As a seasoned crypto investor with over two decades of experience under my belt, I must admit that the partnership between FLock and io.net is nothing short of revolutionary. The concept of Proof-of-AI (PoAI) consensus mechanism has the potential to reshape the landscape of AI and Web3 segments.
AI learning platform FLock, not Flock with a lowercase ‘l’, has partnered with io.net to create the first-ever Proof-of-AI (PoAI) verification system for nodes on a distributed computing network. This innovation aims to enhance the efficiency of AI computations across numerous applications.
FLock, io.net announce partnership, tease Proof-of-AI concept
io.net and FLock, a GPU management platform and federated AI learning service respectively, have disclosed plans for a long-term strategic partnership. This alliance is anticipated to offer the AI and Web3 sectors an array of brand-new tools for development and computational purposes.
1/ Exciting collaboration between @ionet and X FLock: a breakthrough 🚀
Working on the creation of a groundbreaking Proof of Artificial Intelligence (PoAI) consensus mechanism.
The purpose? To authenticate the reliability of DePIN nodes within decentralized computing networks.
Discover more about this work-centric Proof of Work, powered by AI.
— FLock.io (@flock_io) August 29, 2024
Specifically, they are joining forces to develop the initial Proof of Artificial Intelligence (PoAI) agreement system for verifying the reliability of nodes functioning within a distributed computing network.
Through PoAI, decentralized physical infrastructure networks (DePINs) can authenticate the reliability of their nodes by performing complex AI training assignments. PoAI is a type of Proof of Work specifically designed for AI, channeling verification resources towards valuable AI projects. This way, nodes can receive block rewards not only from DePIN but also from AI training networks like IO.net and FLock.io.
Jiahao Sun, the founder and CEO of FLock, emphasizes that the upcoming release holds significant relevance for the DePIN, AI, and Web3 sectors.
Trustworthy computing resources are crucial for both AI engineers and end users alike, and Proof of AI (PoAI) serves as the foundation for building such trust. Since compute infrastructure forms the backbone of AI development, it’s essential to address this aspect first. We’re thrilled to collaborate with io.net, a pioneer in its industry, to ensure we deliver top-notch computing resources for our AI endeavors.
The system that ensures the reliability of DePIN nodes using a decentralized and AI-integrated approach features an engine that consistently generates challenges, collects responses, and delivers relevant statistics (like latency, score variations, data accuracy) to io.net nodes for making decisions.
Pushing barriers of AI model training with Web3
Tory Green, CEO and co-founder of io.net, is thrilled about the broad array of possibilities the latest partnership offers for applying Artificial Intelligence in multiple scenarios.
The advent of Proof of AI is expected to bring about significant enhancements in the training and inference processes of AI models across distributed computing systems. It’s likely that GPU node operators and the broader AI/ML development community will embrace Proof of AI warmly, as they anticipate its benefits.
As a crypto investor dabbling in the realm of artificial intelligence, I’ve come to appreciate the significance of synthetic data in model training. However, the task of synthesizing and refining 15 trillion tokens, as seen in LLama3 training, is no small feat. Therefore, FLock Data Generation has devised a clever solution: leveraging idle GPU resources to execute batch inference on Language Models (LLMs) demanded by both the FLock Task Creator and Training Node.
In the long run, it’s essential that distributed AI systems using GPUs perform well for decentralized AI to thrive. However, there are unscrupulous individuals who try to exploit the system by falsely claiming they possess more computational power than they truly have. One typical method is to deceive the network into thinking they have a greater number of computing resources.
As a researcher, I’ve encountered a significant concern: The lack of strong deterrence mechanisms could potentially lead node operators to act unscrupulously in pursuit of network rewards, irrespective of their actual contributions. Verifying the integrity of nodes is a formidable task due to the possibility that malicious actors might fabricate representations of their resources and claim rewards without performing any genuine work.
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2024-08-29 19:19