Fetch.ai decentralized machine learning: collective learning framework
As our healthcare demonstration shows, these algorithms will enable organisations to train machine learning models in new, privacy preserving ways which previously had not been possible
CAMBRIDGE, England (PRWEB)
July 14, 2020
Fetch.ai (http://fetch.ai) today announced the development and release of software demonstrating novel machine algorithms that will enable developers and enterprises to train machine learning models without sharing any underlying data or exposing private or personally identifying information (PII) to any of the individual participants of the system.
The software deployment is the next stage in the progressive release of the Fetch.ai network (FET) which is a Tokenized Open-Source Software stack built on a decentralized network of distributed system operators.
Humayun Sheikh, CEO and co-founder of Fetch.ai commented:
“With this release, we are progressively building functionality into the Fetch.ai network, increasing utility, and developability for application builders to train AI collectively and deploy agent-based software solutions on the Fetch.ai open network”.
Toby Simpson CTO, and co-founder of Fetch.ai continued:
“As our healthcare demonstration shows, these algorithms will enable organisations to train machine learning models in new, privacy preserving ways which previously had not been possible, helping realize our vision of an open, global-scale machine learning network”.
The breakthrough vision of Fetch.ai, a Cambridge-based artificial intelligence company, is to create a decentralized machine learning platform based on distributed ledger technology, that enables secure sharing, connection and transactions based on data globally.
Fetch.ai’s network is based around an open-source technology that any business can operate to gain access to the power of a world-scale AI network, to carry out complex coordination tasks in the modern economy.
The advances in Artificial Intelligence over the past decade have been driven by the revolution in “machine learning” – the ability of computers to perform processes or tasks, using algorithms that “learn” from past experience. This has driven huge improvements in how rapidly and cost-effectively businesses can operate at scale, as well as unlocking new economic opportunities.
One of the current limitations of the Machine Learning revolution is the extent that data can be securely and ethically shared to deliver value for businesses and users. At present, huge data aggregators now control large portions of our online lives, presenting ethical and legal challenges. This data often resides in standalone “silos” where its value cannot be fully realized.
Decentralized Machine Learning enables privacy preserving training on remote data models to enable new forms of collaboration. Fetch.ai is releasing a code module to enable anyone to deliver collective learning solutions within their organisation.
For more information or to discover more about decentralized machine learning, please visit fetch.ai.
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Media assets:
Provided with this press release are the following assets available for your use
Healthcare use case animation –
https://drive.google.com/file/d/1axoeKCrRAB7lx5semIb16cdLkPkQdPDB/view
Demo video –
https://drive.google.com/file/d/1ftpigcYr1UBXtTVVt7sd7L7Hc_NH_23g/view
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ABOUT Fetch.ai:
Fetch.ai is an artificial intelligence lab based out of Cambridge, UK.
Founded in 2018, Fetch.ai delivers a groundbreaking AI and blockchain powered platform that enables emergent solutions to complex problems. It does this by enabling the deployment of complex multi-agent systems (MAS) over a decentralized network and provides tools to enable the delivery of new finance models. Powered by innovations such as the smart ledger, Fetch.ai has digital intelligence at its heart: delivering actionable predictions, instant trust information, enabling the construction of powerful collaborative models, improving efficiencies and streamlining processes. Find out more about Fetch.ai: https://www.youtube.com/watch?v=s34H-yujHIg
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