Fetch.AI is a decentralised digital world in which useful economic activity can take place. This activity is performed by digital entities that we call Autonomous Economic Agents (AEAs). AEAs can work alone, together, serially or in parallel, and they can represent humans, services, themselves, data and more. AEAs connect to the world through our Open Economic Framework (OEF) which acts as an agent’s senses. The OEF presents a highly tailored world to each individual agent, one that is adapted specifically for it. Underpinning this is the Fetch.AI smart ledger which provides the network’s integrity and delivers consensus through useful proof of work. Fetch.AI are developing the OEF and smart ledger to enable everyone to develop agents. We like to think of Fetch.AI as the ultimate dating system for value providers and those that want that value: connect, and the smart ledger through the OEF will ensure that what you see is precisely what you need to see in order to do business.
Yes, 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. Following the sale, we are focussing our efforts to continue releasing more innovative technology over the coming weeks and months.
Yes, we released the Fetch.AI public test network on 30 April 2019. This means everyone, not just those who took part in the token sale, can access, use, experiment with and develop on the Fetch.AI decentralised network. The release includes a decentralised search for agents and allows individuals to develop in Etch, Fetch.AI’s smart contract language, inside a simple, web-based interface. To find out more about what the testnet enables, please read this blog post.
Yes. All of Fetch.AI’s decentralised technology will be released open source. You can explore the code for yourself by visiting our GitHub repository.
The Fetch.AI Smart Ledger is an entirely new ledger technology that provides the scalability necessary to support millions of transactions per second. We are also developing infrastructure that supports a decentralised digital world and provides a platform for machine learning to deliver accurate predictions to the marketplace. Our technical whitepaper, provides information on the ledger structure and how it functions and further details can be found in our publication outlining the Design of a Scalable Distributed Ledger.
Fetch.AI is developing an unpermissioned ledger system. However, we also envisage operating the ledger in permissioned environments, such as healthcare and IoT settings, to facilitate the bottom-up deployment of autonomous agents. These could be used, for example, to improve medical diagnoses by linking together information from multiple separate data sources, whilst maintaining the privacy of the underlying data.
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 decentralised 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.
The Fetch.AI world is what we'd describe as “loosely tied” to reality: it exists as space, you can position yourself in it, and the concepts of range and direction apply as you would imagine them to. You can, for example, position yourself around London and look towards Cambridge and generally see things (and by things, I mean autonomous economic agents) that are in that direction. However, precisely what you see and exactly where it is positioned varies according to its relevance to you at this time as determined by the network’s collective intelligence. This ability to have a dynamic, somewhat abstract representation of reality where you may be viewed as being in many places depending on your importance to each of your observers, is what we call unreal estate: a representation of the world tailored for you, right now.
Humayun’s involvement was to investigate commercial applications of machine learning and artificial intelligence to modelling commodity markets. He did not have a title and was not part of the technical team, but played a role in the initial commercialisation strategy for the technology on the basis of his background in commodity trading and deployment of trading algorithms. Over the past four years he has been working to implement predictive analytics in the transport and supply chain sectors.
Toby was at DeepMind from 2011 to 2013 and his job title was ‘Head of Software Design’. After 2013 he stayed on as a consultant for a while. The details are available on his LinkedIn profile. His primary influence from an AI/software perspective, though, was from the management and direction of the Creatures series of games in the second half of the 1990s. He learnt how metaphors and other inspiration from nature could be used to manage software complexity and generate general purpose AIs that could evolve to better meet the challenges of the environment that they inhabit. He went on to create a biologically inspired MMOG engine where tens of thousands of autonomous agents interacted with each other to allow rich, complex worlds to merge that could adapt to the needs of their users. This philosophy is a key component of our vision for Fetch.AI .
We don’t believe there is currently a scalable smart ledger project which allows virtual worlds to be deployed and prediction models to be built.
An incomplete list of projects that operate in a similar space, and that could complement or compete with Fetch.AI is shown below:
Our approach is to build our framework on top of our own ‘Smart Ledger’ from the bottom up, with intelligence built into it from the start, and with the tools to create and refine a collective intelligence, and deliver it to all of its users. The autonomous agents that live in the Fetch.AI digital world are able to adapt dynamically to deliver or receive value . For more details about our unique network, please read our technical introduction.
Fetch.AI are working with a growing number of partners. We are excited to announce agreements with several more in the coming months, but the ones we have discussed publicly include:
Trusted IoT Alliance: TIoTA was founded to support the creation of a secure, scalable and interoperable IoT ecosystem and includes major corporations such as Bosch, Cisco and Siemens among its members.
MOBI: As part of the consortium, we are collaborating with companies such as BMW, Bosch and Ford. The group is dedicated to exploring the opportunities of applying blockchain technology for the benefit of the mobility industry, consumers and communities.
Fetch.AI benefit from the assistance of several advisors from a wide range of sectors. To learn more about these individuals, please visit our website.
Together we can thwart the big-tech data grab (The Guardian)
New ways to trade data (The Economist)
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.
We have a community page where you can access our smart ledger and learn how to build autonomous agents. To find out what we're working on, you can visit our Medium page and our blog. For our latest updates, please join us on social media:
If you're interested in working with us, please visit our careers page, where we have a number of opportunities available.
If you would like to invest in Fetch.AI, please email firstname.lastname@example.org.
For all other enquiries, please email email@example.com.