Book Image

Blockchain for Business 2019

By : Peter Lipovyanov
Book Image

Blockchain for Business 2019

By: Peter Lipovyanov

Overview of this book

Blockchain for Business 2019 is a comprehensive guide that enables you to bring in various blockchain functionalities to extend your existing business models and make correct fully-informed decisions. You will learn how decentralized applications are transforming numerous business sectors that are expected to play a huge role in the future. You will see how large corporations are already implementing blockchain technology now. You will then learn about the various blockchain services, such as Bitcoin, Ethereum, Hyperledger, and others to understand their use cases in a variety of business domains. You will develop a solid fundamental understanding of blockchain architecture. Moving ahead, you will get to grips with the inner workings of blockchain, with detailed explanations of mining, decentralized consensus, cryptography, smart contracts, and many other important concepts. You will delve into a realistic view of the current state of blockchain technology, along with its issues, limitations, and potential solutions that can take it to the next level. By the end of this book, you will all be well versed in the latest innovations and developments in the emerging blockchain space.
Table of Contents (17 chapters)

Future of AI for smart contracts

The more futuristic concept of AI for smart contracts, which eventually can be used to build and run AI DApps and decentralized autonomous organizations (DAOs) in such a way that they can adapt and evolve to complete tasks with very limited human intervention is also very interesting.

A project called Cortex claims to be the first blockchain to support on-chain AI. They've managed to deploy a couple of AI models on their testnet using techniques such as quantization and compression. Quantization is a concept in machine learning that combines lightweight inference with high performance, which allows AI models to be executed with high levels of accuracy and low memory costs:

The inference process works as follows:

Compression reduces the size and data usage of models. The first model, a Cat or Dog classifier, was originally over 500 MB in...