Machine learning in blockchain

Abstract

The promise and potential of blockchain to drive social impact is enormous. Blockchain will touch every significant industry which people interact with in dayto-day life. Blockchain will enable solutions that are not previously possible. Health sector recently attracted more initiatives than any other industry. Applications for blockchain in health include digital health records exchange and pharmaceutical supply chain management. In many of these areas, blockchain offers a more secure, decentralized and efficient solution than would otherwise be possible. Blockchain and machine learning (ML) technologies are gaining strong momentum and thrust around the world. Blockchain, a disruptive technology, made its big splash with crypto currencies invention and trading. On the other hand, with predictive and descriptive algorithms, ML is making considerable waves in harnessing existing data to identify patterns and gain insights. Congregating the two technologies can only make them super disruptive! Both have the potential to hasten data exploration and analysis as well as intensify transactions security. Additionally, distributed blockchains can be a significant and proven input for ML, which requires big datasets to make quality predictions. It goes without saying that each technology has its degree of complexity, but both artificial intelligence (AI) and blockchain are in situations where they can benefit from each other and help one another. Both these technologies are able to effect and enact upon data in different ways as their combination makes sense, which can take the exploitation of data to new levels. At the same time, the integration of ML and AI into the blockchain, and vice versa, can enhance blockchain's underlying architecture and boost AI's potential. Additionally, blockchain can also make AI more coherent and understandable for tracing and decision-making using ML techniques. Blockchain and its ledger can record all data and variables that go through a decision made under ML. The present chapter focuses on a few standard ML algorithms that are useful in supporting blockchain technology.