Meet Rakis: A Decentralized Verifiable Artificial Intelligence AI Network in the Browser
How does Rakis's consensus mechanism ensure deterministic AI outputs?
Rakis's consensus mechanism ensures deterministic AI outputs by using a novel approach based on embeddings and a commit-reveal process. It clusters AI inference results in high-dimensional spaces and reaches consensus based on specified security parameters. This mechanism allows for reliable and transparent AI outputs, even with the inherent randomness in AI algorithms.
How does Rakis address privacy risks in AI inference?
Rakis addresses privacy risks in AI inference by employing a decentralized approach, leveraging the collective computational power of interconnected browsers. This peer-to-peer network model democratizes access to AI capabilities and mitigates privacy risks associated with centralized data storage and processing. Additionally, Rakis uses multiple networks for redundant message delivery and integrates with blockchains for secure storage of inference results and incentivization mechanisms.
What limitations do traditional AI inference systems face?
Traditional AI inference systems often rely on centralized servers, which pose scalability limitations, privacy risks, and require trust in centralized authorities for reliable execution. These centralized models are also at risk of single points of failure and data breaches, limiting widespread adoption and innovation in AI applications.