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Highlights
- Flare Network is proposing a new approach to AI
- This approach seeks the integration of AI and blockchain to usher in more safety
- Blockchain protocols are now making clear dive to AI
In a new research paper, Flare Networks (FLR) introduced a new approach to Artificial Intelligence (AI) that involves the safe and security combination of AI and blockchain.
Flare Consensus Learning Integrates AI
Flare’s AI model, which is dubbed Consensus Learning (CL), is designed to facilitate collaborative AI across different applications. The core focus of the system is to enhance the development of AI models that are very accurate and robust at the same time. Above all, the Flare AI is suitable for the integration of innovative technology in sectors that are data-sensitive including healthcare, finance, and others.
CL assists in improving decision-making processes as well as enhancing overall operational performance and efficiency. Tackling these challenges could guarantee a lower cost for services to the final consumer. In the healthcare sector, this may result in better patient care outcomes, even more accurate financial analysis, or enhanced fraud detection in the finance industry.
Markedly, this marks a milestone and a burgeoning interest in the integration of AI and blockchain. It comes only a few hours after Internet Computer (ICP) broke a record with the world’s first blockchain-based AI represented as a smart contract. The Internet Computer AI runs on DFINITY’s ICP testnet.
Flare AI Push for Decentralization
It is worth noting that CL is a result of a growing emphasis on decentralization. Technology enthusiasts are concerned with having distributed environments, where data and computational resources are spread across multiple devices. Unlike previous implementations of AI and blockchain, CL does not enable access to centralized machine learning (ML) through the blockchain. Rather, the new Flare AI leverages blockchain to create decentralized AI models.
The preference for decentralized models over centralized ML is linked to the fact the latter comes with some inherent risks. Centralized ML relies on a single trusted party and this restrains its use to single enterprise settings and constrain their broader adoption. In the long run, the infrastructure of these centralized MLs opens up the vulnerabilities of the system and ends up leading to potential attacks or system failures.
Flare is confident that the CL AI model will contribute significantly towards combating malicious attacks on blockchain networks. Increased performance, security, data privacy, efficiency, and full decentralization are a few of the benefits touted to be attached to the Flare AI platform.
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