The terms “Machine learning” and “Deep learning” are often considered synonymous, which is a misconception. Both terms can be found in the media or technical articles, but it is important to understand that they are two separate areas of artificial intelligence, each with its meaning and significance. Before moving on to examine them in detail, let’s examine the meaning of Artificial Intelligence.
About Artificial Intelligence
In the middle of the 20th century, specialists began to develop computer systems that could solve various problems and questions. Previously it was thought that only humans could do this, i.e., intelligence was needed to perform such mental operations. In short, artificial intelligence is an intelligent system capable of solving creative tasks, traditionally considered to be the prerogative of humans.
A well-known example of the perfection of artificial intelligence is computer games. Their first versions were simplified in their functionality, such as games of chess or checkers, where the main actions are the player’s piece moves and reading combinations of the opponent. With the development of new technology, machines began to take on a completely different meaning and content. Artificial intelligence is now able to analyze the situation, calculate steps ahead and even beat people in computer games, which previously were unavailable.
There are practically no areas where artificial intelligence has not yet found its application, thanks to which medicine, science, education, etc. is rapidly developing.
Let’s take a closer look at the importance of Machine learning for artificial intelligence.
Machine learning
Initially, the methods that were created to work with artificial intelligence were not suitable for solving complex questions. For example, rigid algorithms were not suitable for recognizing images or videos, text,t, or emotions. To that end, machine learning, the field of artificial intelligence that is responsible for developing algorithms that can transform themselves without human help, has come to the rescue.
Simply put, these are methods that replicate the human learning system on a “simple to complex” basis. For example, like a schoolchild learning to read: first,t he learns the alphabet, then syllables, words, phrases,s, and finally texts.
Approximately the same principle is used by specialists to develop Machine Learning algorithms and provide them with a huge amount of data. The algorithms look at the information and come to conclusions, based on which the artificial intelligence is upgraded. If an algorithm is given the signs of a cyber fraud attack on a banking platform, the system, trained on this example, can then calculate such actions on its own.
It is important to understand that algorithms alone cannot analyze more accurate information; that is what neural networks are for, which we will talk about next.
Deep learning of neural networks
Artificial neural networks are mathematical models that replicate the structure of the human brain. They were created so that artificial intelligence can analyze images, text, human speech, etc.
Simple neural networks are capable of recognizing simple objects, distinguishing one from another, or counting how many objects are depicted in a picture. More complex networks solve problems that computers couldn’t handle before.
For artificial intelligence to learn to distinguish between animals, it needs to be provided with marked images of them. The probability of error-free identification becomes higher if as many marked-up images as possible are provided.
But as it turned out, this was not enough for the system to analyze video footage and recognize voices. To do this, the specialists began to work on the more in-depth training of neural networks.
Deep learning
Deep learning of neural connections is one of the varieties of machine learning, a new stage in the development of science, where neural networks include various constituent elements that communicate with each other within extended boundaries. In this case,e artificial intelligence can solve the most non-standard tasks.
The functionality of computer games, which we looked at earlier, has become real thanks to Deep Learning. Such deep neural networks can recognize complex images in real-time, such as an airplane at a non-standard angle, against any background,d, and even in a disguised form.
The data obtained by the system is analyzed by different layers of the neural network simultaneously. Each layer identifies the picture from its position.
There are three types of neural network layers:
- Input layer;
- Hidden layer;
- The output layer.
Image recognition takes place in the hidden layer.
Deep learning plays a special role in speech analysis. A multilayer neural network can cope with such a task: “France is my homeland, I lived in Peru and England. What language am I fluent in?” The neural network will analyze this phrase, generate a list of languages that the author probably knows, and eventually determine that the correct answer is French.
Deep learning became a reality after the development of high-performance computer systems, without which video recognition and analysis are impossible.
What problems can machine learning and deep learning solve?
Both disciplines are designed to solve different types of problems. In terms of business processes, Machine learning is designed to:
- Business automation. Machine learning will be able to recognize users, analyze and organize customer data, and provide a customized approach.
- Analysis of data that needs to be structured and applied to train algorithms.
To apply Deep learning the following conditions are necessary:
- A large body of information that has not yet been analyzed and cannot be used to train algorithms.
- The need to solve problems that machine learning cannot handle.
From what has been said, we can conclude that without artificial intelligence, machine learning, and deep learning, many computer functions would not be available. For example, such as speech and image recognition and even games of checkers. By processing large amounts of information and identifying relationships and patterns in it, machines can perform tasks of varying complexity.
The initial information always contains the answers needed by professionals in different fields. The main task is to learn how to find solutions using the latest technology.
Today, information and computer technology rules the world. And the winner is the one who has the most advanced artificial intelligence.
