Book Image

Artificial Intelligence Business: How you can profit from AI

By : Przemek Chojecki
Book Image

Artificial Intelligence Business: How you can profit from AI

By: 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)

Cons of using AI

Using Artificial Intelligence solutions can create three risks.

Firstly, the machines may have hidden biases due to the data provided for training. For instance, if a system learns which job applicants to accept for an interview by using a data set of decisions made by human recruiters in the past, it may inadvertently learn to perpetuate their racial, gender, ethnic, or other biases. These biases are hard to detect as they wont appear explicitly, but rather be embedded in the solution where other factors are considered.

The second risk is that, unlike traditional software engineered systems built on explicit logic rules, neural network systems deal with statistical truths rather than literal truths. Thus it much harder or sometimes impossible to prove that the system will work in all cases — especially in situations that werent represented in the training data. Lack of verifiability can be a concern in critical applications, such as controlling...