Nimble Network, a well-known AI protocol, has announced an exclusive integration to provide comprehensive decentralization. The company announced on its X account that Greenfield (BNB Chain’s data storage mechanism) will integrate with Nimble Network to increase decentralization of the AI stack.
Our latest integration with Greenfield from @BNBCHAIN brings us one step closer to fully decentralizing the AI stack.
Nimble will use BNB’s Greenfield data storage system to store models used within our protocol, eliminating another point of centralization in traditional AI.… pic.twitter.com/E7bV3Q0HYS
— Nimble Network (@Nimble_Network) April 12, 2024
Nimble Network Announces Integration with BNB Chain’s Greenfield to Increase Decentralization
This effort will be very beneficial to the platform’s consumers as it promotes decentralization. Additionally, the respective integration will leverage BNB Chain’s data storage mechanism to store models used under Nimble’s protocol. As a result, the step in question eliminates an additional centralization spot in conventional artificial intelligence (AI).
Greenfield offers its consumers decentralized access and management of data. This potentially paves the way for community confidence in the project. Thereafter, the project may witness a significant increase in its acceptance among community members. Furthermore, the integration also enables the secure storage of both the training data and the models on the decentralized forum.
The integration enables decentralized data access, management and storage
The respective secure storage also increases the confidence of the consumers by attracting them with its inclusivity. According to the company, it is prioritizing this approach as a prerequisite for data markets and data provenance. These are some of the notable things that have helped Nimble Network maintain its position.
Apart from that, the latest collaboration with the famous BNB Chain is likely to help raise its status in the market. According to the company, Nimble creates and uses datasets to perform model training. In line with this, Greenfield will make it possible to fairly compensate data providers. Furthermore, model expectations can be better attributed to their real data providers.
