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Book Description

While O’Reilly has identified several trends among enterprise companies for adopting artificial intelligence, we decided to drill down further to learn just how businesses worldwide are planning and prioritizing this work. In a recent survey, we asked respondents about revenue-bearing AI projects their organizations have in production. How might their AI adoption patterns change over the course of the next year?

In this report, data science experts Ben Lorica and Paco Nathan examine the survey’s results to reveal how (and how many) respondents are ramping up AI projects. You’ll also learn how the scope of AI use among companies is quickly expanding into deep learning, human in the loop, knowledge graphs, and reinforcement learning.

You’ll learn:

  • How far companies have increased their budgets to accommodate AI—particularly those further along in the process
  • Why nearly half of the respondents cite lack of data and skilled people as factors that slow down AI adoption
  • How companies use AI for R&D projects, customer service, and IT
  • How many companies are exploring deep learning, knowledge graphs, and reinforcement learning
  • Which tools these organizations have chosen for their AI projects

Table of Contents

  1. Artificial Intelligence Adoption in the Enterprise
    1. Introduction
    2. Survey Respondents
    3. Investing in AI
    4. What Is Holding Back Adoption of AI
    5. Understanding the Skills Gap
    6. How Organizations Are Using AI
    7. Building-Block Technologies
    8. Data Types
    9. Deep Learning and Reinforcement Learning
    10. Risks
    11. Tools for Building AI Applications
    12. Overall Analysis