of decentralized data management solutions.
In terms of data ownership, under the traditional AI data model, it is difficult for users to take control of their data, and there is a lack of control and revenue. While Crypto technology is based on blockchain, users can use their private keys to encrypt and manage their data, making ownership clear. For example, in some blockchain AI data platforms, user data is encrypted and stored on the chain, and only the user with a private key can authorize access to realize the return of data ownership. Privacy protection is a difficult problem in AI data processing, and cryptographic techniques such as zero knowledge proof (ZK) in Crypto technology play an important role. platforms such as MEMO utilize ZK technology to complete validation without exposing the data itself, and model trainers can also use encrypted data for training, which protects the privacy of data providers. Homomorphic encryption technology also enables ciphertext computation, further enhancing privacy protection. Incentive mechanism is the key to the development of the AI data ecosystem, Crypto technology introduces a token economic system to incentivize users to participate in data production and processing. In the decentralized AI data platform, users can get rewards by contributing data, participating in labeling, providing computing resources, etc., thus effectively promoting data circulation and ecological prosperity.
2. From data storage to AI data management: MEMO
Since its inception in 2017, MEMO has focused on research in the field of decentralized data storage, sharing, analysis and computation. By building a decentralized cloud storage system and applying AI technology, MEMO provides comprehensive solutions to data challenges in AI development. In terms of data storage, it adopts an innovative data tiering mechanism to manage the importance of data in tiers, shorten the data upload and download time, save space on the chain, and improve the cost-effectiveness of storage. Currently, MEMO has realized an EB-level storage scale to meet the needs of large-scale data storage for AI. In terms of data sharing, it establishes a safe and credible data trading market, protects user privacy, promotes the flow of data value, and provides a rich data source for AI model training. In terms of data analysis and computation, advanced technology is utilized to process stored data, provide valuable analysis results, provide strong support for AI model training through distributed computing, and reduce computation costs.
III. Integration of global data through data DID protocols
1.MEMO Data DID
MEMO’s Data DID protocol is crucial in AI data management. The protocol assigns a unique identification to each piece of data, through which every flow, use, and transaction of the data from the moment it is generated can be accurately recorded, thus realizing the full life cycle traceability of the data.
Data traceability is one of the core advantages of the MEMO Data DID protocol. Taking medical data as an example, medical data is given a DID after it is entered into the system, and its usage information is recorded on the blockchain, which helps to standardize and make medical research transparent, and allows for quick traceability in the case of safety issues to safeguard the rights and interests of patients.
In terms of ownership confirmation, the DID protocol makes use of the blockchain’s non-tampering characteristics to make it clear that the data is owned by the user. Users have absolute control over their own data, which cannot be accessed by others without authorization. For example, if a social media user publishes content, with the help of DID protocol, he or she can clearly confirm the ownership and safeguard his or her rights and interests. Encryption and sharing is an important way to maximize the value of data in DID protocol.
Encrypted sharing is an important means of maximizing the value of data with the MEMO Data DID protocol. Through advanced encryption technology, users can share data with specific objects or organizations while protecting data privacy. In the financial sector, companies can utilize the Data DID protocol for encrypted sharing when providing financial data to partners. Partners can only access and use the data within the authorized scope, and the process of data usage will be recorded in the whole process, which ensures the security and compliance of the data.
The Data DID protocol also opens up the possibility of long-term gains for users. Users can reap the financial rewards by sharing and trading their data wisely.
2. MEMO data management system
MEMO uses decentralized storage and management to build a global node network covering more than 50 regions with 50,000 nodes and over 400,000 registered addresses. These nodes are distributed in different geographical locations, forming a decentralized storage architecture.
When a user uploads data, the data is split into multiple segments and stored on different nodes. This storage method not only improves data security and effectively avoids the risk of data loss caused by a single point of failure, but also improves data storage efficiency and scalability. Even if some nodes fail or go offline, other nodes can still ensure data integrity and availability.
In terms of data management, MEMO has cooperated with more than 20 projects, such as Metis and Arkreen, to launch the co-construction program. Utilizing its own DID protocol and node network, it provides a data management and interaction platform for cooperative projects. Cooperative projects can store their data in MEMO’s system and realize efficient management with the help of its tools, and MEMO can also obtain more data resources to enhance its competitiveness. This decentralized management promotes data circulation and trading, and in the MEMO ecosystem, users and enterprises can legally and compliantly trade and share data to maximize the value of data and promote the development of AI technology.
IV. MEMO’s Data Ecosystem Collaboration Program with the first 20+ partners
In order to further promote the development of the AI data field, MEMO has actively laid out and launched a data ecosystem cooperation program. This program aims to integrate the resources of all parties to jointly build a more complete and efficient AI data ecosystem. By working hand in hand with many partners, MEMO hopes to achieve a wider circulation and deeper utilization of data, and provide a constant source of power for the innovation of AI technology.
The first 20 or so partners to join this ambitious program are distinctive in their own right, showing unique strengths and potential in a variety of areas.
