Book Image

Artificial Intelligence By Example - Second Edition

By : Denis Rothman
Book Image

Artificial Intelligence By Example - Second Edition

By: Denis Rothman

Overview of this book

AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.
Table of Contents (23 chapters)
21
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22
Index

Chapter 3 – Machine Intelligence – Evaluation Functions and Numerical Convergence

  1. Can a human beat a chess engine? (Yes | No)

    The answer is no. Today, the highest-level chess tournaments are not between humans but between chess engines. Each chess engine software editor prepares for these competitions by making their algorithms faster and requiring less CPU. Today, a top chess engine running on a smartphone can beat humans. In human-to-human chess competitions, the level of chess has reached very high limits of complexity. Humans now mostly train against machines.

  2. Humans can estimate decisions better than machines with intuition when it comes to large volumes of data. (Yes | No)

    The answer is no. The sheer CPU power of an average machine or even a smartphone can generate better results than humans with the proper algorithms.

  3. Building a reinforcement learning program with a Q function is a feat in itself...