What Is a Neural Network and How Does It Work?

Neural networks are one of the main trends in recent years. Many users are already using “nets” to create texts for websites, articles and dissertations, and even write code for computer programs.

Neural networks can recognize speech and conduct a decent conversation, so chatbots based on artificial intelligence are now actively used. They help businesses process a large flow of requests without wasting human resources.

We will discuss below how neural networks work, what other problems they can tackle, and what are the advantages and disadvantages of the technology.

What is a neural network in simple terms?

Simply put, a neural network is a program modeled after the human brain. It is trained on large data sets, which allows it to either recognize new information and check it for correctness, or generate unique analogues.

Neural networks improve during the learning process, and they themselves determine the optimal algorithms for their work. This is the difference from ordinary computer programs, which always work according to certain algorithms implemented by the developers.

How does a neural network work?

To understand the principle of operation of a neural network, let us look at a couple of examples. Let's start with ChatGPT — a popular neural network that is designed to generate answers to any questions. Experts believe that this is one of the smartest neural networks in the world. 

The developers trained it on a huge amount of data using RLHF — reinforcement based on human feedback. The network was then retrained several times on its own responses to make them more correct.

Due to this, the service can generate unique content on various topics. What distinguishes the program from other analogues of text generation is that it can perform tasks from different areas: generate basic program code, summarize scientific and technical articles, do financial analysis, make predictions, and in fact can answer any question.

Like other neural networks, ChatGPT has no intellect. The program does not understand the picture of the world, but only operates with large amounts of data, combining it and creating new texts. However, it does it many times faster than people.

Another popular neural network is Midjourney. There are also DALL-E 2 and Stable Diffusion, their purpose is to generate images. At the training stage, from a huge number of images with descriptions, the neural network identified matches. For example, if the description included the word “lion,” it remembered all possible images of that animal. Based on this data, the neural network can now draw unique pictures.

What a neural network can do directly depends on what arrays of information the developer used to train it. Despite the similarity with the human brain, the neural network is built on artificial neurons and does not understand what a “lion” is, and also does not see the differences between a “creature” and an “object”. It simply finds matches in large data sets and then reproduces them. 

Neural network structure

A neural network combines a large number of artificial neurons. To solve a given problem, they exchange data with each other — much like neurons in the human brain. Each of them is responsible for performing a very small task. Data is transmitted from one neuron to another until it is processed in accordance with the user's request.

The structure of the neural network consists of 3 elements:

• Input layer. The neural network receives information, whether it is a picture or text. The input layer converts the data into machine-readable numbers and passes it on for further processing.
• Hidden layer. Performs many actions in order to find the optimal solution to a problem. For example, it detects familiar elements in the original picture - raindrops, an animal, an object.
• Output layer. Here, the results of data processing are converted into a form understandable to humans. For example, into the text describing the picture, if the neural network was tasked with image recognition.

The neural network has no intelligence, mind, or consciousness. It’s just that now everyone has computer programs at their disposal that are fundamentally different from the usual ones. Some people were probably a little intimidated by this. Over time, people will get used to the existence of neural networks and learn to work with them.

Why do we need neural networks?

It may seem that neural networks have become widespread quite recently, when the results of their work flooded the Internet. But large IT companies have been actively using the technology for many years. Here is what neural networks can do right now:

• Voice search. When you speak to a voice assistant — Siri or Alice, or simply search for something using your voice, the neural network recognizes your speech and translates it into text. The assistants also give answers based on the huge amount of information that comes to them regularly. Due to this, they never stop learning and improving.
• Navigation. Many mapping services use neural networks that analyze the routes available to the driver and determine the optimal path to the point of arrival, taking into account various factors such as accidents or traffic jams.
• Medicine. Many scientists are already creating diagnosis programs for various diseases. For example, specialists from Stanford University are developing a neural network that can diagnose arrhythmia faster than doctors.
• Camera in a smartphone. A small neural network is now available in almost any mobile device. It is face or fingerprint recognition or auto settings for shooting.
• Recommendations. Social networks and streaming services have neural networks that select content (communities, music, films) for each individual user, analyzing their interests. Artificial intelligence can also create your own ringtone based on your preferences.
• Loans. Large banks use neural networks to assess the reliability and solvency of the borrower. In the vast majority of cases, decisions on loans are made automatically. Only unusual cases are transmitted to a person, for which the neural network does not have enough data to process.

