- FLock.io introduces train.flock.io, a web app for decentralized AI training, in its incentivized beta phase.
- Foundry, a key player in decentralized AI, joins FLock.io’s beta program, emphasizing alignment with FLock.io’s mission.
- The platform integrates private data with on-chain rewards, fostering collaboration while preserving data privacy.
- Participants stake FLock.io’s beta token, $FML, to engage in tasks, promoting good behavior and rewarding contributions.
- FLock.io employs blockchain ledger technology to ensure security and integrity, as highlighted in recent research.
- Users can engage as training nodes, validators, or delegators, shaping the development of AI models for diverse applications.
Main AI News:
FLock.io has officially rolled out train.flock.io, its web application for the decentralized AI training platform, as part of its incentivized beta initiative. This cutting-edge platform enables users to reap rewards by staking tokens towards the refinement and training of AI models.
Foundry, a prominent node operator within the decentralized AI realm, has enthusiastically joined the FLock.io incentivized beta program, poised to offer invaluable insights. “Foundry is thrilled to be part of the FLock.io beta, operating both Training and Validator nodes. FLock.io’s mission to decentralize and democratize AI perfectly aligns with Foundry’s commitment to fostering a decentralized infrastructure,” remarked Mike Colyer, CEO of Foundry. “Our collaboration with FLock.io signifies a pivotal moment in our dedication to advancing decentralized AI for future generations.“
FLock.io’s latest web application, train.flock.io, marks a significant stride towards decentralized AI training. By amalgamating private data with on-chain incentives, FLock.io ensures equitable rewards and fosters open collaboration. The platform caters to the demand for tailored AI models from Web3 and Web2 projects, all while safeguarding data privacy by training models sans data exposure. This beta program heralds a substantial leap forward in transparent contribution and on-chain incentivization for data proprietors, model developers, and computational service providers. The overarching objective is to cultivate specialized models tailored to diverse communities, thereby shaping the trajectory of AI model training. FLock.io extends an invitation to the public to partake in shaping the future of AI by engaging in tasks within the platform.
Jiahao Sun, Founder and CEO of FLock, expressed his enthusiasm, stating, “We are delighted to welcome Foundry aboard, significantly expanding our decentralized network and bolstering its stability. Foundry’s unwavering support and early adoption serve as a catalyst for the decentralized AI community. With train.flock.io, we are addressing the mounting demand for bespoke AI models, empowering communities to craft and train specialized models while upholding paramount data protection and security.”
Cryptocurrency incentives play a pivotal role in propelling momentum within open-source, decentralized, and composable AI development, offering a remedy to the financial hurdles often encountered by traditional open-source projects. Participants stake $FML, FLock.io’s beta token, to partake in tasks. Developers are presented with the option to either run automated training/validation scripts or devise bespoke processes to enhance performance. This staking mechanism fosters commendable conduct, doling out rewards or penalties based on user actions. Leveraging blockchain ledger technology, FLock.io ensures the security and integrity of these processes, with recent research from FLock.io researchers spotlighting the platform’s efficacy in thwarting malicious attacks, as detailed in a paper published in the IEEE Transactions on Artificial Intelligence.
Users can interact with the FLock.io network as training nodes, validators, or delegators. Training nodes oversee AI task training and stake tokens for task eligibility, with FLock.io furnishing a training script for expeditious initiation. Validators execute validation scripts to assess models submitted by training nodes, thereby ensuring equitable task allocation, with rewards contingent on FLock.io’s on-chain model consensus. Delegators delegate tokens to validators, augmenting the validation process and indirectly enhancing the network’s efficacy and reward mechanism. Task creation is presently overseen by the FLock.io team but will soon transition to public involvement, enabling users to delineate desired models. These participants are orchestrated by the on-chain rewards mechanism to generate a diverse array of AI models requisite for communities, encompassing AI companions, cryptocurrency trading bots, and a Web3 search engine.
Conclusion:
FLock.io’s partnership with Foundry and the launch of its decentralized AI training platform signify a significant milestone in the market. By combining incentives, privacy, and collaboration, FLock.io is poised to disrupt traditional AI training methods, offering a promising avenue for the development of bespoke AI models across various sectors.
