Everything To Know About Fetch.ai (FET)

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What is Fetch.ai (FET)?

Fetch.ai (FET)

Fetch.AI is a decentralized digital representation of the real world in which autonomous software agents perform useful economic work. To do so, it relies heavily on Machine Learning (ML) and Artificial Intelligence (AI).

Fetch is a decentralised digital representation of the world in which autonomous software agents perform useful economic work. This means that they can perform tasks, such as delivering data or providing services, and are rewarded with a digital currency for their efforts – the Fetch Token. These agents can be thought of as digital entities: life-forms that are able to make decisions on their own behalf as well as on behalf of their stakeholders (individuals, private enterprises and governments for example). Fetch’s digital world is exposed to agents via its Open Economic Framework (OEF) and is underpinned by unique smart ledger technology to deliver high performance, low cost transactions. The ledger delivers useful proof-of-work that builds market intelligence and trust over time – growing the value of the network as it is used. Fetch can be neatly interfaced to existing systems with minimal effort, allowing it to take advantage of the old economy whilst building the new: plug existing data in to Fetch and watch markets spontaneously form from the bottom up.

Who founded Fetch.ai?

Fetch.ai was founded in early 2017, following a merger of two independent companies, itzMe.AI and uVue. itzMe.AI was operating in the field of social networks while uVue was involved in developing control systems for autonomous vehicles.

History of Fetch.ai.

Fetch.ai held a public token sale on Binance Launchpad on 25 February 2019. Due to overwhelming public demand, $6m was raised in just 22 seconds.

Token allocation

From Binance Research (10-8-2019). The foundation and the team both got 20%. And they used their funds as followed:

“As of 1st December 2018, Fetch.AI has used approximately 85% of SAFT funds according to the allocations below:

  • 10% Partnerships
  • 10% Marketing
  • 25% Team
  • 45% Development
  • 5% Professional services
  • 5% Misc

Utility

The Fetch.AI (FET) token is used as the value exchange on the Fetch.AI network. It is the payment method for all transaction fees, and also a refundable method of registering on the ledger, and can be staked to secure the overall network.

Fetch.AI tokens can be used for many purposes, the largest six of which are:

  • Ability to connect agents and nodes to the network: FET is an access deposit token that acts as a form of stake to demonstrate desire to behave appropriately. It modulates the ability for bad actors to flood the network with undesirable nodes or agents due to the escalating cost of doing so. Fetch.AI are performing modelling and simulations on their testnets (both public and private) during the first half of 2019 to determine what this stake should be.
  • Value exchange between agents: The Fetch.AI token is required in order to allow for two agents, regardless of where they are, to perform a value exchange. The Fetch.AI token is infinitely divisible, thereby supporting transactions that have very low monetary value, but in aggregate provide new and profound level of insight and opportunity.
  • Access to search and discovery, and Advertisement Services: Fetch.AI tokens are needed to access, view and interact with the Fetch.AI search engine. This is a space optimized for autonomous digital entities, providing dynamic and autonomous price setting and negotiation.
  • Access to Fetch’s multi-dimensional digital world: Fetch.AI tokens allow agents to view and explore its digital space geographically, semantically or economically. Some of these detailed views involve considerable computing time and communications between many nodes. These more resource intensive operations will require more tokens. Fetch’s AI and ML constantly restructure this world to optimize each agent’s individual view.
  • Ability to access and develop ledger-based AI/ML algorithms: The Fetch.AI token enables development of and access to a broad range of machine learning and artificial intelligence tasks that are available on the ledger. These may be Fetch-developed primary services such as trust and prediction models, or they may be large-scale independently developed services for network users.
  • For exchange into Fetch’s operational fuel: Operation costs in Fetch.AI are decoupled from the Fetch.AI token in a similar way to that of “gas” on the Ethereum network, but with additional functionality designed to increase the stability of such a fuel and look at addressing issues associated with high and low-velocity economies. Fetch’s operational fuel allows access to processor time for contract execution and services for agents.

How does Fetch.ai work?

Fetch.AI uses a combination of Proof-of-Stake (“PoS”) coupled with a protocol to perform work to deliver consensus. New blocks are minted through the PoS protocol with the transaction order being determined by the work carried out between two blocks. This work is recorded on a directed acyclic graph (DAG) that grows between the blocks. The DAG is notarized by the blockchain which removes the need for a coordinator. A leader or committee of leaders is elected in each block epoch using a decentralized random beacon that is based on a threshold signature scheme.

Useful Proof of Work (µPoW™) is a mechanism for reaching consensus in the network by performing computation that delivers results that are useful either to the network itself or to individual users of the network. It reclaims this processing power to deliver a decentralized super-computer that provides trust information, the prediction model and the ability to perform public and private distributed computing, allocating computational work according to capacity. Such work is delivered as packages that can be broken down and distributed across the network’s nodes for execution, with the cost of verifying that the work was done being trivial in comparison to the cost of doing the work itself. Fetch.ai have built a unique consensus system to store the results of µPoW™ and a high-performance, machine-learning oriented virtual machine to perform the computing itself.

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