How Datamall Chain from DMC Foundation Drives AI Development as Storage Requirements Go Up?

How Datamall Chain from DMC Foundation Drives AI Development as Storage Requirements Go Up?

With the continuous development of Artificial Intelligence (AI) represented by ChatGPT, AI can not only assist daily life, but also perform huge automatic operations in data analysis, image recognition, AI-Generated Content, natural language processing, intelligent decision-making and other work. The rapid development of AI technology has become one of the important trends in today’s technology domain, profoundly changing people’s way of life and work.

The development and iteration of AI depends on learning and training from massive data. Through the training process, AI can better understand and predict future trends, thus improving the accuracy of its decision-making. At the same time, AI can continuously optimize its own algorithms and decisions and enhance its autonomy and flexibility. So, data is an important foundation and driving force of AI, and the quality and diversity of data directly affect the effectiveness and performance of AI. The security, cost, and storage capacity of data are important factors that affect the progress of AI.

In addition to large-scale AI learning, the current trend is toward AI personalization, where each user can upload their own information to train AI and allow it to provide personalized services for different individuals. Therefore, personal servers will have stronger computing power in the future.

Currently, the mainstream approach is to store data in the cloud storage systems. However, centralized cloud storage systems have some drawbacks, such as data security, data reliability and cost.

Vulnerability of centralized data storage to attacks and destruction. Centralized storage often adopts a centralized server to store a large amount of data. This is prone to hacker attacks or virus infections. Once the server is attacked or destroyed, the entire dataset will be destroyed, causing significant damage to the development of AI.

Low efficiency in data access. Centralized storage requires data to be transmitted through the network, and data access efficiency is often limited by network bandwidth and transmission speed. For large-scale datasets, the time and cost of data transmission are both very high, which will affect the training and application of AI models.

Data privacy issues. Centralized storage centrally stores a large amount of user data in servers, making it difficult to ensure data security and privacy. Besides, some big techs do not obtain user’s consent when collecting user data, which has also caused many privacy breaches.

As an alternative to centralized storage, decentralized storage has shown great potential in addressing the aforementioned problems. Next, we will take the decentralized storage platform Datamall Chain (DMC) as an example to explain the advantages of such solutions.

Decentralized storage can improve data security and reliability. Decentralized storage reduces the risk of single-point failure by storing data on multiple nodes in a distributed manner. The nodes of DMC are distributed in the United States, Singapore, Hong Kong and other countries and regions, thus the risk of single-point is significantly lowered. In addition, decentralized storage adopts redundant storage, which means multiple backups of a single piece of data. Even if one node encounters a problem, the data can still be accessed through other nodes, and data loss can be prevented.

Decentralized storage can reduce costs. Traditional centralized storage systems require a large quantity of hardware equipment and carry high maintenance cost. Although many cloud service providers offer different discounts for enterprise needs, but the prices are still high. Decentralized storage, on the other hand, utilizes idle resources of nodes, which is in line with the concept of “sharing” that is gaining popularity around the world this year, and can reduce hardware equipment and maintenance costs. This in turn can reduce the research and application costs of artificial intelligence technology and drive the rapid development of artificial intelligence technology.

Not only that, DMC also provides two ways to further reduce user cost: ① creating a fair and transparent decentralized storage trading market which enables suppliers  to freely set price and demanders to choose  to freely , and ② turning all storage facilities in the decentralized storage market into sources of storage services for DMC and breaking down barriers between different storage projects to bring prices further down.

Decentralized storage can enhance data sharing and accessibility. During the course of AI development, different organizations and individuals need to share data to achieve better collaboration and innovation. Decentralized storage technology can break down data barriers and make data easier to share and access. This is particularly important for the development of AI, as the quality and quantity of data are important performance factors of AI algorithms performance, and sharing data can improve algorithm performance and accuracy.

In the future, decentralized storage will become more intelligent and automated. Data and resources will be managed more intelligently, and data storage and usage will be optimized in automatic ways. At the same time, decentralized storage will also place greater emphasis on data security and privacy protection and adopt more advanced encryption and privacy protection technologies to ensure data security. With a continuously widening scope of applications, decentralized storage technology will gradually become an indispensable part of AI development.

For more details, please visit the DMC official website.




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