DML an introduction

Health Oracle Protocol (HOP) founding mission was to find a way to allow decentralized machine learning or machine learning peer to peer. The first usecase was performing sport science on swimmers usin competition and training times. From this data the following question was posed: Can an AI produce a better training programme for swimmers than a human coach?

Each swimmer had a smart stopwatch collecting time data and when wearables came along this data was added. Sure, all the data could be pushed to a database in the cloud but the goal of DML is to perform the learning peer to peer. Is this possible? If it is, it starts with sovereign data. This is the core ingredients of machine learning. For a peer to peer network, at least two peers are required to be connected with permissionless access to join. The goal is to learn continuously, when an improvement is verified then all can be notified. There is no demand to collected all data from day one. Learn from the data available and like a baby becoming a toddler to child to an adult; be patient and allow learning and intelligence to emerge with time. Allow a range of learning techniques to be included, a besearch cycle that will conclude for each peer and network of peers if a better AI has been established. Be open ended; give the learning space to explore new techniques, take leaps of faith in new directions. Not all the time but enough to explore new search spaces, concepts, ideas and computational techniques. Be mindful that intelligence at all scales will be need. Tiny bottom up or top down, give enough information for self organization. Lastly, be mindful that this approach need to be continuously assessed for risks of an AI taking its own path; act like an oracle responding to human.