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Artificial Intelligence Business: How you can profit from AI

Artificial Intelligence Business: How you can profit from AI

By : noelle silver russel, Przemek Chojecki
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Artificial Intelligence Business: How you can profit from AI

Artificial Intelligence Business: How you can profit from AI

By: noelle silver russel, Przemek Chojecki

Overview of this book

We’re living in revolutionary times. Artificial intelligence is changing how the world operates and it determines how smooth certain processes are. For instance, when you go on a holiday, multiple services allow you to find the most convenient flights and the best hotels, you get personalized suggestions on what you might want to see, and you go to the airport via one of the ride-sharing apps. At each of these steps, AI algorithms are at work for your convenience. This book will guide you through everything, from what AI is to how it influences our economy and society. The book starts with an introduction to artificial intelligence and machine learning, and explains the importance of AI in the modern world. You’ll explore how start-ups make key decisions with AI and how AI plays a major role in boosting businesses. Next, you’ll find out how media companies use image generation techniques to create engaging content. As you progress, you’ll explore how text generation and AI chatbot models simplify our daily lives. Toward the end, you’ll understand the importance of AI in the education and healthcare sectors, and realize the risks associated with AI and how we can leverage AI effectively to help us in the future. By the end of this book, you’ll have learned how machine learning works and have a solid understanding of the recent business applications of AI.
Table of Contents (10 chapters)
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Practical AI and how it is done

Artificial Intelligence in business is practical. When you think about neural networks, dont think about abstract mathematical structures, but rather computer systems that need data to learn business processes and how to operate within them.

Data Science is not a real science, its an experimentation domain, where you need to constantly adjust, test, build prototypes from scratch, and rebuild what you have. Its a framework for approaching problems rather than a specific set of tools. This paradigm of using neural networks, statistics on steroids, is what makes AI both practically and theoretically complex, with such a broad range of applications, which were going to cover in the next chapter.

So how Data Science or Artificial Intelligence is currently done? You could split the actual work into two parts, connected strongly with each other:

  • implementation,
  • research.

Implementation phase is focused...

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