Fetch.ai (FET) is an innovative project that can change the world. It is an innovative platform that connects IoT (Internet of Things) devices and algorithms to enable collective learning. The Fetch.ai is launched on a high-throughput granular ledger; its architecture provides a unique contract capability to deploy ML (Machine Learning)/AI (Artificial Intelligence) solutions for decentralized problem-solving. Open source tools allow users to build different ecosystems (ecosystem infrastructures) and deploy new trading models.
Who is the supportive team behind Fetch.ai?
The Fetch.ai team is dynamic, fast-growing international engineers and forward-thinking technology researchers working on the convergence of blockchain, artificial intelligence, and multi-agent systems. Fetch.ai (FET) is known to have a collective super-intelligence on top of the decentralized economic internet built with a highly scalable next-generation distributed ledger technology. Combined with machine learning, this provides predictions and infrastructure to power the future economy.
The Fetch.ai (FET) team is world-class software engineers and researchers working in multiple fields (such as multi-agent systems, machine learning, economics, cryptography) developing fascinating and promising new technologies. The team collaborates with international, top academics, and corporate partners to further develop their solutions and place them in real-life situations.
The Fetch.ai (FET) team believes its technology will improve the way they communicate, provide a voice, and new opportunities to people, organizations, and the Internet of Things (IoT), effectively democratize the space and improve the lives of citizens.
What is Fetch.ai?
Fetch.ai has a vision of a world where economic activities can take place without the interference of human activity. They try to build a decentralized network of autonomous agents that can be representative of themselves, other individuals, or devices and services. These agents learn and evolve through AI algorithms and can work independently or with other agents to provide possible solutions for complex problems.
Fetch.ai is the platform that aims to connect Internet of Things (IoT) devices and algorithms to enable collective learning. It was started in 2017 by a team based in Cambridge, England.
Fetch.ai (FET) is built on a high-throughput granular ledger and offers proper contract capabilities to deploy machine learning and AI solutions for decentralized problem-solving. These open-source tools need to help users build ecosystem infrastructure and deploy trading models.
Who Are the Founders of Fetch.ai (FET)?
The Fetch.ai (FET) was established by the aims of 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, as well as he is the founder of uVue and itzMe. Toby Simpson is the COO of Fetch.ai. He was also CTO at Ososim Limited and head of software design at DeepMind. Thomas Hain is the chief scientist in Fetch.ai. Before that, he served as the co-founder and director of Koemei.
Three co-founders and four heads are managing Fetch.ai. Humayun Sheikh is the CEO. He has a long history with artificial intelligence. He was one of the early investors in DeepMind, which was later acquired by Google. His past entrepreneurial experience includes artificial intelligence startup itzMe and drone company uVue.
Fetch.ai’s CTO is Toby Simpson, who has more than a decade of experience in the role of CTO at other tech companies. He has also been at DeepMind and served as Head of Software Design.
Third Fetch.ai co-founder and Chief Scientist Thomas Hain holds a Ph.D. from Cambridge University and specializes in Machine Learning. In addition to his role at Fetch.ai, he also serves as a professor at the University of Sheffield.
The leadership team consists of Jonathan Ward (Head of Research), Troels F. Ronnow (Head of Software Engineering), Maria Minaricova (Head of Business Development), and Arthur Meadows (Head of Investor Relations), in addition to the co-founders. Other members of the Fetch.ai team consist of 10 developers, 11 researchers, and 5 administrative staff.
What makes Fetch.ai (FET) unique?
Fetch.ai’s service token FET is designed to find, create, deploy and train autonomous economic agents and is an essential part of smart contracts and oracles on the platform.
Thanks to the use of FET, users can create and deploy their agents on the network. By paying through FET tokens, developers can access machine learning-based utilities to train autonomous agents and deploy collective intelligence.
Verification nodes are also enabled by receiving FET tokens.
The Fetch.ai technology suite has four different elements:
- Tool frame. Provides modular and reusable components that help build multi-agent systems.
- Open economic framework. It provides search and discovery functions to agents.
- Agent metropolis. A collection of smart contracts running in a WebAssembly (WASM) virtual machine to keep an immutable record of agreements between agents.
