Fetch.ai (FET) Will Unlock 3,390,000 Tokens on December 28th

Fetch.ai's FET, a utility token, is the bedrock for discovering, creating, deploying, and training digital twins.

Fetch.ai, a platform harnessing artificial intelligence, collective learning, and digital twins, is set to unlock 3,390,000 FET tokens on December 28th. This allocation constitutes approximately 0.13% of the currently circulating supply.

As an integral part of the Fetch.ai platform, FET tokens serve as a utility token, enabling users to build and deploy digital twins on the network. These twins are then empowered with machine-learning capabilities, allowing them to autonomously learn and collaborate within the network. Moreover, validation nodes can stake FET tokens to facilitate network validation, enhancing their reputation in the process.

The Fetch.ai platform is underpinned by four distinct technological components. Firstly, the Digital Twin Framework provides modular components for teams to construct marketplaces, skills, and intelligence for digital twins. Secondly, the Open Economic Framework offers search and discovery capabilities for digital twins. Thirdly, the Digital Twin Metropolis comprises smart contracts that maintain an immutable record of agreements between digital twins on a WebAssembly (WASM) virtual machine. Finally, the Fetch.ai Blockchain utilizes multi-party cryptography and game theory to ensure secure, censorship-resistant consensus and rapid chain-syncing for digital twin applications.

The platform also features a learner component, where each participant represents a unique private dataset and machine learning system. Collectively, they contribute to the formation of a global market, which in turn generates a machine learning model that is trained by the collective effort of the learners. Additionally, the Fetch.ai Blockchain supports smart contracts for secure and auditable coordination and governance. Lastly, the platform incorporates a decentralized data layer based on IPFS, enabling the sharing of machine learning weights among all learners involved.