AI in Digital Marketing. Part 1 | Mobio Group

Neural networks have gradually, but densely entered our daily lives. And now it is difficult to name a field of human activity that does not use AI technologies. Personal identification, automatic trading, visualizing diagnostics in medicine, learning autopilots, seismic data analysis, restoration of archive materials, evaluation of personnel efficiency in companies, calculation of securities value, forecasting of climatic and social changes — the list goes on.

Naturally, marketing and advertising have not been left out of the technical progress. Both actively use all the possibilities of neural networks. In Internet marketing AI is used to improve the results of social networks and search engines, to show the most relevant content and advertising messages to users, to monitor and analyze the market, to track the behavior of the target audience and so on. Neural networks have helped marketing teams get rid of complex multidimensional or monotonous chores and make them faster and more efficient.

One of the most popular topics today in the application of AI in marketing is the discussion of GANs. Generative Adversarial Networks are a class of deep learning frameworks with a generative model structure. In simple terms, generative AI is a class of machine learning algorithms designed to create new original content based on a set of input data. GANs can perform creative tasks previously thought to be unique to humans, such as creating text, images, music, code, Web sites, and videos.

Many of the features of generative AI Mobio Group are being tested in practice for specific tasks, and we’ll be sure to share our results and findings. This article systematizes the applications of GAN in the advertising business that we have identified as promising.

Visual Implementation

All the material obtained with the help of GAN will be unique and assume commercial rights (ownership) of the created content.

· Image generation on the basis of the text

Artificial Intelligence makes it possible to generate text-based images. Based on given parameters, theme, style, or location, words can be used to create the desired visual material, both animated images and realistic pictures, indistinguishable from photographs. Networks are capable of creating completely realistic images of non-existent people or animals, abstract and surrealistic landscapes and pictures that cannot be made with a camera, and even virtual worlds.

· Semantic image translation and 3D shape generation

Based on a semantic image or sketch, a realistic version of an image can be created. By analyzing millions of photos, a neural network recreates scenes and landscapes that mimic real landscapes. AI also generates text, numbers, and 2D images into 3D shapes with topology, rich geometric details, and textures. To build a photorealistic world, the 3D artist needs to create the bare minimum, and GAN will continue the process of 3D modeling complex landscape scenes.

· Transforming an image into an image

Neural networks have an infinite number of transformation variations, and the user regulates the desired result by his requests. What can be done with the use of different theses (queries):

• Transfer the style of one image to another or prescribe a certain style in the query with text (realism, anime, fantasy, cyberpunk, sketching, steampunk, etc.). GANs also make it possible to get images in the style of famous artists (like Van Gogh or Dali) or photographers.

• Transform image elements such as color, medium or shape while preserving the constituent elements (e.g. turn a daytime image into a nighttime one). You can also set the entourage or outfit of famous characters (Spider-Man, Harry Potter, or Dr. House) for a character, or change the age of the person in the image.

• Restore or reconstruct photos and low-quality images merge images.

• Create convincing fakes of celebrities which are hard to distinguish from real photos.

· Increase image quality (super resolution)

Neural networks can be used to improve the quality of images by making them clearer, sharper, and more detailed. It is possible to apply this GAN ability not only to photos, but also to drawings. Services offer removal of noise, backgrounds, increasing the size of images and improving the quality by several times (up to 16).

· Video Generation

AI allows you to convert text or image into video. Suitable for generating simple “speaker+phone” videos. The quality of more complex videos is still questionable. Google, for example, does not yet provide access to using its video creation system, but demonstrates the results of its service capable of generating video with a resolution of 1280×768 pixels and a frequency of 24 frames per second based on verbal queries.

The ability of AI algorithms to superimpose one person’s face on another person’s real video leads to deepfake. There are a lot of funny deepfake videos online, but the number of cybercriminals and deepfake crimes is also growing. Services are already being created that automatically recognize fakes, but it is worth noting that new machine learning technologies are constantly emerging that make fakes more and more realistic

The main image generators are Midjourney, DALL-E, Stable Diffusion, Deep Dream Generator, NightCafe, AI Playground, Let’s Enhance. Almost all services are not free, except for a small trial period, when beginners are given free minutes, energy, attempts, etc.

Free sites also exist (Dream by WOMBO, Crayon, Lexica), but the quality of the images in the output is very low. And you can only use these services as entertainment. At least for now.

After testing a number of platforms for practical customer assignments (see our next articles), we were convinced that the free services are considerably inferior to the paid ones. Here are the images generated by Craiyon and Dream by WOMBO for the simple query “sad cat sitting under an umbrella near a puddle, autumn leaves whirling, it’s raining”.

