Neural Networks in Business, Marketing and Life

The technology market is growing: if in 2020 it was worth $14 billion, then by 2030, according to forecasts, the volume will reach $152 billion. Therefore, we will hear more and more about neural networks in the news.

Now it's part of our everyday life. Neural networks draw, generate texts, calculate complex data needed for decision-making in business, marketing, and daily life.

In this article, we will explain what neural networks are, how they work, and what benefits they bring.

What are neural networks

Neural networks are data processing systems (software code) that simulate the workings of the human brain: digital neurons are connected to each other by virtual synapses, through which information is transmitted. Neural networks belong to the field of machine learning and artificial intelligence.

Due to their similarity to the human brain, artificial neural networks are capable of analyzing, memorizing, and even reproducing data. The first such system appeared in 1958, thanks to neurophysiologist Frank Rosenblatt. At that time, a simple neural network (a mathematical model) could simulate the perception of machine information, similar to how the brain does it.

The future of neural networks turned out to be promising: today they are used in people's everyday lives. For example, users can generate unique avatars from their images for social networks in just a few minutes. To get such a photo from neural networks, it is enough to install an application.

How neural networks work: basic principles

Neural network technologies mimic the workings of the human brain, where electronic impulses are transmitted from one neuron to another. In the artificial version, neurons are represented by software nodes that obey set algorithms and transmit signals from one to another through synapses.

What is important for the system to work:

• Prepare the input data to train the neural network. Without information, nothing can be created or recognized. Therefore, the answer to the question of how to create a neural network is to first gather data. To train, you need many examples so that the system can understand patterns.

For example, if the task of the neural network is to learn to distinguish between handwritten "A" and "B", you need to load hundreds or thousands of files with images of letters.

• Training a neural network involves human participation. In one scenario, a specialist selects the necessary data and uploads it to the system, which then analyzes it on its own. In another scenario, a human sets algorithms and corrects the robot's mistakes.

For example, having analyzed handwritten "A" and "B", the system produced a numerical value as a result (the task was to find "B"). The higher the number, the more confident the neural network is that this option is correct. The humans know the answer, and if there is an error, they adjust the parameters in the system and give the command to recalculate everything.

What happens inside a neural network

A basic neural network consists of three layers:

• Input layer receives information from the external world. Here, the data is analyzed, distributed, and passed on to the next layer.

• Hidden layer (one or several) is responsible for processing the information from the first layer and other hidden layers. Specific features are extracted.

• Output layer gives the final result.

This is a simplified description, as the structure of neural networks is much more complex.

Classification of neural networks

Types of neural networks are distinguished by their structure, tasks, and subject matter. There are many classifications, but the most common ones are as follows:

Why neural networks are required

The application of neural networks covers various spheres of human life.

Forecasting is used in finance, industry, and in the operation of human life support systems. For example, a system can calculate the load on power grids during a certain period of time.

A neural network for marketing works in recommendation lists: it analyzes online user behavior and provides offers that they will definitely like. Such personalization increases sales.

Facial and image recognition is important for finding the necessary information and ensuring security. For example, if the system finds images that are not allowed for publication on an online resource, it immediately removes them.

In medicine, a neural network analyzes images of similar diagnoses and quickly provides a result.

The ability to compare and classify is useful for quality control of products. Thus, a neural network for businesses eliminates human error and speeds up work.

Recognition of spoken and written speech improves communication channels. Neural networks work in voice assistants, transcription, and analyze comments on the internet.

Artificial neural networks excel in creative fields because they can create unique art. However, some artists do not appreciate robots behaving this way.

Drawings generated by neural networks have caused controversy and even legal precedents. Several artists have sued image generation services Midjourney and Stable Diffusion, as well as the creative platform DeviantArt, which uses its own neural network called DreamUp. The plaintiffs argue that copyright is being violated because thousands of images from the internet were used to train these systems.

Artists on ArtStation platform have also protested against neural networks, uploading images with “AI crossed out” as a form of protest. The reason for their dissatisfaction is the appearance of robot-generated "art" on the site alongside authentic content.

Examples of using neural networks in marketing, business and everyday life

• One of the most popular neural networks DALL-E 2 can create original and realistic images based on textual descriptions. This is how the generated cover of Cosmopolitan turned out.

• Heinz collaborated with marketing agency Rethink Ideas to create "the first-ever ad campaign with visuals generated entirely by artificial intelligence." The agency used AI image generator DALL-E 2 to create ketchup-related prompts, and the results looked just like Heinz bottles. The campaign featured social media posts and print ads featuring the best AI-generated images.

• Everypixel's AI-powered filter rates the aesthetic value and commercial potential of stock images, providing more accurate and efficient search results for designers and image editors.

• Perfect Corp. has launched a new virtual try-on solution that uses AI and AR technology to provide customers with hyper-realistic hairstyle simulations. Users can choose from 12 unique styles and view before and after simulations. The technology takes into account factors such as hair color and skin tone to create inclusive and impactful simulations.

• An AI-created sneaker collection inspired by iconic Nike designs has gone extremely viral in December 2022 after being shared on Instagram* by @ai_clothingdaily. The digitally created shoes feature gothic and romantic elements with lace and beads, drawing attention from sneaker enthusiasts who praised the unique design and dreamed about buying a real item. The collection's popularity on social media could inspire Nike's future design choices.

• Kamil Banc, an AI-assisted author, has published several books using AI image creator and prompts to speed up the writing and publishing process. His books, including an illustrated children's book “Bedtime Stories” and an adult coloring book, have received positive reviews despite only selling a few copies. It took Banc just four hours to publish an illustrated book. He credits AI for making the process simple and fast, saying he was surprised at how quickly he was able to go from concept to publishing.

• Looka is a service that can generate logos. The system can create a logo for free within minutes, you just need to specify a few parameters. These are the options the neural network suggested for Altcraft Platform.

The use of neural networks for such purposes in business speeds up the process of creating logos and makes it cheaper.

• The service Deep Nostalgia brings old photos to life. The neural network recognizes faces and animates the images.

• Lensa app generates unique portraits from the user's photos using 10-20 samples.

Balaboba is a neural network that generates text based on inputs and styles them to fit specific formats. Here's an example of the text that was generated with the phrase "marketing automation service".

The service for removing the background from photos, Retoucher, also works based on neural networks. This is a useful function for marketing, especially when a company uploads images to online stores.

• Disney's neural network is capable of changing the age of actors in a frame. Now it is possible to avoid the use of make-up and searching for similar actors to portray characters at a different age.

Automatic content creation using neural networks is a possible solution to the problem of copyright when creating videos for business and marketing purposes.

Summary

A neural network is a data processing and analysis system that replicates the work of the human brain. The software code imitates neurons and connections between them (synapses) through which information is transmitted. The system learns from previous experience or from algorithms provided by humans.

Modern neural networks generate images, write texts, create music, recognize faces, predict events, and much more. Technology is becoming part of everyday life and business, simplifying and speeding up human labor.

*The product belongs to Meta, a company recognized as extremist in Russia.