What Is Fetch.ai (FET)?
Founded in 2017 and launched via IEO on Binance in March 2019, Fetch.AI is an artificial intelligence (AI) lab building an open, permissionless, decentralized machine learning network with a crypto economy. Fetch.ai democratizes access to AI technology with a permissionless network upon which anyone can connect and access secure datasets by using autonomous AI to execute tasks that leverage its global network of data. The Fetch.AI model is rooted in use cases like optimizing DeFi trading services, transportation networks (parking, micromobility), smart energy grids, travel — essentially any complex digital system that relies on large-scale datasets.
Who Are the Founders of Fetch.ai?
Fetch.ai was founded by Toby Simpson, Humayun Sheikh and Thomas Hain.
Humayun Sheikh is the current CEO of Fetch.ai. He is also the CEO and founder of Mettalex and the founder of uVue and itzMe.
Toby Simpson is the former COO of Fetch.ai, now a member of the Advisory Board. He was also the CTO at Ososim Limited, as well as Head of Software Design at DeepMind.
Thomas Hain is the former Chief Science Officer of Fetch.ai. Before that, he was a co-founder and director of Koemei.
What Makes Fetch.ai Unique?
Fetch.ai’s utility token FET was designed to find, create, deploy and train digital twins 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 digital twins on the network. Developers, by paying with FET tokens, can access machine-learning-based utilities to train autonomous digital twin 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 Digital Twin Framework — provides modular components that help teams build marketplaces, skills, and intelligence for digital twins to connect with.
The Open Economic Framework — provides search and discovery functions to digital twins.
The Digital Twin Metropolis — a collection of smart contracts that run on a WebAssembly (WASM) virtual machine to maintain an immutable record of agreements between digital twins.
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 digital twin 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.
Founded in 2017 and launched via IEO on Binance in March 2019, Fetch.AI is an artificial intelligence (AI) lab building an open, permissionless, decentralized machine learning network with a crypto economy. Fetch.ai democratizes access to AI technology with a permissionless network upon which anyone can connect and access secure datasets by using autonomous AI to execute tasks that leverage its global network of data. The Fetch.AI model is rooted in use cases like optimizing DeFi trading services, transportation networks (parking, micromobility), smart energy grids, travel — essentially any complex digital system that relies on large-scale datasets.
Who Are the Founders of Fetch.ai?
Fetch.ai was founded by Toby Simpson, Humayun Sheikh and Thomas Hain.
Humayun Sheikh is the current CEO of Fetch.ai. He is also the CEO and founder of Mettalex and the founder of uVue and itzMe.
Toby Simpson is the former COO of Fetch.ai, now a member of the Advisory Board. He was also the CTO at Ososim Limited, as well as Head of Software Design at DeepMind.
Thomas Hain is the former Chief Science Officer of Fetch.ai. Before that, he was a co-founder and director of Koemei.
What Makes Fetch.ai Unique?
Fetch.ai’s utility token FET was designed to find, create, deploy and train digital twins 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 digital twins on the network. Developers, by paying with FET tokens, can access machine-learning-based utilities to train autonomous digital twin 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 Digital Twin Framework — provides modular components that help teams build marketplaces, skills, and intelligence for digital twins to connect with.
The Open Economic Framework — provides search and discovery functions to digital twins.
The Digital Twin Metropolis — a collection of smart contracts that run on a WebAssembly (WASM) virtual machine to maintain an immutable record of agreements between digital twins.
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 digital twin 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.