AI Blueprints

How to build and deploy AI business projects

Dr. Joshua Eckroth

• Publisher: Packt Publishing
• Year: 2018
• ISBN:
  9781788997973
• Edition: 1
• Topic: Informatica
• Subtopic: Intelligenza artificiale (IA) e semantica

The essential blueprints and workflow you need to build successful AI business applications

Key Features

• Learn and master the essential blueprints to program AI for real-world business applications
• Gain insights into how modern AI and machine learning solve core business challenges
• Acquire practical techniques and a workflow that can build AI applications using state-of-the-art software libraries
• Work with a practical, code-based strategy for creating successful AI solutions in your business

Book Description

AI Blueprints gives you a working framework and the techniques to build your own successful AI business applications. You'll learn across six business scenarios how AI can solve critical challenges with state-of-the-art AI software libraries and a well thought out workflow. Along the way you'll discover the practical techniques to build AI business applications from first design to full coding and deployment.

The AI blueprints in this book solve key business scenarios. The first blueprint uses AI to find solutions for building plans for cloud computing that are on-time and under budget. The second blueprint involves an AI system that continuously monitors social media to gauge public feeling about a topic of interest - such as self-driving cars. You'll learn how to approach AI business problems and apply blueprints that can ensure success.

The next AI scenario shows you how to approach the problem of creating a recommendation engine and monitoring how those recommendations perform. The fourth blueprint shows you how to use deep learning to find your business logo in social media photos and assess how people interact with your products. Learn the practical techniques involved and how to apply these blueprints intelligently. The fifth blueprint is about how to best design a 'trending now' section on your website, much like the one we know from Twitter. The sixth blueprint shows how to create helpful chatbots so that an AI system can understand customers' questions and answer them with relevant responses.

This book continuously demonstrates a working framework and strategy for building AI business applications. Along the way, you'll also learn how to prepare for future advances in AI. You'll gain a workflow and a toolbox of patterns and techniques so that you can create your own smart code.

What you will learn

• An essential toolbox of blueprints and advanced techniques for building AI business applications
• How to design and deploy AI applications that meet today's business needs
• A workflow from first design stages to practical code solutions in your next AI projects
• Solutions for AI projects that involve social media analytics and recommendation engines
• Practical projects and techniques for sentiment analysis and helpful chatbots
• A blueprint for AI projects that recommend products based on customer purchasing habits
• How to prepare yourself for the next decade of AI and machine learning advancements

Who this book is for

Programming AI Business Applications provides an introduction to AI with real-world examples. This book can be read and understood by programmers and students without requiring previous AI experience. The projects in this book make use of Java and Python and several popular and state-of-the-art opensource AI libraries.

AI Blueprints

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Table of Contents

AI Blueprints

Why subscribe?

