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6 minutes read

The Top Use Cases for Generative AI Applications

By Jonathan Tarud
Generative AI applications
By Jonathan Tarud
6 minutes read

Generative AI applications have been getting a lot of attention from organizations, governments, and the public as the capabilities of generative Artificial Intelligence have gotten better and produced near-human results. 

Businesses are beginning to see the value of working with generative AI tools in various use cases. Concurrently, the capabilities of generative AI models have been at the center of ethics debates in schools, governments, and copyright law. 

Generative AI applications create a lot of questions that society will have to grapple with as these AI tools get more adept at content creation and can create convincing fake images and audio files. 

This post will not spend a lot of time focusing on these issues. Instead, we will explore how generative AI can be used by businesses to improve operations and deliver quality customer experiences. 

The Capabilities of Generative AI Models 

Generative AI applications are incapable of conscious thought or human-like behavior like the AI systems portrayed in science fiction. Instead, generative AI tools are powered by generative adversarial networks (GANs) and other Machine Learning models and algorithms. 

While a well-trained generative adversarial network or generative AI tool can accomplish many incredible things, conscious thought and decision-making are not among them. The primary applications of generative AI models include the following:

  • Text generation 
  • Image generation 
  • Text to speech generation
  • Speech conversion
  • Music generation 
  • Semantic image translation 
  • Code generation
  • Code review
  • Bug fixes 
  • Code refactoring 

Text Generation

Of all the capabilities of generative AI tools, text generation has arguably been the most visible, thanks largely to the popularity of the generative AI model ChatGPT. Generative AI applications are adept at creating text content based on input data

Organizations can use the text generation capabilities of generative AI tools to create social media posts, write blog content, and other marketing materials. However, ethical questions have been raised about using generative AI in academic settings.

In addition, questions have been raised about the training data used to train these generative AI models. As a result, some writers and creators feel like their work is being used without their permission. However, the training data used by ChatGPT and other popular generative AI models have not been published. 

While the marketing and advertising industries are being transformed by the text-generation capabilities of generative AI tools, these generative AI models should not be used to replace human writers and marketing professionals. 

Savvy organizations can arm their human workforce with generative AI applications to help them work more efficiently and effectively. 

Image Generation 

Users can use a generative model to turn text content into an image. Image generation is almost as popular as text generation when it comes to generative AI. A user simply inputs text describing the desired image, and the generative AI model creates the image. 

Organizations can use these tools to create materials for marketing, branding, advertising, etc. These generative AI tools can help your organization’s graphic design team create more content and work more efficiently. 

In addition, these tools can be used to convert image file types and improve the resolution of low-quality images. For instance, an ai upscaler can transform a pixelated image into a high-resolution version without the need for manual editing.

Text-to-Speech Generation

Using generative AI tools, organizations can create realistic voice audio from text input. For example, businesses can use this technology to create advertising voice-overs, podcast content, make notes and other text content more accessible for people with disabilities, and turn any text into audio files. 

These generative AI capabilities give organizations access to various linguistic talents. For example, your team can use generative AI to create realistic-sounding voice-over content instead of hiring a voice actor for a simple advertisement. 

Speech Conversion 

Using speech conversion generative AI tools, you can record a voice and make it sound like anyone else. These tools are helpful when you want to sound a specific way but don’t have the voice talent.

These tools have been making waves lately, especially in copyright law, as people create songs using a famous singer’s voice. However, you should not use this technology to impersonate any other person or create promotional material for your business using the voice of a famous person. 

Music Generation 

Generative AI models can create original musical content that your business can use in ad campaigns and other marketing materials. However, using these tools can open your business to copyright infringement issues.

For example, if protected material was used in the training data, you could unknowingly infringe on the copyright of protected content. Therefore, unless you can verify that protected material was not used in the training data, your organization should be cautious about using this type of generative AI tool. 

Semantic Image Translation 

Generative AI models can be used for image-to-image conversion or semantic image translation. For example, a generative AI tool can take a sketch and produce a realistic image or take an aerial photograph of a city and turn it into a map. 

Semantic image translation has proven useful in the medical industry, helping doctors make more accurate diagnoses earlier when the naked eye might not be capable of seeing an issue like skin cancer. 

Code Generation

Generative AI can be used to write code. Not only does this capability help professional programmers, but it can also help non-technical people create code for web pages and applications. 

For example, if you want to create a form for your website but don’t have the technical coding skills, you can ask a generative AI tool to write the code by specifying the task and the programming language to use. 

However, these tools can be used for more than just code generation. Generative AI can also be used to refactor code, complete unfinished code, fix buggy code, and perform quality checks on code. 

Generative AI will not replace skilled coders, but these tools can help programmers work more efficiently and help them find errors. 

Final Thoughts 

Many people are concerned about generative AI taking jobs from people. However, while this fear is understandable, organizations would be making a critical mistake if they believed they could replace humans with these tools despite their proven capability. 

Generative AI is most effective when used to bolster work. Writers, programmers, designers, etc., can use these tools to work more efficiently and effectively. If you want to learn more about generative AI, contact an experienced AI development partner like Koombea.

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