Fetch.ai

Fetch.AI has built the world’s first adaptive ‘smart ledger’ which enables Autonomous Economic Agents to perform proactive economic activity by adapting to events in the real world in real-time. The network is capable of handling millions of transactions every second, powered by a collective super-intelligence and a unique Useful Proof of Work system. Fetch delivers the missing critical infrastructure for tomorrow’s digital economy.

Fetch.ai’s utility token FET was designed to find, create, deploy and train autonomous economic agents and is an essential part of smart contracts and oracles on the platform. Through the usage of FET, users can build and deploy their own agents on the network. Developers, by paying with FET tokens, can access machine-learning-based utilities to train autonomous agents and deploy collective intelligence on the network.

Validation nodes are also enabled by staking FET tokens, which facilitates network validation and reputation as a result.The Fetch.ai technology stack has four distinct elements, which are: The Agent Framework — provides modular and reusable components that aid in building multi-agent systems. The Open Economic Framework — provides search and discovery functions to agents. The Agent Metropolis — a collection of smart contracts that run on a WebAssembly (WASM) virtual machine to maintain an immutable record of agreements between agents. The Fetch.ai Blockchain — combines multi-party cryptography and game theory in order to provide secure, censorship-resistant consensus as well as rapid chain-syncing to support agent applications.

When it comes to the platform’s core components, there is the learner where each participant is the learner in the experiment, representing a unique private dataset and machine learning system. There is also the global market, which is the result of a collective learning experiment, where the machine learning model is trained collectively by the learners themselves. Next, there is the Fetch.ai Blockchain that supports smart contracts which permit coordination and governance in a secure and auditable way. Lastly, there is the decentralized data layer based on IPFS which enables the sharing of machine learning weights between all of the learners involved.