Demystifying Artificial intelligence: Simplified AI and Machine Learning concepts for Everyone (English Edition)

CHAPTER 1

Introduction

In this introductory chapter, we will explore some of the basic terms related to artificial intelligence (AI) such as machine learning, deep learning, data science, neural networks, etc.

After reading this, you will know what the field really is, what each term means, and how each part connects to the other. We will take a look at all this by keeping it short and simple.

Structure

In this chapter, we will cover the following topics:

What is data?

How to convert images, text, and audio data into numbers?

What is AI?

Subfields of AI

Types of AI

What is ML?

What is DL?

What are artificial neural networks and how are they built?

What is "deep" in deep learning?

Advantages/limitations of DL

What is data science?

Why do we need AI?

Why does automation matter?

Machine learning and maths

Why is ML so dependent on maths?

Objectives

After studying this chapter, you should be able to:

Learn about various terms related to AI and their correlation

Have a general idea about what an AI system might look like

Understand how machines can do something better than humans and what makes them so much better at some tasks

What is data?

Before we start discussing AI and machine learning, let’s first discuss data.

Data is the main reason why AI and ML algorithms/models are able to learn anything meaningful. It plays a crucial role in the performance of any ML model. In general, data can mean anything like tables in spreadsheets, images, videos, audios, text, track records, etc. We use data to teach machines/computers, but, guess what, machines can only understand numbers. Other than numbers, it can’t understand anything. Data is the crux of every ML model, and data is the only thing that the ML model gets from us. Quality and quantity of data are very important as that’s what the model uses to learn something. As long as we can somehow convert something into numbers, it becomes data for the machine learning models.

(By the way, a model is just an algorithm that learns to capture patterns in data. Basically, we teach models to make predictions in the future.)

It is okay for spreadsheet tables as they already have numbers, but what about images, audios, videos, or text? They are not numbers. Then, how can a machine learn from them?

Computers only understand numbers; nothing more or less. They operate in 0 and 1. So, we need to communicate with computers in numbers because that’s what they understand.

Well, if the data is not in numbers, we convert it into numbers.

How? Let’s see.

1. Text

Text is made from different words. We give each word a unique number. Let’s say, there are a total of 80,000 unique English words in our test dataset. We give each of them a unique number. So, the sentence "machine learning is very easy becomes something like 19768 45734 3 2349 459". Now, machines/computers can read this sentence. This process is called encoding in technical terms. Did you get the idea? We replaced each word with its corresponding number to convert text into numbers. While doing this, we maintain the order of the words.

2. Images

Actually, an image is already in the number format. When we capture a photo or shoot a video from a mobile phone or camera, it’s stored in numbers in memory. So, unlike text data, there is no need to exclusively convert images into numbers.

An image is nothing but a group of pixels in a certain order.

A pixel is the smallest unit of an image. Every pixel has a number associated with it. So, in a nutshell, an image is nothing but thousands of numbers in a certain order. For example, let’s take a look at the following image:

Figure 1.1: How a pixel looks like if we zoom in a photo

If we zoom the image to some extent, we can see each pixel. You may have seen these types of pixels in your mobile or camera. Each number in a pixel represents how much of these fundamental colors (i.e. Red, Green, and Blue) contribute to a particular