Fetch.ai secures $40m to build decentralized machine learning tools
The UK-based startup aims to create communication and actions between AI applications, leveraging blockchain technology and FET tokens for transactions.
Fetch.ai, a startup based in Cambridge, UK, has secured $40m in funding to develop its autonomous agents, network infrastructure, and decentralized machine learning tools.
The company aims to create communication and actions between AI applications to make the work produced by them more actionable.
CEO Humayun Sheikh believes that Fetch.ai could have a role to play in the creation of learning models, providing a more equitable and traceable approach to AI by way of distributed ledgers for entities to feed data into those models.
Fetch.ai is built on blockchain technology and has created a FET token that will be used on its platform. Sheikh said that the capital raised will be invested in developing Fetch.ai’s platform as it gears up to launch commercial services later this year. The funding has been provided by DWF Labs, an incubator connected to an entity called Digital Wave Finance, a top 5 trading entity by volume in cryptocurrency.
Fetch.ai’s platform is designed to provide a comprehensive solution for building and deploying peer-to-peer applications with automation and AI capabilities. It seeks to create a new paradigm for developers and entrepreneurs by offering a decentralized approach to machine learning. The platform is being built on a foundation of blockchain technology and will leverage FET tokens to facilitate transactions.
The company’s previous work includes an AI pilot project in 2020 focused on parking solutions. This project used AI to determine free spots in city parking lots and changed pricing. It also rewarded people with free public transport tickets when they chose not to drive at all. Fetch.ai wants to apply similar concepts to other fields by creating services that take results from applications powered by generative AI and turn them into transactions.
One of the major goals of Fetch.ai is to address the divide between the well-capitalized companies in AI and the ones with less funding. Sheikh believes that the ability for people to train models on their own is difficult because it requires a lot of money. Therefore, building a model trained by multiple entities is the solution. He believes that multiple people creating a model with ownership sitting with multiple stakeholders who trained it is a more equitable approach.
While Fetch.ai’s approach is significant, it remains to be seen if companies will have the appetite to be part of it. However, the startup’s exploration of the space is a sign of how the hype around AI might ultimately land in the world of real usage.
Andrei Grachev, Managing Partner of DWF Labs, which provided the funding, said that Fetch.ai’s technical architecture and decentralized approach to machine learning create a new paradigm for developers and entrepreneurs. He added that DWF Labs is thrilled to support Fetch.ai’s growth and development.
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