- Fetch.ai Blockchain. It combines multilateral cryptography and game theory to provide secure, censorship-proof consensus also fast chain synchronization to support broker applications.
Fetch.ai layers
There is a “learner” part, where each participant is the learner in the experiment, representing a unique set of custom data and machine learning systems. Also, there is a global market, which is the result of the collective learning experiment. The machine learning model is a collectively training layer. Then there is the Fetch.ai Blockchain, which supports smart contracts that allow coordination and governance in a secure and auditable way. Finally, there is a decentralized data layer that is IPFS-based. This layer enables the learners to access all the machine learning facilities.
Fetch.ai technology
Fetch.ai technology consists of three main parts: Autonomous Economic Intermediaries (EEAs), Open Economic Framework (OEF), and Fetch Smartbooks. All of them are under the support of the Proof-of-Work consensus model and artificial intelligence machine learning used by the Fetch blockchain.
Autonomous Economic Intermediaries (EEAs) were digital citizens. They are fully enabled to act on behalf of individuals, organizations, and devices. EEAs paired with data sources and hardware systems. They allowed them to navigate the Fetch ecosystem and derive value from its predictive nature and data discovery functions. This issue enables EEAs to use detailed data and forecasting models to find the best solution to real-world problems. They can also learn from their mistakes, which improves their performance over time.
The Open Economic Framework (OEF) is an adaptive simulation and provides maximum connectivity and interaction capacity to EEAs. OEF stores information and uses artificial intelligence to optimize forecasting and support EEAs. EEAs can collect information from the OEF, and node operators receive token rewards for providing reliable and consistent data and services.
Fetch.ai Smart Notebooks use the blockchain as a directed acyclic graph. It is a unique network structure that combines elements of DAG technology. Fetch.ai collects transactions in a sharding scheme and processes them in chains. Unlike traditional shredding processes on Fetch.ai, several transaction lanes can get assigned simultaneously.
Fetch.ai consensus model
Fetch.ai smartbooks are monitors that support, evaluate and control interactions between EEAs in the system. To do this, Fetch.ai uses the uniquely beneficial Proof-of-Work (uPoW) consensus protocol.
The Fetch.ai team believes that this consensus mechanism has several benefits over the traditional PoW mechanism. In traditional PoW, nodes must download each block and add it sequentially to the chain. This matter takes a lot of time and energy. PoW systems can also lead to miner centralization.
Once confirmed by the two nodes, the DAG system will consider any transaction valid and free up computational resources to train the AI.
Machine Learning (ML) and Artificial Intelligence (AI)
Fetch.ai (FET) includes machine learning and artificial intelligence in all three layers of its protocol. They are used to provide trust information at four different layers:
- Confidence in typical transactions.
- Confidence in the information received from other nodes in the network.
- Confident of interested parties based on their history.
Emerging market and data intelligence.
Deep Learning methods helped to implement each of the above layers. Fetch.ai uses process mining, long-short-term memory, and neural networks. Deep learning methods allow Fetch.ai to predict future authenticity by evaluating the past behavior of EEAs in the system, using this natural language processing.
Fetch.ai community
The Fetch.ai team focuses on the technical aspects of the project and allows their community to grow organically. This matter created a smaller but more passionate community of supporters.
Development and Roadmap
Given the amount of scrutiny ICOs are seeing right now, the team to have a clear roadmap with development goals and activities.
The expectations from the project of 2019 are revealed on roadmaps on their website.
FET Token and Mass Sale
The FET token is the trading medium on the Fetch.ai network, allowing EEAs to transact with each other, exchanging FET tokens for services and data or other products. This matter allows cross-machine transactions seamlessly.
FET was initially an ERC-20 token, but later on, the team planned to create a native token. When this native token is released, the ERC-20 will get exchanged at a fixed conversion rate; so these ERC-20 tokens will get burned. The native token will be released as planned in Q4 2019, but this looks like a rough time frame.
The first coin offering of FET took place on February 25, 2019, at 14:00 UTC. It sold out in just 15 minutes after the first promotion.
The FET is built to be infinitely divisible, providing ease of use in even the smallest micro-operations. This issue will be in favor of EEAs to operate and allow the project to keep the coin supply low if desired.
