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AI Blueprints

AI Blueprints

By : Dr. Joshua Eckroth , Schoen
5 (14)
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AI Blueprints

AI Blueprints

5 (14)
By: Dr. Joshua Eckroth , Schoen

Overview of this book

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.
Table of Contents (11 chapters)
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10
Index

Method – NLP + logic programming + NLG

Our method for building a natural language question-answering service is straightforward. Three primary components are involved, as shown in Figure 4. Our method will be as follows:

  • The user first provides the query in text or voice. If voice is used, we can make a request to the Google Cloud Speech-to-Text API (https://cloud.google.com/speech-to-text/) to get the text version of the speech.
  • Next, we need to figure out what the user is asking about. There are lots of ways to ask the same question, and we wish to support several different kinds of questions in both example domains. Also, a question may contain internal phrases, or entities, such as the name of a particular Pokémon, or the name of a college course. We will need to extract these entities while also figuring out what kind of question is being asked.
  • Once we have the question and entities, we then compose a new kind of query. Since we are using Prolog...

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