Artificial Intelligence Business: How you can profit from AI

By Przemek Chojecki
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About 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.

Publication date:
August 2020



Were living through a revolution. Artificial Intelligence is changing how we operate in the world and how smooth certain processes are. Just think about going on holidays. Multiple services allow you to find the most convenient flights and best hotels, you get personalized suggestions on what you might want to see, you go to the airport via one of the ride-sharing apps. At each of these steps, some AI algorithms are at work for your convenience.

I’ve started writing this book to fill in the gap. There are plenty of resources to learn machine learning and data science for technical people but no overviews, trends, discussion of applications, or anything at a more abstract level. On the other hand, those technical books are inaccessible to business people who dont want to go into coding themselves and want to stay at a more abstract idea level. The goal of this book is to address both issues and have a book that will be interesting both for data scientists and people with a business background.

Thats why theres no code in this book, but I wasnt refraining from explaining certain more technical trends or discussing general toolset for artificial intelligence. The book is intended as a useful guide rather than a novel, that you have to read from the beginning until the end. Chapters are connected, but feel free to skip them whenever you want.

All in all, after reading this book, youll know recent business applications of AI, understand how machine learning works and what to expect from it, whats trending, and how AI transforms every single business. Whether youre running a business, work at a large enterprise or in the public sector, this book will give you an overview of Artificial Intelligence as it is practiced today. I finish by discussing how we should integrate AI into our society, what are the risks of AI and how we can use AI to our benefit in the future.


Why Artificial Intelligence

Executive Summary

Artificial Intelligence is used in business through machine learning algorithms. Machine learning is a part of computer science focused on computer systems learning to perform a specific task without using explicit instructions, relying on patterns and inference instead.

Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data, rather than by receiving explicit programming instructions (if-thenloops). The algorithms improve over time with new data coming in, learningthrough examples.

Machine learning is primarily used in:

  • predictions: what will happen,
  • prescriptions: what should be done to achieve goals,
  • descriptions: what happened.

There are three main types of machine learning algorithms: supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning uses training data and feedback from humans to learn the relationship of given inputs to a given output (for example, how the inputs dateand salespredict customerspreferences). Use it if you already know how to classify the input data and the type of behavior you want to predict, but you want to do it on new data.

Unsupervised learning explores input data without being given an explicit output variable (for example, explores customer sales data to identify patterns and classify them). Use it when you want to classify the data, but youre unsure how to label the data yourself, or you want to discover hidden patterns.

Reinforcement learning learns to perform a task by trying to maximize rewards that you prescribe for its actions (for example, maximize returns of an investment portfolio). Use it when you have limited training data, and you cannot clearly define the end goal, or you want to explore possibilities without assuming what the solution might be.

The most common framework for doing machine learning is Python as a programming language. Experiments with machine learning models usually require access to powerful computers to trainalgorithms. Thats why the additional cost of doing AI is the cost of the cloud when data scientists train their models. Those can range from a couple of hundred dollars per month to millions of dollars, depending on how heavy is the data and machine learning architecture. For most businesses the cost wont exceed a couple of thousand dollars per month unless they want to invest heavily in AI capabilities and train their own models, rather than mostly use pre-trained, open-source solutions.

The most common architecture for machine learning algorithms is neural networks. You can think of them as Lego blocks of different sizes and colors that you can mix to build a specific construction. The basic parameter of a neural network is how many layers it has and how those layers interact with each other.

Deep learning is a subfield of machine learning which focuses on neural networks with at least 3 layers. Deep learning is the actual reason why AI is so popular today, as its applications in image or voice recognition are far better than classical methods. Neural networks combined with enough computing power give outstanding results on real-world data.

Big datais another buzzword used in the last decade often. Big data never had a proper definition, always meaning having more data than is possible to process using a single personal computer. Thats why what we today understand as big data (petabytes of data) is far away from used to be big data just 10 years ago (terabytes) and how it will change in the next 10 years (exabytes).

As data is crucial for machine learning algorithms, big datais coming back in organizations as a fundamental term to explore AI capabilities. Machine learning requires that the right set of data be applied to a learning process. You dont need big data to use machine learning algorithms, but big data can help you improve the accuracy of your algorithms.

Thats why often its not necessarily true that you need a lot of data to start experimenting with AI. Especially with the raise of reinforcement learning and techniques like one-shot learning,AI is within reach for every single organization. The first step to benefit from AI is to prepare data by cleaning it and sorting it by human coworkers. Then machine learning engineers and data scientists will be able to take care of the rest.




Voice Recording


Speech Recognition



Image Recognition

Recipe Ingredients

Customer Reviews

Food Recommendations

Historical Market Data

Future Market Data

Trading Bots

Drug chemical properties

Treatment efficacy

Pharma R&D

Transaction details

Is transaction fraudulent?

