AI3 contributors at #AutonomysNetwork , supported by @DAO Labs through the #SocialMining initiative, are advancing decentralized learning by addressing the practical bottlenecks of bandwidth, data growth, and state storage in AI-based decentralized physical infrastructure networks.
Lately, it has been explained in technical discussions that Autonomys domain framework allows AI workloads to be used without putting additional pressure on block validation. Isolating its machine learning work from the main chain means Autonomys solves the problem brought by AI’s high resource use in decentralized transactions.
This separation enables dynamic workload adjustment, aligning with research by Li (2023) that flagged state bloat and historical data overflow as the leading barriers to usable decentralized AI systems. Members of Social Mining note that using the Subspace Protocol, storage can be scaled efficiently all the while ensuring transparency which is important in decentralized learning.
The proposed Proof-of-Training (AI-PoT) domain would handle model training, validation, and rewards, using AI3 as the economic engine. They make it possible to encourage honest use of computing resources and cut down on the need for one central group to supervise everything.
Social Mining in Autonomys Hub is responsible for the research: it records what the community observes and applies it to developing the protocol. The model mixes economic stability with being decentralized, so AI3 can support new AI-based economies as a basic layer.