• Theta Labs, a blockchain video delivery network, has partnered with AI platform FedML to enable collaborative machine learning on content recommendation and generative AI.
• This partnership will focus on the collaborative training of large-scale generative AI models and will allow Theta TV community to contribute personal preferences and compute resources in training and deploying AI models.
• Theta Labs co-founder and CEO Mitch Liu highlighted that the partnership unlocks a new use case for Theta’s edge nodes by unlocking AI and machine learning development at a “distributed, global level.”
Theta Network Announces New Partnership
The blockchain video delivery network Theta Labs announced a new partnership with the artificial intelligence (AI) platform FedML. This partnership is aimed at enabling collaborative machine learning on content recommendation and generative AI across the globe.
Collaborative Training for Generative Models
Through this collaboration, both firms are focusing on the collaborative training of large-scale generative AI models which will allow users from Theta TV community to contribute their personal preferences as well as computing resources in order to train and deploy AI models to enhance personalized recommendations and advertisements through FedML. According to the FedML team, since Theta’s Edge Network is operated by many decentralized nodes across various regions, it makes it an ideal fit for distributed computing required for collaborative machine learning applications.
Unlocking New Use Cases
The co-founder and CEO of Theta Labs, Mitch Liu stated that this new partnership has unlocked a new use case for their edge nodes by unlocking the development of both AI and machine learning at “distributed, global level”. Similarly, FedML’s co-founder Salman Avestimehr also said that this agreement combines two visions in order to enable collaborative AI within Web3 space while mentioning that ad recommendation & generative AI are just two such immediate applications where there is an immediate need for people to contribute their private data into training ML models which they all benefit from.
Benefits of Collaboration
This collaboration between both firms can bring several benefits like improved accuracy in recommendations based on collected user feedbacks & preferences as well as enhanced security when it comes to dealing with sensitive data due to decentralization ensured through distributed nodes operated by Theta’s Edge Network across multiple regions.
Conclusion
Overall, this collaboration between both firms can prove beneficial not only for users but also for developers who are looking forward to integrating newer technologies such as machine learning into their projects without compromising privacy & security issues associated with sensitive data being handled during such process.