What is a neural network (artificial intelligence): Evgeny Kasyanenko on AI in simple terms

Key Concepts and Functions of AI

What is artificial intelligence (AI)? Let me try to explain it in simple terms. It can be imagined as separate software or an entire system. This software can easily perform tasks that require human intelligence.

• Tasks for recognizing voice and live speech.
• Work and analytical processes with large data sets.
• Making complex decisions based on statistics and mathematical calculations.
• Creating forecasts and analytical models for different fields.

Modern artificial intelligence is capable of these tasks thanks to machine learning algorithms. However, it cannot handle this on its own; neural networks assist it. It is neural networks that perform tasks that were previously done by humans.

Before we continue exploring artificial intelligence, let’s take a moment for some theory. This way, we can communicate on the same wavelength. I have prepared a list of important terms you will encounter when working with neural networks and AI.

Machine Learning — a technology that allows a system to improve and learn independently. The learning process is based on data analysis and performance enhancement. The system itself does not undergo changes in its code.

There is also the concept of Deep Learning. Deep learning is a more advanced form of machine learning. This model is based on multilayered neural networks. Such a structure allows the system to model complex data structures.

Neural Network — an architecture that mimics the functioning of the human brain. It consists of neurons that work with the received information. They process, analyze it, and help the system improve.

Pattern Recognition System — a learning algorithm whose goal is to teach the system to recognize objects and classify them. The system should be able to work with objects in photos, videos, and even in the real world. A simple example is a facial recognition system in a crowd, which is actively used in various countries.

Big Data — this term will come up frequently. It refers to data sets used in the training process of AI. Over time, AI learns to identify hidden trends, patterns, and tendencies.

Reinforcement Learning — a separate learning algorithm for the system. It is built around feedback. The system learns and receives feedback. Thanks to this feedback, the system understands the results of its work and strives to improve them.

What has the development of artificial intelligence led to? What is it capable of? Let’s take a look.

1. AI allows for the automation of work tasks. This significantly saves time and resources for businesses. AI is often used for automating data entry in warehouse management.
2. As I mentioned earlier, AI is great for analyzing large data sets. It is used for creating forecasts and analytics in business. It helps generate forecasts for price growth, sales, or market trends based on data from a specific time period.
3. It is also used for transcribing speech and text, all thanks to the methodology of pattern recognition systems. AI can be integrated into your business for recognizing and interpreting speech. For example, you can create a smart chatbot that is almost indistinguishable from a regular operator.
4. Artificial intelligence allows you to find the most efficient way to solve tasks. AI is devoid of emotions and human factors; it operates solely on numbers and data, making it an indispensable assistant in logistics, financial management, and production.
5. AI technology allows for generating and suggesting personalized content. How? Think of Netflix. Algorithms recommend series and movies based on your interests.
6. Another very important feature is adaptability. AI adapts almost instantly to new data and conditions. This is a fantastic characteristic that makes it an essential tool in the dynamic environment of finance or cybersecurity.

Having covered the basic concepts and features of AI, do you want to know how modern AI works? Let me explain.

How do Neural Networks and AI Work? Let’s delve into the fundamentals of these systems.

What are neural networks? Neural networks represent a specific mathematical model that resembles the structure of the human brain. Each neural network is made up of "neurons" that are connected to each other in layers.

This structure allows neurons to continuously learn and improve independently. They do not require external intervention from programmers and operators. Each time the neural network receives new information, it processes, analyzes, and enhances its performance.

How are neural networks structured? And what do the layers that connect the "neurons" in them represent? To answer this question, we need to look into the design of the neural network.

Input layer.

The starting point where input data from the user is received. For example, let’s take a neural network that recognizes objects in images. After the neural network interacts with an image, the pixels enter the input layer. The layer accepts the data and sends it further.

Hidden layers.

Now the computational processes and data transformations begin. Each layer is formed from neurons that accept input data (in our case — pixels).

Then they process the input data using mathematical functions. The processing process is structured as follows:

• Weighting each data input.
• Selecting the appropriate mathematical function.
• Applying the function to the data.
• Detecting complex patterns.

Output layer.

So, our pixels have passed through all the hidden layers, and now the data is directed to the output layer. It is the output layer that formulates the prediction or solution to the task.

In our case, we considered image analysis. This means that the neural network can say that the image contained an airplane, a helicopter, a fighter jet (formulate a hypothesis).

And here’s how the training of the neural network occurs using the backpropagation method (the method of backpropagating errors).

1. The data is run through the neural network.
2. The result of the neural network is compared with the correct answer.
3. The neurons are adjusted to reduce the likelihood of repeated errors.

The process is repetitive and is conducted many (very many) times in a row. The neural network is trained until it can recognize the most complex patterns with high accuracy.

And where is AI actively applied?

Where is AI used? There are many fields to list, so I decided to choose several large-scale industries and clearly demonstrate the capabilities of AI.

Examples of AI Use in the Real World

Diagnosis.

Let's start with the automation of diagnosis. AI helps doctors detect diseases at the earliest stages when they are easy to overlook. There is a well-known system called IBM Watson Health. It analyzes medical images, then analyzes the results of the patient's tests, and helps diagnose tumor processes in the body, including oncology.

Pharmaceutical Sector.

• Neural networks are used for drug development. Due to their power, they quickly analyze molecular structures and create new drugs.
• The company Exscientia has been successfully applying AI for drug development for several years now. There is a platform called Tempus. It uses genetic information from patients for personalized cancer treatment.

Financial Sector.

• Banks use AI to work with large data sets. A good example is JP Morgan. They integrated neural networks for market analysis and forecasting.
• There is a system called FICO Falcon. What does it do? It analyzes financial transactions to detect fraud.
• AI analyzes credit history and other client data to understand their creditworthiness. Who does this? The company ZestFinance.

The Automotive Industry also does not ignore AI.

• Everyone knows Tesla? Its signature autopilot is built on AI and neural networks. Tesla Autopilot analyzes information from the car's cameras and radars and then plans the route.
• AI helps plan production processes at enterprises. General Motors uses neural networks for two processes: analysis and potential component needs and improving logistics routes.

E-commerce

• AI algorithms analyze customer behavior and offer them relevant products. Who does this? Amazon and many other large brands have long used neural networks to create personalized offers.
• Companies use AI for logistics (optimizing delivery routes, managing inventory, and warehouse accounting).
• AI helps manage pricing. AI uses data on customer purchasing activity and market conditions for dynamic pricing.

Advantages of Working with K.I.S.S. Software

If you have been considering integrating AI and neural networks into your business, it’s time to take action. As you can see, large companies and brands actively utilize the capabilities of machine learning and process automation. Why do clients choose Kiss.software?

• The team consists of specialists with many years of experience in AI, machine learning, and automation.
• Our team is always available and regularly holds meetings with clients. You will see all stages of integration and the results of the work. Honest feedback and direct communication come first.
• Honesty and openness during collaboration help make timely adjustments to the project and achieve the desired result.
• Each project is a unique experience and task. We offer comprehensive solutions for integrating AI into business.
• We select a stack of specialists and technological solutions based on the requirements and specifics of the project.
• We are ready to implement your projects that others cannot execute.