We noted the good possibilities for generating landscape and interior images (even on the free platforms).

Images of people, animals, and abstractions on paid platforms are much more impressive so far.

The main video generators are Pictory.ai, Phenaki, InVideo, Imagen Video,  Veed.io, Lumen5,  Designs.ai, Elai, Synthesia. As with image generators, video generators are not free either. At least the ones that deserve close attention.

Simple videos don’t require much professionalism and are recorded in a few minutes. With Synthesia we made this simple clip for free. We made this kind of video with Pictory.ai, but the potential of this service is much wider if you pay attention to it.

The main design generators are Uizard, Brandmark, Khroma, VanceAI to help you create designs for mobile apps, websites or weblogs, logos, color palettes and more.

Audio Implementation

· Text-to-speech generator

Against the backdrop of the development of Text-to-Speech (TTS) technology, speech synthesizers that recognize context-aware text using artificial intelligence have become publicly available. This technology has many applications, including audiobooks, movie voiceovers, podcast recording, “speech navigation,” voice chat, etc. Synthesized speech can be recorded in audio format or saved in OGG format. However, so far all these methods cannot surpass the quality of the natural human voice.

· Conversion of speech to speech

Generative AI applications related to sound involve generating voice using existing voice sources. Using STS, you can create voiceovers for advertisements or games without hiring a voiceover specialist.

· Music Transformation

With generative AI, it is now possible to create music based on your preferences for style, genre, etc. These tools can be used in many areas of creativity, including games and advertisements. Also, some systems work by converting an audio recording into a spectrogram, after which a neural network generates lyrics corresponding, in its opinion, to the music being played.

The main voice generators are iSpeech, Text-to-Speech,  Voicemaker,  Play.ht,  and Lovo.ai.

The main music generators are AIVA, Soundraw, Amadeus Code. In marketing, when using these services it became possible to create background music for advertisements without having specific musical skills. We tried to record a melody for a commercial with Christmas discounts theme. This tune is our first experience in this direction. The result is quite modest, but the prospects are promising – neural networks were able to complete the unfinished Ludwig van Beethoven’s 10th Symphony (BeethovANN Symphony 10.1).

Textual Implementation

Although GAN was originally used for visual purposes, algorithms are now used for text generation as well. In marketing and games, generative AI is used to create dialogs, headlines, advertising slogans and texts, blog posts, chat rooms for real-time communication with customers, or for creating product descriptions, articles and content in social networks.

This paragraph, for example, after processing on Rytr’s service turned out to be this:

“Imagine the power of an AI-powered assistant to help you create compelling content in seconds without sacrificing quality! With GAN, you can do just that — create compelling content without much effort.” Content generator Copy.ai suggested that, “General AI has moved beyond early development and into other areas such as text generation. With generative algorithms, you can create new content and include microbes. Algorithms can be used for news portals and articles, email newsletters, website descriptions and more”.

Obviously, you can’t do without text correction yet.

There are many neural networks for text generation — Frase IO,  Peppertype, Outranking, Writesonic, and GPT-3 is one of the largest (up to 4.5 billion words generated per day). Still, you have to understand that these services are not a panacea. And so far they are best suited for writing titles or short posts (product descriptions, filling product cards, feature descriptions, etc.) and for small texts where there is no need to consider the context. But in large volumes, human participation in content creation is still necessary. At least to check the generated text. Because some of the “thoughts” AI can puzzle or make you think. For example, when testing neural networks for text creation, we got phrases like, “First of all, you have to be able to write well, otherwise you’ll write badly” or “Designers don’t worry about being replaced by AI; they worry about other designers using AI.”

Code Generation

Another application of generative AI is software development because of its ability to generate code without the need for manual coding. Because of this quality, code development is possible not only for professionals, but also for people who are not involved in programming.

In marketing, the code generator can be used to write the right bots. For example, a bot that will send links to competitors’ articles which have got more than 100 likes. This is how the interest of the audience in a particular subject will be monitored.

The main code generators are CodeWhisperer, Copilot, Tabnine, AlphaCode.

Creating content (text, audio or video) with GAN is the future that has already arrived. And this means that it’s time to start using the opportunities neural networks give us. We found the most promising by far in the use of image generators, particularly Midjourney and DALL-E. We tested how these neural networks can help with the actual tasks of our customers, namely in the creation of visual creations and design. Read about what we were pleasantly surprised by, what difficulties we encountered, what conclusions we drew and what exactly we succeeded in doing in our next articles.