Blockchain Consensus Mining Through AI Model Search
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Solution Overview
Problem
Mining processing in blockchain systems is unproductive and leads to significant power consumption due to repetitive computation without effectively utilizing computational resources.
Innovation Solution
Transform mining processing into productive tasks by integrating AI model development, where nodes compete to solve AI-related tasks and the first to achieve a satisfactory result earns a reward, effectively utilizing high-performance computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mining processing is performed to add blocks to the blockchain, then distributed consensus formation is achieved and block addition is enabled, but enormous power consumption and unproductive computation occur
Solution Approach 1:
The patent converts the harmful waste of computational power in traditional mining into a beneficial resource by having nodes perform productive AI model training tasks during the mining process. The computational resources that would otherwise be wasted are now utilized to train AI models, creating value from what was previously a loss.
Solution Approach 2:
The patent makes the mining process multi-functional by combining block validation with AI model training. Nodes simultaneously perform the traditional function of achieving distributed consensus and the additional function of training AI models, thereby eliminating the need for separate dedicated computing resources.
2Productivity
If traditional mining processing is performed with repetitive hash computation, then block addition is achieved, but computational resources are wasted without productive output
Solution Approach 1:
The patent transforms the harmful waste of computational resources into a beneficial outcome by directing nodes to perform AI model training instead of repetitive hash computation. The same computational power that would be wasted is now producing valuable AI models with practical applications.
Solution Approach 2:
The patent introduces dynamics by allowing nodes to flexibly choose between traditional mining tasks and AI model training tasks based on network conditions and resource availability. This dynamic allocation optimizes resource utilization and adapts to changing system requirements.
3Productivity
If vast computational resources are deployed for mining, then hash value computation is performed, but the processing remains unproductive
Solution Approach 1:
The patent makes computational power multi-functional by enabling it to serve both traditional mining purposes and AI model training simultaneously. The same computation infrastructure that validates blockchain transactions also trains AI models, maximizing the utility of deployed computational resources.
Solution Approach 2:
The patent converts the harmful aspect of excessive computational power deployment into a benefit by directing this power toward productive AI research and development. The enormous computational resources that would otherwise be wasted are now generating valuable AI models and research outcomes.
Data Source
AI summary
The present disclosure relates to an information processing apparatus, an information processing method, and a program that enables mining processing performed in distributed consensus formation of blockchains to be effectively utilized. By replacing mining processing with processing of searching for an AI model that satisfies a predetermined condition, since a searched AI model itself has productivity, a huge amount of resources and enormous power consumption associated with the mining processing can be effectively utilized. The present disclosure can be applied to a blockchain.