The two founded Fetch.ai in 2017, and the Cambridge-based startup provides a smart ledger in the form of autonomous economic agents (AEA) whereby economic activity can take place in a decentralised system.
As the world becomes more digitised on a daily basis, it is becoming increasingly difficult for several companies, startups included, to monetise their AI and machine learning algorithms. This is where Fetch comes in. The startup plans to solve this problem with its agent-based model to shorten the distance between the problem and solution.
“Middlemen are the ones building the system because they know as AI and machine learning comes in place they will have a problem, so that’s why they are controlling the machine learning and AI space,” Humayan Sheikh, CEO and co-founder at Fetch.ai told Techworld.
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Centralised systems are what “middlemen” like Amazon are using, which causes a lack of context to enable them to provide the right predictions to customers as they are poorly placed.
A centralised system is a single point or hub to control all activities in an organisation, whereas a decentralised system offers multiple points to control such activities and information.
“Amazon is a middleman, Google is a middleman, Uber is a middleman. All these tech companies, I mean even if you look at WeWork which is a middle man, they don’t own any assets they capitalise and they know when this kind of technology comes into fruition they’ll be out of business, that’s why they’re controlling it,” Sheikh added.
Fetch vs Amazon
“What Fetch is doing is we’re building a world where the things within the economy of things can live, but this is no ordinary world. This is a world that’s able to restructure itself and move the walls and floors around in real-time, so what it presents to any given individual is an optimised view just for them,” Toby Simpson, CTO and co-founder said.
Fetch technology is built to provide the required context and as an AEA, it is designed to easily work with existing systems, devices and databases.
This means that data will be shared across a secure network, and the entire context required is made available without any leaks to a centralised system.
“The reason why Amazon does not have that full context is because Amazon is looking at a particular silo and if they could get the context from Google, which Google wouldn’t share with them, I want to share it with them because I want them to give me the right predictions.
“I want the right products because I don’t want, for example, to be presented with chairs just because I bought one once, and the reason why this happens is because the context is missing,” Sheikh added.
Fetch also has a health agent- a piece of software that represents users’ health data that can also be connected to medical, pharmaceutical and all other information that would be relevant.
“It doesn’t just learn from the past, it needs to predict the future so what we’re enabling is to provide the context while getting paid for it. So you actually unlock revenue potential for everybody not just the big players, and as a human I’m a consumer so I want that revenue to come to me and this enables that,” said Sheikh.
How it works
Often referred to as the ‘ultimate dating agency for value providers,’ the aim of Fetch is to provide the missing context in the market today using digital representatives.
“These digital representatives can represent a piece of hardware, people, infrastructure, software, services and pieces of data and we often talk about the data industry and how it’s not the data we use that’s interesting.
“It’s actually the data that we don’t use, either because we don’t know it’s there or we simply just don’t have the time to go out and find it or the costs to deploy it exceeds its actual value,” Simpson added.
The agents are software-based decentralised systems that can be synchronised as a health agent, travel agent and more. All agents communicate with each other, so in the case of booking travel flights, it will be able to gather the users’ preferences and health data.
It offers high security, so only the user and agent has access to the data that is inputted. The agent also becomes more intelligent as data is included, so things like shopping habits and recommendations can be learned directly from personal data rather than from Google or Amazon.