You're reading from Artificial Intelligence By Example Acquire advanced AI, machine learning, and deep learning design skills

Product type Paperback

Published in Feb 2020

Publisher Packt

ISBN-13 9781839211539

Length 578 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Author (1):

Denis Rothman

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Table of Contents (23) Chapters

Preface

1. Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning

2. Building a Reward Matrix – Designing Your Datasets FREE CHAPTER

3. Machine Intelligence – Evaluation Functions and Numerical Convergence

4. Optimizing Your Solutions with K-Means Clustering

5. How to Use Decision Trees to Enhance K-Means Clustering

6. Innovating AI with Google Translate

7. Optimizing Blockchains with Naive Bayes

8. Solving the XOR Problem with a Feedforward Neural Network

9. Abstract Image Classification with Convolutional Neural Networks (CNNs)

10. Conceptual Representation Learning

11. Combining Reinforcement Learning and Deep Learning

12. AI and the Internet of Things (IoT)

13. Visualizing Networks with TensorFlow 2.x and TensorBoard

14. Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)

15. Setting Up a Cognitive NLP UI/CUI Chatbot

16. Improving the Emotional Intelligence Deficiencies of Chatbots

17. Genetic Algorithms in Hybrid Neural Networks

18. Neuromorphic Computing

19. Quantum Computing

20. Answers to the Questions

21. Other Books You May Enjoy

22. Index

Summary

Emotional polysemy makes human relationships rich and excitingly unpredictable. However, chatbots remain machines and do not have the ability to manage wide ranges of possible interpretations of a user's phrases.

Present-day technology requires hard work to get a cognitive NPL CUI chatbot up and running. Small talk will make the conversation smoother. It goes beyond being a minor feature; courtesy and pleasant emotional reactions are what make a conversation go well.

We can reduce the limits of present-day technology by creating emotions in the users through a meaningful dialog that creates a warmer experience. Customer satisfaction constitutes the core of an efficient chatbot. One way to achieve this goal is to implement cognitive functions based on data logging. We saw that when a user answers "no" when we expect "yes," the chatbot needs to adapt, exactly the way we humans do.

Cognitive data logging can be achieved through...