In this way, technology either automates manual labor or simplifies user interaction with tools and services. There are different types of neural networks, but all the tasks they perform can be divided into 4 categories:

1. Classification. The neural network studies the information received at the input layer using algorithms formed during the learning process. Then it assigns the data to one class or another. For example, it determines the degree of solvency of a bank client or the type of object included in the frame.
2. Recognition. Neural networks of this type determine, for example, what a user said into the microphone and translate his speech into text.
3. Prediction. Such networks help to supplement existing information with high reliability. They, for example, can be used for adding colors to an old black and white photo or determining whether a particular phrase will be appropriate in a particular place in the article. 
4. Generation. Neural networks of this type create content at the user’s request, be it an article, a picture or a musical composition.

Задачи и сферы применения нейросетей постоянно расширяются. Раньше они были лишь вспомогательным инструментом для бизнеса или узких технических специалистов. Но сейчас буквально каждый пользователь может сгенерировать с помощью искусственного интеллекта качественную статью или уникальную картинку.

What is the difference between artificial intelligence and neural network?

Artificial intelligence or AI is a term with great variability in interpretation. Even the specialists engaged it understand it differently. However, in order to understand this term, we can make a generalized concept. Artificial intelligence (AI) is the ability to learn, with the goal of solving problems as well as a human.

Meanwhile, a neural network is a tool for solving specific types of problems. In other words, artificial intelligence is a much broader concept that includes a large number of neural networks and similar technologies.

Advantages and disadvantages of neural networks

To understand the essence of neural networks, it is important to take into account their positive and negative features. The main advantages include:

• Speed. A copywriter or journalist spends several hours or days writing an article. The neural network copes with the same task in a couple of minutes.
• Labor automation. Nowadays, some simple tasks that previously required human work are assigned to neural networks. Take, for example, content moderation on social networks. Automation allows us to direct more human resources to solve complex problems.
• Constant learning. The more tasks a neural network performs and the more data it processes, the more accurate the result of its work will be.

Of course, as with any new technology, there are some downsides:

• Need for a revision. This disadvantage follows from the previous one. Most often, the result obtained from a neural network needs to be refined, adjusting it to the user task. For example, editing text or processing an image.
• Difficult to master. It is easy to get results from a neural network, but getting a high-quality result is quite difficult. How good and correct the response of the neural network will be depends on the data provided by the user and its technical capabilities.
• Devaluation of human labor. Many specialists are afraid of losing their jobs, fearing that artificial intelligence will replace many specialties. 

At the same time, new professions are beginning to appear, such as neural network operators. These specialists formulate correct queries that give the most valid results from the neural network, requiring minimal modification.

In addition, some experts believe that progress towards the development of artificial intelligence could pose a threat to all of humanity. After all, there are still no mechanisms that can ensure people’s safety.

Can a neural network replace human?

Technologies based on artificial intelligence amaze with their capabilities, but they are still very far from the capacity of the human brain. Besides, a neural network requires a huge amount of energy to operate. In order to serve a program comparable to the work of the human brain, an entire substation will be required.

Of course, many services can help business owners, marketers, journalists and regular users in generating text and other content. However, a few nuances are worth considering:

• Neural networks cannot write an expert-level article. As a rule, each neural network is trained on its own data array, and it uses different algorithms. While doing so, it does not have the ability to think, cannot compare facts and conduct in-depth analysis. However, it can be entrusted with simple tasks — creating posts for social networks and blogs, describing products according to given characteristics. In addition, neural networks can help with generating ideas, as they quickly analyze popular user queries on a topic. 
• It provides answers to user questions based on the information it has been taught. The neural network writes responses to user requests as if a living person were answering them. However, we cannot be sure of the accuracy of the response. Many neural networks draw data from open sources, the reliability of which is not exactly known.

Conclusion

Neural networks that have become popular among regular users made a splash and attracted the attention of the whole world to the technology. Major corporations, one after another, are announcing large investments in the field of artificial intelligence.

A neural network is a truly useful assistant. Everyone who wants to keep pace with technology should be acquainted with it. Yet, people should know about the limitations of neural networks and not equate their abilities with the potential of the human mind.

We can expect that in the next couple of years, neural networks will make people’s lives much easier. After all, our experience has shown that the speed of their development exceeds even the wildest expectations.