When Should You Use Generative AI?

Today, we are surrounded by unique technologies that were once only science fiction. Artificial Intelligence is everywhere—in the news, on LinkedIn, and even in the local pub. Many predict (or at least hope) that it will revolutionize how we live, work, and interact. But what is generative AI? What’s the hype all about? And, most importantly, what are the best use cases of gen AI?

In this article, you’ll get many answers to your questions about the generative AI model and learn how this kind of artificial intelligence is used. 

What Is Generative AI?

Before we dive into the reasons, let’s answer a simple question: “What does generative AI mean?” 

Generative artificial intelligence is a type of machine learning model. It cannot think or feel emotions but is excellent at finding patterns. In the past, artificial intelligence was used to process and suggest information. Today, generative AI tools help you create content such as images, music, and even code. In addition, Generative AI can recommend interesting ideas, including conversations, stories, and videos.

The hype around generative AI models began to build in late 2022 with the launch of ChatGPT, a state-of-the-art chatbot capable of creating incredible content in human language. However, generative AI existed long before 2022. 

Though one can already find many AI applications online, they are still used mainly by researchers, developers, and creative professionals. Once the technology becomes more advanced and user-friendly, and there are more platforms to work on, the number of users will increase exponentially.

Examples of Generative AI in real-world Scenarios

Now let’s look at the most famous AI-based programs to assist you:

• OpenAI’s GPT-4. This transformation-based model is a high-performance language generator capable of writing emails, composing code, creating written content, training, and translating.
• ChatGPT. An AI-based chatbot developed by OpenAI. It is capable of human-like text-based dialog and can be used in customer service and personal assistant systems.
• DeepDream by Google. This program uses AI to detect and enhance patterns in images and perform fantastic psychedelic transformations.
• Jukin Composer. Based on OpenAI’s MuseNet, this tool uses AI to create original music for video content.
• DeepArt. This app uses generative modeling to transform user-uploaded photos into works of art inspired by famous artists.
• MidJourney. This text-to-image generator creates images and graphics based on user input and descriptions.
• In Silico Medicine. A biotech company that uses generative models to predict the molecular structure of potential drugs to accelerate the drug discovery process.
• StyleGAN from NVIDIA: This generative adversarial network generates hyperrealistic images of faces that do not exist in reality.

Obviously, this list is incomplete, as we see new innovative AI tools springing up every day. However, these programs have cemented themselves as industry leaders and are at the forefront of innovation.

Benefits of Generative AI

There are a number of clear benefits of generative AI despite the challenges and complexities of using it:

• Generative AI algorithms are powerful tools that can create new and original content, such as video, text, and images.
• Some of these deep learning style models can create content that is almost indistinguishable from that produced by humans.
• Generative AI algorithms can even be used to improve the accuracy and efficiency of existing AI technologies.
• Enterprises and researchers can use generative AI to generalize.
• Generative AI algorithms can enable developers and organizations to analyze and explore complex data in new ways.
• On a large scale, generative AI algorithms can accelerate and automate a huge number of processes and tasks, saving organizations significant time and resources.

These are just a fraction of all the strengths and opportunities of generative AI.

How Do You Use Generative AI?

Now that you know the meaning of generative AI and some of its most prominent real-world scenarios and benefits, it’s time to understand its usefulness and capabilities. 

Different kinds of experiments based on generative AI can help you start the creative process. But AI is supposed to do only some of the work for you as an assistant that makes your life easier. Here is how to use generative AI to make your life better:

1. Ask to generate ideas. For example, AI can help you come up with a prequel plot for your favorite movie.
2. Ask difficult questions. For example, “What came before, the chicken or the egg?”
3. Ask for help. For example, ask it to think of a title for your story or to identify the type of animal in the picture.

Generative AI Models for Your Business

Modern generative AI models already show satisfactory results when creating marketing and advertising materials. They are also suitable for generating ideas. In addition, AI can handle a large number of requests simultaneously, making it an efficient and cost-effective solution for companies. By utilizing AI for less complex tasks, companies can allow employees to focus on more complex and important issues.

By definition, generative AI is a made for simplifying and automating mundane and manually-intensive tasks. By using artificial intelligence to provide customers with personalized suggestions and recommendations, companies can increase conversion rates and boost sales. 

Generative AI is already being used in a wide range of fields, such as security systems, robotics control, and healthcare. For example, generative AI can help doctors detect diseases in their early stages and implement more effective prevention and treatment methods.

Many experts are beginning to fear that generative AI will suddenly lead to job losses. This idea is wrong! On the contrary, this technology can create new jobs. For example, people who know how to build queries properly and get good generated results can utilize this technology. In addition, there are already platforms that allow talented “early adopters” to sell their skills. 

The widespread adoption of generative AI will likely increase demand for professionals with creative skills (e.g., writers, designers, story and character creators). These professionals will play a key role in maximizing the potential of generative AI and ensuring the high quality of the results produced by the technology. 

On this positive note, it is worth moving on to a detailed breakdown of each niche in which generative AI is already being actively used, followed by more generative AI examples further down in this article.

