This article was contributed by John Brooks who works as a tech blogger for Orbeen.
The integration of AI and blockchain technologies offers enhanced data management solutions across multiple sectors. The secure, decentralized nature of blockchain complements AI’s data processing capabilities, proving effective in addressing various data management challenges. From healthcare to supply chain management, these innovations support more reliable and efficient data systems. The continuous advancement in these technologies promises further improvements in large-scale data management practices.
Integration of Blockchain and AI in Key Sectors
Blockchain technology securely stores extensive data sets essential for various sectors. The technology, combined with artificial intelligence, demonstrates its utility in sectors such as healthcare, finance, and energy management. Specifically, blockchain ensures the authenticity, verifiability, and immutability of data critical for AI model training. The AI market, valued at $136.55 billion in 2022, is projected to reach $407 billion by 2027 with a compound annual growth rate of 36.2%.
In smart energy management, blockchain maintains the integrity of data used to train AI models. This is vital to avoid bias and inaccuracies. The transparency and traceability of blockchain enhance the reliability of AI applications in managing large-scale data. For instance, in healthcare, blockchain effectively addresses issues related to data privacy, security, and interoperability. Blockchain protocols allow for decentralized data management, ensuring the robustness and reliability of stored information. Studies have shown improved efficiency, reduced costs, and enhanced democratization of healthcare through this integration.
The finance industry similarly benefits from the intersection of blockchain and AI, where secure data management is paramount. Financial institutions face stringent regulatory requirements, making emerging technologies like AI essential for maintaining data accuracy and security. Blockchain’s decentralized characteristics help mitigate risks related to data integrity and compliance.
Blockchain in Supply Chain and Federated IoT Environments
Supply chain management benefits from the integration of blockchain and AI. A bibliometric analysis of 280 research articles indicated that 42 studies focused specifically on this integration, primarily at a conceptual level. These studies underscore the value of combining blockchain’s traceability with AI’s data processing capabilities, resulting in secure and efficient data management systems.
In federated IoT environments, the CapBlock model, which integrates blockchain, presents a solution for managing data access. CapBlock employs smart contracts for distributed authorization policy evaluation and access credential generation. This model’s feasibility has been validated in the EU IoTCrawler project, aimed at building a secure search engine for large-scale IoT data. This model empowers users to control data sharing, addressing privacy concerns effectively.
Additionally, AI crypto search engines play a pivotal role in this domain. These search engines enhance the ability to analyze and interpret large-scale blockchain data, providing valuable insights. The integration of an AI Crypto Search engine into blockchain systems ensures the efficient handling of extensive data sets. This facilitates better data management across various sectors, including healthcare, finance, and IoT.
Blockchain’s Role in Climate Data Management
Blockchain and AI present complementary solutions for climate data management. Blockchain ensures transparent and immutable recording of environmental impacts, building stakeholder trust. AI processes complex datasets, yielding actionable insights for evidence-based policymaking. However, challenges such as scalability and energy consumption of blockchain systems necessitate continuous technological advancements. Addressing these issues is crucial for the effective integration of these technologies in managing climate-related data.
Polkadot, a prominent blockchain network, uses sharding to address scalability concerns. Sharding breaks large data sets into smaller pieces, allowing parallel processing and accommodating the vast data influx from AI applications. This mechanism supports efficient data management and transaction processing, alleviating typical scalability bottlenecks.
Enhancements in Pharmaceutical and IoT Data Management
In pharmaceuticals, securing the medicine supply chain is critical. Conventional blockchains often struggle with storage capacity and data validation. Integrating AI algorithms helps classify valid data, ensuring only authentic information is stored. This enhances supply chain efficiency, supported by decentralized storage solutions like the InterPlanetary File System and private blockchains such as Hyperledger Fabric.
AI and blockchain integration also counter forgery in managing birth certificates. AI models like K-nearest neighbors, decision trees, logistic regression, and perceptrons classify tampered and legitimate documents. Blockchain securely stores this classified data, with smart contracts validating its authenticity. IPFS integration addresses scalability and response time issues, ensuring secure data management.
Managing large-scale data generated by the Internet of Things (IoT) devices presents unique challenges. A Semantic IoT Middleware (SIM) uses blockchain for enhanced data security and AI feedback to refine operational efficiency. This layered approach ensures data interoperability and user-centric functionality, demonstrating its potential in large-scale IoT data management.
About the author
John Brooks is a tech blogger for Orbeen, he covers the latest in technology and digital trends. With extensive industry experience, John delivers insights that keep readers ahead of the curve.
Last Updated on August 13, 2024 11:20 am CEST