Packt.com

Foreword

Contributors

About the author

About the reviewer

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

What you need for this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

1. The AI Workflow

AI isn't everything

The AI workflow

Characterize the problem

Checklist

Develop a method

Checklist

Design a deployment strategy

Checklist

Design and implement a continuous evaluation

Checklist

Overview of the chapters

Summary

2. A Blueprint for Planning Cloud Infrastructure

The problem, goal, and business case

Method – constraint solvers

OptaPlanner

Deployment strategy

Continuous evaluation

Summary

3. A Blueprint for Making Sense of Feedback

The problem, goal, and business case

Method – sentiment analysis

Deployment strategy

CoreNLP processing pipeline

Twitter API

The GATE platform

Reddit API

News API

Dashboard with plotly.js and Dash

Continuous evaluation

Retraining CoreNLP sentiment models

Summary

4. A Blueprint for Recommending Products and Services

Usage scenario – implicit feedback

Content-based recommendations

Collaborative filtering recommendations

BM25 weighting

Matrix factorization

Deployment strategy

Continuous evaluation

Calculating precision and recall for BM25 weighting

Online evaluation of our recommendation system

Summary

5. A Blueprint for Detecting Your Logo in Social Media

The rise of machine learning

Goal and business case

Neural networks and deep learning

Deep learning

Convolutions

Network architecture

Activation functions

TensorFlow and Keras

YOLO and Darknet

Continuous evaluation

Summary

6. A Blueprint for Discovering Trends and Recognizing Anomalies

Overview of techniques

Discovering linear trends

Discovering dynamic linear trends with a sliding window

Discovering seasonal trends

ARIMA

Dynamic linear models

Recognizing anomalies

Z-scores with static models

Z-scores with sliding windows

RPCA

Clustering

Deployment strategy

Summary

7. A Blueprint for Understanding Queries and Generating Responses

The problem, goal, and business case

Our approach

The Pokémon domain

The course advising domain

Method – NLP + logic programming + NLG

NLP with Rasa

Logic programming with Prolog and tuProlog

Prolog unification and resolution

Using Prolog from Java with tuProlog

Pokémon in Prolog

Natural language generation with SimpleNLG

A second example – college course advising

Continuous evaluation

Summary

8. Preparing for Your Future and Surviving the Hype Cycle

Always one step ahead

The state of things

Natural language processing

Computer vision

Expert systems and business rules

Planning and scheduling

Robotics

Understanding the hype cycle of AI

The next big thing

Summary

Other Books You May Enjoy

Leave a review - let other readers know what you think

Index

AI Blueprints

Copyright © 2018 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Acquisition Editors: Frank Pohlmann, Suresh Jain, Andrew Waldron

Project Editor: Veronica Pais

Content Development Editor: Alex Sorrentino

Technical Editor: Saby D'silva

Proofreader: Safis Editing

Indexer: Priyanka Dhadke

Graphics: Tom Scaria

Production Coordinator: Sandip Tadge

First published: December 2018

Production reference: 2281218

Published by Packt Publishing Ltd.

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ISBN 978-1-78899-287-9

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Foreword

In retrospect, this book has been in the making for years. Stetson University, where I serve as an Assistant Professor of Computer Science, hosted its first annual hackathon in Spring 2016. The student organizers wanted faculty to give some lectures about programming and practical application development. AI was a hot topic then just as it is now, so I reached into my background in AI, including a PhD in the subject and multiple years of teaching an AI course in colleges, to develop a presentation I called AI/ML IRL, that is, artificial intelligence and machine learning in real life (AI/ML IRL, J. Eckroth, sudo HackStetson presentation, 2016, https://www2.stetson.edu/~jeckroth/downloads/eckroth-ai-irl-stetson-hackathon-2016.pdf). I covered current applications of AI, the fears and promises of AI, and concluded with advice about how to use AI in real-world applications. In this presentation may be found the seeds of the AI workflow developed in Chapter 1, The AI Workflow, and the discussion of the hype cycle in Chapter 8, Preparing for Your Future and Surviving the Hype Cycle.

Around the same time, my colleague and CEO at i2k Connect, Dr. Reid Smith, was awarded the Robert S. Engelmore Memorial Award, sponsored by the Innovative Applications in Artificial Intelligence conference and AI Magazine. Dr. Smith gave a presentation for this award (A Quarter Century of AI Applications: What we knew then vs. what we know now, R. G. Smith, Robert S. Engelmore Memorial Lecture Award, presented at the Twenty-Eighth Conference on Innovative Applications of Artificial Intelligence (IAAI-16), Phoenix, AZ, 15 February, 2016, http://www.reidgsmith.com/2016-02-15_Engelmore_Lecture.pdf), where he discussed numerous examples of successful AI applications and the lessons learned.

Dr. Smith and I discussed our separate contributions about the topic of programming AI applications and came together to write the cover article of the Spring 2017 AI Magazine (Building AI Applications: Yesterday, Today, and Tomorrow, R. G. Smith and J. Eckroth, AI Magazine 38(1): 6-22, Spring 2017, https://www.aaai.org/ojs/index.php/aimagazine/article/view/2709). This article examined a series of important deployed applications that made significant use of AI a...