Decentralised Machine Learning Special Report with Fetch.ai

The advances in Artificial Intelligence over the past decade have been driven by the revolution in “machine learning” – the ability of computers to improve in the delivery of a process or task, using algorithms to “learn” based on large datasets. This has driven huge improvements in how businesses can operate at scale, and at lower cost, as well as opening new areas of the economy. 

However, this revolution has come at a cost, huge data aggregators now control large portions of our online lives, presenting ethical and legal challenges. Furthermore this data often lives in standalone “silos” that organisations struggle to benefit from since advanced machine learning algorithms require data to be trained on.

An emerging paradigm is decentralised machine learning, where a distributed computer network enables many participants to operate, and benefit from the power of machine learning tasks across industries as diverse as: 

  • Financial services
  • Smart cities
  • Mobility
  • Energy networks
  • Logistics and supply chains
  • Shipping  

This series of articles, brought to you by Fetch.ai, explores the potential of decentralised technology in providing AI services to improve industry processes, and simplify the lives of individuals. 

Fetch.ai based in Cambridge, UK, founded by researchers and computer scientists from Deepmind, and the University of Sheffield, and with a number of prestigious research partnerships, including the University of Cambridge, is developing the software to power a decentralised computer network that is being run by a distributed network of network maintainers and devices globally. 

This open-source software stack allows any organisation to build or configure applications on top of a digital representation of the world in which “software agents”, autonomously search, negotiate and transact enabling complex systems with multiple stakeholders to be optimised.

Over the next 12 weeks we will demonstrate use cases, real world implementations and expand on the need and opportunity that decentralised machine learning presents.

For more information or to contact the team about potential projects, visit fetch.ai