Introduction
Chapter 1: How We Got Here
Definitions and Origins of Machine Intelligence
In this section, you will find the definition for machine intelligence (MI) and the key concepts associated with it, like machine and deep learning. You will also find an abbreviated history of MI’s evolution, including an explanation of its recent ascent. After reading this section, you will be conversant in the foundational concepts that underpin MI.
Chapter 2: Separating Hype from Reality
What MI Can Do and Can't Do
In this section, you will find an overview of MI’s capabilities and limitations as they are today. You will read an introduction to MI applications, including in computer vision, natural language processing, robotic process automation, cognitive robotics, and more. After reading this section, you will understand what kinds of tasks MI can be used to address and to evaluate claims about what it can do.
Chapter 3: Considerations for Getting Started
Five Questions to Get Started with MI
In this section, you will find guidance for deciding which form of MI your or your organization may want to pursue based on the goal you hope to achieve, your organization’s risk tolerance, the amount of time you can wait to see results, and the state of your data assets.
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The Machine Intelligence Primer provides a foundational understanding of where MI came from, how it got to where it is today, and where it’s likely going. It explains and dispels common myths that surround MI. It is meant to help executives, practitioners, and curious skeptics alike consider what MI will mean for them and their teams, and create a world that is more efficient, equitable, meaningful, and verdant.
