Using Neural Networks in Web Design

When OpenAI presented its chatbot to the general public in November '22, the public was divided into three groups:

• some were skeptical,
• others made nervous jokes about Skynet and Isaac Asimov,
• and others either hadn't heard of it or preferred not to think about it.

But among content production-oriented professions, there were virtually no indifferents.

Procedural content generation using machines was no longer just random combinations or simple automatic text filling. Such technologies were now available to a wider audience, and it was a true revolution in creative production.

Optimists anticipated the transfer of drafting work (or even the whole thing) to AI, and some people were scared that their work might soon cease to be in demand, so they began to actively protest against the introduction of neural networks in production and even to raise legal protection against job cuts.

The basics of neural networks

To begin with, we need to be clear: what a neural network is basically and how it differs from other types of AI.

Neural network, in short, is a computational model inspired by the structure of the human brain. It, like it, has complex chains of neurons that are arranged on different layers for step-by-step processing of information, while each neural connection can have a different weight and degree of bias, activation function, etc.

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The OpenAI company, before releasing its brainchild on the expanse of the Internet:

• fed Chat-GPT huge amounts of information from the web, books and articles.
• then trained the neural network to predict the next word in the original sentence to learn how to construct its own speech,
• and then engineers would step in and make precise adjustments to the network's thinking logic,
• after which access was granted to a slightly larger number of operators to collect feedback and ethical considerations were worked out: protection against bias, safety and reliability of the results.

Important! Neural network does not “understand” what exactly it gives out at the request of the user. Like a schoolchild at the USE, it simply guesses the answer options based on information from the network.

Roughly speaking, an advanced search engine compiles the received data into a more or less coherent answer and gives it to the user. Neural network answers are never 100% accurate because it is an averaged version of all the information on the web. Answers can vary with repeated queries, so it's important to verify the information to avoid errors.

The role of neural networks in web design

Now, it's time to look into how neural network can be used in web interface design. Earlier, we looked at how a neural network works using the example of a language model (a system that understands and generates text based on language learning) for easy understanding.

Web design is the development of the user interface of interaction with Internet resources, which includes both visual aspects (colors, fonts, pictures and design) and functional aspects (navigation, buttons, forms, layout, etc.).

You may be interested in: Frontend and Backend - concept and difference

The goal of the web designer is to make interaction with the web product convenient, intuitive, pleasant and attractive for the user. In this field, a couple dozen neural networks have already been developed and are actively used to perform a wide list of tasks:

• generate music and video
• generate images
• generate corporate fonts, logos, icons - create stylistics, visual identity
• draw illustrations, sprites, organic characters, etc.;
• create promts* for other neural networks based on user requests
• process and edit images: convert raster graphics into vector graphics, replace or delete backgrounds, objects or other details, etc.;

PROMT - a request or command containing information to be executed by a neural network. The output result of the neural network depends on the correct and precise formulation of PROMTs.

As you can see, many tasks that used to be done manually by web designers can now be done semi-automatically:

1. Determine details with the customer in a call
2. Upload a recording of a conversation to a neural network for transcription
3. Load transcribed text into a language model to create promts for generating photos, website templates* and other materials based on it
4. Load promts into image-generating neural network
5. Upload images together with promts to the neural network generating templates
6. Get a 60-80% ready site, which is much faster to finalize and fix than to create from scratch.

Template (or template) in web design is a preset layout, the structure of the site, the arrangement of its elements, on the basis of which the final version is created.

Tools and technologies

AI tools and their features that can be used in web design will be listed below:

• ChatGPT - despite previously being labeled as just a rolling search engine, it is being actively refined and supplemented with useful chips. For example, in one of the latest updates added the ability to hand-draw a sketch of the future site, and the neural network will write the code to create such a template.
• DALL-E is also a neural network from OpenAI, which generates images based on text promts. Advantages include good generation of fantasy and complex scenes, as well as good interpretation of abstract concepts.
• Midjourney is one of the first and most popular neural networks for image generation. Generates artistic images from textual promts. Typically produces highly creative results characterized by originality.
• Stable Diffusion is a diffusing model, i.e. it not only generates images based on textual prompts, but also knows how to repair damaged images, e.g. remove noise. It is distinguished by high quality of the final image and flexible customization.
• ClipDrop is a platform of AI-based tools. Allows you to generate, edit, extract and inscribe images.
• Dora AI - yet to gain fame and popularity, but an extremely promising project. Dora allows you to generate websites, has ready-made templates, a block builder, a tool for working with 3D models, as well as creating text and images.
• Uizard - allows you to use your handwritten sketch to generate a prototype of a future site. Also includes tools for generating images, color palette selection and a set of templates.
• Framer AI - prototyping and creating user interfaces. Intuitive interface, content generation, adaptability, ability to work as a team and integrate third-party APIs, i.e., tools.

Advantages and disadvantages

Let's briefly break down the pros and cons of using neural networks in web design:

Advantages:

• Increased speed of development. Neural networks are able to perform the lion's share of rough routine work.
• Automation of processes, which will allow designers to focus on the creative component.
• The possibility of creating unique and non-standard solutions. Since AI has no biases and beliefs, given promts it can interpret and cross-pollinate in completely unexpected ways.
• Integration with other AI technologies so that the usefulness of these technologies can be enhanced.

Disadvantages:

• Limitations of technology and the need for human intervention. Full automation is not yet available and such a step in the development of neural networks will not be taken in the near future.
• Ethical problems: copyright, content generation. But so far there are no problems with this, so it is not possible to predict how serious they will be.
• Phantom risks of dependence on technology, loss of creativity and devaluation of human labor. Unrealistic, because even the most advanced neural network needs an operator and a user.

The future of neural networks in web design

Nothing can stop neural networks from developing and becoming more popular. The usefulness is grandiose, and the disadvantages are questionable. The very technology of machine learning implies rapid and steady growth.

A year ago, neural networks for video generation still caused laughter from the ridiculousness of what was happening on the screen, but today there are far fewer truly incongruous elements. Neural networks are suitable for use not only in web design, but also in education, perhaps even medicine in the near future, and for the profession of web designer the technology does not seem adequately replaceable even now.

The second half of the 24th year in the sphere of content production and digital engineering is entirely under the banner of neural networks as the main tool for saving time and effort. In an ever-changing and adapting job market in a young field like web design, things are changing fast, and experimenting with new tools and technologies like neural networks fits perfectly into this state of affairs. Let's keep our finger on the pulse.

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