Getting Started with Artificial Intelligence

Getting Started with Artificial Intelligence

by Tom Markiewicz, Josh Zheng
Released April 2018
Publisher(s): O'Reilly Media, Inc.
ISBN: 9781492027799

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Book description

Artificial intelligence techniques are becoming a fundamental component of business growth across a wide range of industries. Enterprise technologies that automate and detect patterns can augment human expertise, empowering both employees and applications to make richer, more data-driven decisions.

Complete with case studies, implementation examples, and a survey of the current landscape, this book serves as an ideal starting point for your journey into enterprise AI.

As a developer tasked with building enterprise-level AI applications, you’re looking to learn about the tools and techniques that will help you get the job done. In this book, authors Tom Markiewicz and Josh Zheng—developer advocates for IBM Watson—explore three of the more common uses for AI in the enterprise: natural language processing (NLP), computer vision, and chatbots.You'll also learn methods for creating a data pipeline that forms the backbone for building these applications.

  • Explore why NLP is the key to mining unstructured data in emails, articles, blog posts, customer support discussions, and other documents
  • Gain practical tips for building chatbots, and learn how these applications are used in the customer support and e-commerce industries
  • Examine capabilities that computer vision brings to your applications, including image classification and tagging
  • Get a high-level guide to data pipelines—the backbone of your AI applications

Table of contents

  1. 1. Introduction to Artificial Intelligence
    1. The Market for Artificial Intelligence
    2. Avoiding an AI Winter
    3. Artificial Intelligence, Defined?
      1. Artificial Intelligence
      2. Machine Learning
      3. Deep Learning
    4. Applications in the Enterprise
    5. Next Steps
  2. 2. Natural Language Processing
    1. Overview of NLP
    2. The Components of NLP
      1. Entities
      2. Relations
      3. Concepts
      4. Keywords
      5. Semantic Roles
      6. Categories
      7. Emotion
      8. Sentiment
    3. Enterprise Applications of NLP
      1. Social Media Analysis
      2. Customer Support
      3. Business Intelligence
      4. Content Marketing and Recommendation
      5. Additional Topics
    4. How to Use NLP
      1. Training Models
    5. Challenges of NLP
    6. Summary
  3. 3. Chatbots
    1. What Is a Chatbot?
    2. The Rise of Chatbots
      1. Natural Language Processing in the Cloud
      2. Proliferation of Messaging Platforms
      3. Natural Language Interface
    3. How to Build a Chatbot
      1. The Messaging Channel
      2. The Backend
    4. Challenges of Building a Successful Chatbot
    5. Best Practices
      1. Tip #1: Introduce Your Chatbot to First-Time Users
      2. Tip #2: Add Variations to Your Responses 
      3. Tip #3: Make a Main Menu That’s Accessible Anywhere
      4. Tip #4: Have Context Awareness
      5. Tip #5: Be Able to Fix Incorrect Inputs
      6. Tip #6: Handle the “I Do Not Understand” Case
      7. Tip #7: Be Careful About Creating a Personality
    6. Industry Case Studies
      1. Autodesk: Customer Support
      2. Staples: Conversational Commerce
    7. Summary
  4. 4. Computer Vision
    1. Capabilities of Computer Vision for the Enterprise
      1. Image Classification and Tagging
      2. Object Localization
      3. Custom Classifiers
    2. How to Use Computer Vision
    3. Computer Vision on Mobile Devices
    4. Best Practices
      1. Quality Training Images
    5. Use Cases
      1. Satellite Imaging
      2. Video Search in Surveillance and Entertainment
      3. Additional Examples: Social Media and Insurance
    6. Existing Challenges in Computer Vision
    7. Implementing a Computer Vision Solution
    8. Summary
  5. 5. AI Data Pipeline
    1. Preparing for a Data Pipeline
    2. Sourcing Big Data
    3. Storage: Apache Hadoop
    4. Hadoop as a Data Lake
    5. Discovery: Apache Spark
      1. Spark Versus MapReduce
      2. Machine Learning with Spark
    6. Summary
  6. 6. Looking Forward
    1. What Makes Enterprises Unique?
    2. Current Challenges, Trends, and Opportunities
      1. Data Confinement
      2. Little Data
      3. Inaccessible Data Formats
      4. Challenges of Ensuring Fairness, Accountability, and Interpretability
    3. Scalability
    4. Social Implications
    5. Summary

Product information

  • Title: Getting Started with Artificial Intelligence
  • Author(s): Tom Markiewicz, Josh Zheng
  • Release date: April 2018
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781492027799