OpenGradient, an AI research lab, has successfully utilized the Walrus data storage protocol to develop decentralized AI agents. The collaboration between OpenGradient and Walrus aims to address the issue of centralization in AI systems by leveraging blockchain technology. On June 25, OpenGradient announced its integration with Walrus to introduce decentralized AI to the Sui Network (SUI).
By utilizing Walrus’ decentralized storage network, OpenGradient is able to create and operate decentralized AI agents. This integration allows OpenGradient to host more than 100 AI models across various ecosystems, marking a significant step towards making decentralized AI a reality. Matthew Wang, Co-founder and CEO of OpenGradient, emphasized the benefits of empowering users to control their own AI models, highlighting increased privacy, cost-efficiency, and transparency.
“AI isn’t something that belongs to a handful of massive tech companies; it belongs to users and always has. It’s been our mission to make this idea a reality, giving both seasoned AI developers and everyday consumers the ability to create, modify, and own their own AI models,” Wang explained.
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AI models need decentralized data: Walrus Foundation
Decentralized storage has become increasingly essential for AI models that require access to vast amounts of data. Rebecca Simmonds, Managing Executive at Walrus Foundation, emphasized the importance of storage platforms like Walrus for developers of AI solutions.
“The only options that have been available have come with the risks to privacy and control that are inherent to centralized architecture, making it nearly impossible to build truly decentralized models. Our integration with OpenGradient allows us to fill that need, providing a decentralized storage solution for users that puts them back in control of their data,” Simmonds stated.
Through open storage protocols, users can now have visibility into the training data used in AI models. OpenGradient has enabled users and developers to leverage Walrus data storage for building and training their own models.
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