Blockchain-Validated ML Algorithm Management
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Solution Overview
Problem
The challenge lies in effectively selecting and executing machine learning algorithms due to the numerous alternatives available, each with varying suitability for specific tasks, and the need for more efficient execution and arrangement of these algorithms in complex data environments.
Innovation Solution
A computer-implemented method and system that utilizes a network of machine learning modules, where a machine learning algorithm is configured to approximate a function, and a message handler communicates input and output data, with a blockchain-based transaction system to validate resource consumption and manage communication between modules, enabling the refinement of function approximations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning algorithms are deployed to address diverse challenges, then the system's adaptability and problem-solving capability are improved, but the complexity of selecting and executing the appropriate algorithms increases
Solution Approach 1:
A central manager component is introduced as an intermediary between the diverse machine learning algorithms and the external environment. This manager maintains a registry of available algorithms, receives task requests, selects appropriate algorithms based on task requirements, and coordinates their execution. This mediator approach resolves the complexity by centralizing the selection and coordination logic, allowing the system to handle multiple algorithms without increasing the complexity at the algorithm level.
Solution Approach 2:
The system is segmented into independent functional components: a central manager for coordination, discrete machine learning algorithms for specific tasks, and data stores for information management. Each algorithm operates as an independent module that can be selected and executed based on task requirements. This segmentation allows the system to manage complexity by dividing the overall system into smaller, manageable pieces with well-defined interfaces.
2Productivity
If machine learning algorithms are tightly coupled to specific tasks, then the execution efficiency for particular problems is improved, but the system's flexibility to switch between different algorithms decreases
Solution Approach 1:
The central manager component serves multiple functions: it maintains the algorithm registry, receives task requests, selects appropriate algorithms, coordinates execution, and manages resources. This multi-functional design allows the system to efficiently execute specific tasks through tightly-coupled algorithms while maintaining flexibility to switch between different algorithms by simply changing the task request. The manager's universal capabilities enable both efficiency and adaptability without compromise.
Data Source
AI summary
A computer implemented method of validating use of a computing resource by a an executing requester software module from a plurality of discrete software modules, the method including validating a characteristic of the requester software module; generating a first transaction defining criteria for consumption of the computing resource by the requester software module, the first transaction being encrypted with a private key from a public key/private key pair and being added as part of a block of transactions to a blockchain data structure; generating a subsequent encrypted transaction corresponding to a request of the requester software module to consume the computing resource, the subsequent transaction referring to the first transaction, wherein the subsequent transaction is validated by a transaction miner computing component from a plurality of miners by authenticating the transaction using the public key and verifying compliance with the criteria defined in each transaction.