Have you noticed how much people talk about artificial intelligence these days? It seems like AI is being implemented everywhere, or at least it will be soon!
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Learn everything about artificial intelligence (AI) in our detailed article ⭐ Understand in which fields AI systems are applied ☞ Discover new types and functions of neural networks (AI) ⚡ Examples of artificial intelligence development ✅
AI and neural networks are functional tools that are gradually penetrating all areas. The integration of AI often goes unnoticed by most users; they may not even realize that they interact with artificial intelligence every day.
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.
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.
Having covered the basic concepts and features of AI, do you want to know how modern AI works? Let me explain.
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:
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).
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.
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.
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.
Financial Sector.
The Automotive Industry also does not ignore AI.
E-commerce
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?
Have you noticed how much people talk about artificial intelligence these days? It seems like AI is being implemented everywhere, or at least it will be soon!
read more
In our humble opinion, even the laziest among us cannot fail to notice how rapidly artificial intelligence is developing in our time. Although, just a couple of years ago, AI could only be seen in science fiction films,...
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