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

From reacting to emotions, to creating emotions

Designing a chatbot that reacts to what a user expresses is one thing. But creating emotions during a dialog like a human does requires deeper understanding of how a chatbot manages emotions. Let's start with emotional polysemy.

Solving the problems of emotional polysemy

We will be enhancing the emotional intelligence of a chatbot starting by addressing the issue of emotional polysemy. We are used to defining polysemy with words, not emotions, in the sense that polysemy is the capacity of a word to have multiple meanings. In Chapter 6, How to Use Decision Trees to Enhance K-Means Clustering, we explored the confusion that arose with the word "coach." "Coach" can mean a bus or a sports trainer, which leads to English to French translation issues.

Polysemy also applies to the interpretation of emotions by artificial intelligence. We will explore this domain with two examples: greetings and...