Book Image

Democratizing Artificial Intelligence with UiPath

By : Fanny Ip, Jeremiah Crowley
Book Image

Democratizing Artificial Intelligence with UiPath

By: Fanny Ip, Jeremiah Crowley

Overview of this book

Artificial intelligence (AI) enables enterprises to optimize business processes that are probabilistic, highly variable, and require cognitive abilities with unstructured data. Many believe there is a steep learning curve with AI, however, the goal of our book is to lower the barrier to using AI. This practical guide to AI with UiPath will help RPA developers and tech-savvy business users learn how to incorporate cognitive abilities into business process optimization. With the hands-on approach of this book, you'll quickly be on your way to implementing cognitive automation to solve everyday business problems. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will help you understand the power of AI and give you an overview of the relevant out-of-the-box models. You’ll learn about cognitive AI in the context of RPA, the basics of machine learning, and how to apply cognitive automation within the development lifecycle. You’ll then put your skills to test by building three use cases with UiPath Document Understanding, UiPath AI Center, and Druid. By the end of this AI book, you'll be able to build UiPath automations with the cognitive capabilities of intelligent document processing, machine learning, and chatbots, while understanding the development lifecycle.
Table of Contents (16 chapters)
1
Section 1: The Basics
5
Section 2: The Development Life Cycle with AI Center and Document Understanding
10
Section 3: Building with UiPath Document Understanding, AI Center, and Druid

QnA

  1. What are the characteristics of RPA?
    • It requires manual interaction with applications, is repetitive in nature, has standard inputs, is time-consuming, is rules-based, and is structured.
  2. What are the characteristics of an automation opportunity that requires cognitive technologies?
    • Probability in nature, high variability, and unstructured data
  3. What exercises are performed when evaluating potential automation opportunities?
    • A best-fit assessment, a feasibility assessment, and a viability assessment
  4. When building an automation, what wave would we place the following automation into?
    • An invoice processing automation that interacts with 2 applications, 7 screens, 20 process steps, no business variations, and does not require image-based automation.
    • Process complexity is low based on the characteristics provided; the opportunity can be placed in either wave one or wave two of the pipeline, depending on the potential value provided by the opportunity.
  5. Can we proceed...