A neural network is a mathematical model for processing or reproducing information, designed based on the principle of a neuron from the human brain. Currently, they are constantly evolving, their processing algorithms and information analysis are becoming more complex, and their capabilities more diverse.
What Are Neural Networks
The basic element of an ANN (Artificial Neural Network) is the artificial neuron and is designed exactly like its biological counterpart. Although in reality, it’s merely a function with inputs, outputs, and hidden layers, where the processing of the input information takes place. If there is only one hidden layer, the network is called shallow; if more than one, it’s considered deep.
According to Asimov Institute’s classification, about 25 varieties of models with varying complexity and depth have already been developed; here are just a few of them:
-
Perceptrons – process numerical data;
-
Convolutional networks can process images;
-
Generative models can generate content of varying complexity;
-
Recurrent networks accumulate and analyze information that can change over time.
What Can You Learn From Neural Networks
Although it seems that the human brain is more advanced, people always seek ways to transfer their responsibilities to someone else, but neural networks do not have that option. For example, there is a known case where a student submitted a completely AI-written diploma thesis. What else can artificial intelligence’s capabilities be used for, and what tasks can it help solve?
-
To improve one’s own skills. For instance, if you ask a neural network, “How to become a writer?”, you will receive a detailed instruction on which skills to hone, what actions to take, where and how a budding writer can present their works to get noticed. Of course, simply reading such instructions won’t automatically make you a writer, but it will set the right direction for development.
-
For learning new information. Suppose you want to delve into a topic, but the dry information from textbooks and the Internet is not enough. Let the neural network answer all your questions, allowing you to dissect the information as thoroughly as if you were with a tutor.
-
There are special models for processing and generating graphical information, i.e., drawings. Some even manage to draw independently, based on databases from collections of various artists’ paintings. Such a neural network can help you learn to draw something of your own, finishing templates on a given theme.
-
AI can even write pieces of programming code for you or conversely analyze the code you wrote, breaking down the algorithm into actions, explaining how it will work, and finding errors in the code. If you’re a beginner programmer who’s not confident in your abilities, such an assistant is indispensable.
-
For a website project manager or copywriter, neural networks can help to increase the uniqueness of texts, write texts on a given topic, change the style on a ready-made website page, or find spelling errors in texts.
-
AI can also help with learning a foreign language. Ask it to translate a piece of text or explain the meaning of each word you don’t understand, make sentences using the unfamiliar word, etc. For daily practice and vocabulary building, such a tool is invaluable.
What Can Neural Networks Learn?
Neural networks are constantly trained in something new, and there are various methods of educating and retraining networks.
For example, a neural network that recognizes graphic information can be trained to recognize human faces with high accuracy.
The next step could be to recognize the presence or absence of a seat belt. Such AI could analyze images from road cameras for violations. Of course, the whole process might take more than a day, but such a program could be sold at a decent price.
Here are 3 basic ways to train a neural network that can be utilized:
Supervised learning does not necessarily imply constant human monitoring and grading, although that step is also important. A good example of what can be taught to a neural network this way is an image recognition program.
Each image is labeled, indicating what is depicted, i.e., the correct answer, and the neural network analyzes the image and “remembers” the correct answer. After reviewing millions of diverse images, it will be able to determine what is depicted on an unfamiliar picture.
Unsupervised learning is based on AI self-education, where there’s no initial information, but a task is set that needs to be resolved more quickly or effectively. For instance, a neural network receives a request “find a photo of a rose” and begins searching independently. Then the operator evaluates the results on a certain scale. It can be a simple “yes” or “no” scale. Or more complex, for example:
-
Yes;
-
No;
-
It’s a flower, but not a rose;
-
It’s Rose, but not a flower.
As you can see, such an information selection algorithm will be more complex, but the neural connections will become more intricate in the learning process, making them more flexible in searching for the right answers.
Reinforcement learning is considered by many to be the most interesting method, yielding the most unexpected results. You place the electronic brain in a certain environment: a database with information, music, photos, or construction company reports from the last 10 years. The AI has no initial tasks and can do as it pleases with this data array, and the neural network always makes the decision.
Then a human confirms whether the neural network behaved correctly in these conditions. Accordingly, following the principle of academician I. P. Pavlov, positive confirmation is referred to as a “reward” for the AI.
Developing programs, applications, or chatbots based on neural network technology is one of the most promising directions today. You can develop the area that personally interests you, achieving perfection in programming and AI setup. Or you can use the fruits of others’ labor: create unique collages or photos, write texts, poems, or scripts, create software prototypes to later refine them manually, and even compose music.