Use Cases for Generative AI Models

If someone asks you: “What is generative artificial intelligence?” you now understand the basics of these systems and can hold a good discussion, so it’s time to talk about how you can apply these tools. 

Overall, the current wave of generative AI is limited to applications requiring good redundancy (GAN models) or additional assistants responsible for speech transcription or text generation. Some use cases mentioned here should give you an overview of the possibilities:

Creative Arts and Design

Generative AI has found many applications in art and generative design AI is changing the way people around us create and perceive art. Dall-E, Midjourney, and many other image generators have shown that creating realistic and captivating art is possible. GANs play a central role in this area. For example, an AI-generated portrait created using GANs by the “Obvious” art collective was sold for a whopping $432,500 at a Christie’s auction.

• Composing and generating music: Generative AI models are also used to compose music. A few years ago, it was hard to imagine that a machine could produce something as complex and creative as music. Now, networks like Google’s MusicLM or OpenAI’s MuseNet are trained on MIDI files of various genres and sources and can easily generate compositions in multiple styles.
• Transform art into different styles: AI doesn’t only create new works but also transforms existing ones. AI models can learn the style elements of one image and apply them to another image—a technique called neural style transfer. The result is a hybrid image that combines the content of one image with the artistic style of another.

These are tools that continue to gain momentum and improve. In the future, the capabilities of such applications will be further expanded. 

Natural Language Processing (NLP)

What other secret meanings does generative AI hide? Besides its use in virtual assistants, generative AI plays a key role in NLP tasks such as content creation, dialogue systems, and translations.

• Conversational systems and virtual assistants. With language understanding and targeted content generation, generative models also have the potential to enable conversations between humans and machines. You can generate contextualized answers and conduct human-like conversations.
• Text and content creation. Models like GPT-3 and GPT-4 have contributed significantly to the current hype. Their extraordinary ability to create human-like text is astounding. These models can write articles, compose poetry, and write or improve code, making them valuable tools for automating content creation.
• Language transcription and enhancement. Another well-known use case is language models for creating content from speech. The challenge is that these models need to understand the environment to compensate for poor microphone quality or noise in the room.

These tools can be used extensively by journalists, copywriters, blog writers, or people in other specialties.

Computer Vision and Image Synthesis

Generative AI has a significant impact on computer vision tasks, as neural networks can also identify objects or create deceptive replicas. What other examples of generative AI can be highlighted?

• Image synthesis. GANs are often used to generate realistic synthetic images. For example, NVIDIA’s StyleGAN creates exceptionally realistic images of human faces where none exist. Some AIs can also generate cinematic content without the need for professional cameras.
• Image enhancement. Generative models can also fill in missing parts of an image in a process called inpainting. They predict missing parts based on the context of surrounding pixels. Photoshop AI became a hit on social media because it enriched images with content that didn’t exist.

Such tools are proving especially useful in today’s realities, where social media development and quality content are gaining strong traction. 

Drug Development and Health Care

Generative AI has enormous potential in healthcare and drug discovery, as it can also predict or “invent” different structures or compounds. Here are some generative AI examples in medicine:

• Drug development. Generative models can predict the molecular structure of potential drugs, thereby accelerating the drug development process. For years, companies have been trying to use artificial intelligence models to invent new molecular compounds and apply them to develop drugs to treat diseases.
• Personalized medicine. Generative models can also help personalize medicine. By learning patterns from patient data, these models can help find the most effective treatments for individual patients.

At the same time, such possibilities are both impressive and frightening. Still, this niche will be explored and developed for many years to come.  

Generative AI in the Future  

Generative AI has already made great strides in many areas quickly and holds great promise for the future. However, it is important to realize that current models have their limitations, and even with them, it is impossible to create true super-intelligent AI. 

As innovators continue to optimize and improve generative AI models, new ways to create tools to detect and overcome potential problems and challenges are being researched. Some companies are even creating tools that can detect images, videos, and text created by artificial intelligence.

Improvements in AI platforms will also help boost the development of generative AI solutions in the future. Business leaders and developers are already looking for ways to incorporate generative AI into the tools and systems we already use on a massive scale. 

Design tools will become more intuitive, grammar-checking tools will evolve, and learning tools will soon be able to identify best practices on behalf of business leaders automatically. The future of generative AI has the potential to change virtually every aspect of human life and work.

Conclusion

Generative AI is undoubtedly an attractive technology for creating realistic content at scale. It has the potential to save money for the automation of many things that used to be costly. However, like any technology, AI has challenges and ethical considerations. Many issues have yet to be resolved, from deep falsification to misinformation, privacy issues, and bias.

It will soon become clear whether the industry’s promises can be fulfilled. Existing applications, features, and algorithms have their limitations, with many experts believing they have already been reached. This is because we have seen a plateau of opportunity, and models do not scale linearly with increasing scale. In other words, models will only improve slightly as the scale increases. But one thing is certain: generative AI will continue to exist, with its limitations and strengths.