Fraud detection

Purchase history

Future purchase behavior

Customer retention



Face Recognition

Car locations and speed

Traffic Flow

Traffic Lights


Artificial Intelligence Paradigm

Though it might seem like weve come a long way in the last ten years, which is true from a research perspective, the adoption of AI among corporations is still relatively low. According to some studies, only around 20% of companies are experimenting with AI. Most of the companies have only started to dabble with artificial intelligence.

This new era of information depends heavily on the knowledge, and were currently missing a lot of experts.

Theres a lot of fear and hype around AI, and its crucial to have as many people as possible to know about AI in order to understand whats possible and whats not. Only an educated AI-wise society will be able to adopt the technology fully. The goal of this book is to make it happen sooner than later.

At business level decision-makers, project managers and executives need basic knowledge, understanding of the machine learning paradigm, which would allow them to apply practical algorithms to real use cases, driving business, and growing sales.

On the other hand, trust is a crucial ingredient in any system. Thus explainable AI, being able to explain why these automation systems, machine learning architecture, act in this way, will allow us to understand how its going to affect all of us: co-workers, citizens, humans. For that purpose, we need to think about re-education when it comes to our organizations and implementing AI elements in schools from the very start of education.

AI will mostly enhance what we do on a daily basis. Its not going to be full automation most of the time, but an augmentation of how we do certain tasks and how we work. This way of collaboration will also require rewiring of how we think of machines.

Thats why we also need to think about regulations. AI is the atomic energy of our times, and we can either use it to produce bombs or use it to produce energy. The standard paradigm of computing is based on if-thenloops. Coders give instructions to computers, supervising every single step of computing. Machine learning changed that completely. Coders dont have to code every single step to make the computer work. They can just build a general architecture, like a specific neural network, and supply data in order to trainthis architecture - that is, let the computer system self-tune as it sees fit by analysing data. This approach is more similar to teaching a child a particular task or introducing a junior coworker to a particular business process for the first time. And as it is the case with children, it often takes time for them to grasp the concept fully. Its similar to machines. They need retraining and rebuilding parts of their architecture to excel at a given task.

Because of this shift of paradigm, it became possible to automate more tasks and business processes than ever before. You dont have to program every single step of the process, predicting at each step what might happen and how to react to that. You can leave many of those details to algorithms, letting them see data and decide for themselves in each case how it should be solved.

Of course, thats theory. In practice, things can get messy. AI is not a magic wand, and it doesnt solve any problem you throw at it. You need good preparation to really benefit from artificial intelligence. This includes:

  1. Clearly describing what business process you want to automate or optimize.
  2. Defining what the output of the process is and how to distinguish between good or bad results. If thats not possible, or if this is a continuous process without an end, then define mid-steps and mid-results that are anticipated.
  3. Defining what the input of the process is, that is what kind of data you take into account when looking for the output.
  4. Acquiring large datasets related to the business process. Cleaning it by removing unnecessary parts and organizing it in one place and in one format (e.g. .doc files stored on a cloud).

Having done this work, youre ready to start hiring data scientists to build machine learning algorithms for you. Often this preliminary work will be revised and enhanced with new data and new insights, but you dont have to worry about it at the start of the process. The crucial part for you, as an executive, is being clear about what business process exactly you want to tackle with AI.

Explanation of this new machine learning paradigm and how to apply it in business is the main reason for writing this book. I believe that understanding how data science teams work, how machine learning models are constructed, and what they need to perform well, is crucial to be competitive amid todays technological revolution.

It is also crucial for legislators, politicians, and philosophers to understand how the machine learning community operates and what is possible with AI, to make legislations and laws working for everyone involved. Theres definitely too much hype regarding AI when it comes to how fast it will disrupt human jobs as we know them. And even though I believe that will eventually happen, what we should prepare for is the next 5-10-20 years of growing dependence on AI. Every job will be enhanced by artificial intelligence, rather than replaced by it, and we shouldnt be scared of it. Examples in art and video games show that perfectly. We can learn a lot from the way machine learning systems look at our world and how they transpose it.

The most important thing is to stay open, embrace new technologies, and learn constantly. After all, what humans do best is adapt,

About the Author
  • Przemek Chojecki

    Przemek Chojecki joined The University of Oxford as a research fellow after completing his Ph.D. in mathematics in Paris, and then moved to the Polish Academy of Sciences where he worked as an assistant professor until 2019. His interests lie in mathematics, computer science, data science, and AI. He is currently the CEO at Contentyze.

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Artificial Intelligence Business: How you can profit from AI
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