Metis has outstanding performance in Layer2, its Hybrid Rollups technology combines the advantages of various capacity expansion solutions, introduces decentralized sequencer pools to reduce the risk of single point of failure, enhance network stability and transparency, and allows users to participate in network revenue sharing through pledges, bringing strong performance and a wide range of users to the MEMO ecosystem.Arkreen focuses on the carbon market, utilizing blockchain technology to ensure the transparency and credibility of carbon transactions. Arkreen focuses on the carbon market, utilizing blockchain technology to guarantee the transparency and credibility of carbon transactions, and cooperating with MEMO to introduce carbon related data to help develop carbon market prediction models and environmental protection strategy tools. .bit has conducted in-depth research in the field of decentralized identity, and its DID technology has been integrated with MEMO system to enhance data security and manageability; Infinitar has innovated in the field of GameFi, combining game and finance, and its cooperation with MEMO can bring a large amount of gaming data to its ecosystem, and promote the application of AI in the field of gaming, such as the development of intelligent gaming assistants.
MEMO’s data ecosystem is rich in resources and synergistic with other leading projects in various fields. These projects have continued to inject new vitality into the MEMO data ecosystem with their in-depth efforts in technology R&D, market expansion, application innovation, etc. In the future, they will continue to play their respective specialties to help the MEMO data ecosystem continue to develop and innovate in the field of AI data, and lead the industry to move towards a higher stage of development.
V. How MEMO allows users to participate in data sharing, processing and obtaining benefits
1. User participation mechanisms
In the MEMO ecosystem, there are rich ways for users to participate. Users can deploy data storage nodes and contribute idle storage resources to support the expansion of the MEMO network, while gaining economic returns. Co-construction of AI databases is also an important way. Users can contribute high-quality data for medical and scientific research, which can help AI model training, and receive points and rewards based on the value and usage of the data.
Under the premise of privacy protection, users can authorize personal data or public data for AI model training to promote AI personalized services and the development of applications in various fields. Data labeling tasks are equally critical, and users get paid for labeling images, text and other data according to rules to provide high-quality samples for model training. In addition, users are rewarded with points through community contributions, such as answering questions, providing technical support, making suggestions, etc., to enhance their status and influence in the community.
In addition, MEMO has officially launched the data system joint construction program on its official website, and users can go to the official website to open the relevant activity-level tasks and receive the corresponding point rewards.
2. Conversion of earnings
Users are able to earn points and rewards by participating in the above data processing tasks, and points are one of the important rewards for users’ participation in data processing tasks. The more data labeling tasks users complete, the higher the quality, the longer and more stable the deployment of data storage nodes, and the more active they are in community contributions, the more points they receive.
In the future, MEMO plans to introduce a Token mechanism, whereby user points can be exchanged for Token in proportion. these Token have economic value and can be circulated in the cryptocurrency market.
With the development of MEMO ecology, the value of Token increases, and users holding Token can share the ecological growth dividends. Users can also participate in the MEMO governance mechanism by virtue of points and Token, exercising voting rights in major decisions such as technology upgrades and cooperation projects, influencing the direction of project development, and creating more revenue opportunities for themselves.
VI. MEMO’s technological advantages and innovations
1. MEMO’s modular data layer
MEMO’s modular data layer is one of the core highlights of the technical architecture, which contains the storage layer, processing layer and DA layer. The storage layer utilizes decentralized storage technology, with tens of thousands of nodes covering the global market. Through redundant storage and data slicing technology, it ensures data safety and reliability, and provides stable storage services for industries with high data standard requirements. The processing layer adopts advanced algorithms to efficiently process data through parallel computing, such as quickly analyzing market research data to provide support for enterprise decision-making.The DA layer guarantees data accessibility at any time through data availability sampling and challenge mechanisms to ensure smooth access to AI model training data.
2.AI techniques used in MEMO network
In the MEMO network, the application of AI technology also runs through all aspects of data processing, providing strong support for achieving efficient data storage, processing and analysis.
In data storage, machine learning algorithms are used to analyze data access and usage patterns, predict storage needs, dynamically adjust storage locations and methods, and improve storage system performance. In data processing, AI realizes intelligent classification, labeling, cleaning and semantic analysis to improve data quality and usability. In data analysis, deep learning algorithm mines data laws and provides decision-making basis for market trend prediction and risk assessment. In the execution of smart contracts, AI monitors in real time to ensure the safe and reliable execution of contracts.
VII. Summary
The MEMO data DID protocol and decentralized data management system guarantee safe storage, efficient circulation and rational use of data. The Data Ecology Cooperation Program has attracted many quality partners to promote the development of AI data field.
In terms of user participation, rich pathways allow users to gain practical benefits and stimulate motivation. On the technical level, the modular data layer and the application of advanced AI technology make data processing efficient, high-quality and secure.
With the continuous development of AI technology, the importance of data will become more and more prominent.MEMO encourages users to actively participate in the construction of the MEMO ecosystem, and jointly explore the infinite possibilities of the AI data economy, to create a smarter, more efficient and fairer digital future.
