Generative AI Use Cases and Applications

The advent of Artificial Intelligence (AI) has significantly impacted the way businesses operate and manage daily workflows. The emergence of diverse AI applications and tools has enabled businesses to make wiser decisions and automate repetitive tasks, making operations more efficient and effective. Although many AI algorithms exist, generative AI has gained prominence across industries.

With the popularity of prominent generative AI tools like Midjourney and ChatGPT, businesses can generate new ideas, content, and solutions faster than ever before. This improves decision-making, streamlines operations, and allows businesses to stay competitive in an ever-evolving market by creating new products and services. Generative AI is proving to be a game-changer in the business world, with its potential being widely recognized in 2023.

Through this article, let us look at generative AI use cases and applications in various industry domains.

• What is Generative AI?
• Generative AI models
• Generative AI Use Cases
• Popular Generative AI applications across industries

What is Generative AI?

Generative AI is a subfield of Artificial Intelligence that utilizes Machine Learning techniques like unsupervised learning algorithms to generate content like digital videos, images, audio, text or codes. In unsupervised learning, the model is trained on a dataset without labeled outputs. The model must discover patterns and structures independently without any human guidance. Generative AI aims to utilize generative AI models to inspect data and produce new and original content based on that data.

Generative AI tools use sophisticated algorithms to assess data and derive novel and unique insights, thereby improving decision-making and streamlining operations. The application of generative AI can also help businesses stay competitive in an ever-changing market by creating customized products and services.

Using Generative AI, computers can generate new content output by abstracting the underlying patterns from the input data.

Generative AI models

Generative AI commonly works by training a deep learning model on a dataset of pictures, which is then applied to create new images. It utilizes several Generative AI models like BERT and Transformer or Autoregressive models. However, Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are two of the most prominent generative AI model types.

A GAN comprises two components: a generator and a discriminator. While the generator is trained to produce new images that mimic those in the training set, the discriminator is trained to discriminate between actual photos from the training set and fake images generated by the generator. The generator and discriminator are trained in tandem in a procedure known as adversarial training. During this training, the generator tries to produce images that can dupe the discriminator while the discriminator strives to detect counterfeit images accurately. The generator gets better with time and can create more lifelike images.

VAEs, on the other hand, work by learning probabilistic mapping from a high-dimensional input space like a photograph to a lower-dimensional unrevealed space and then back to the original space. While training, the VAE is exposed to a large dataset of pictures, and it learns the patterns and features of images to understand the probability distribution of the images. Once trained, the model can produce new images from the dataset by taking samples from the hidden space and mapping them back to the original space.

In the case of GAN or VAE, the model, once trained, can produce a new image or content that mimics the ones in the training set. This technology has multiple applications, including creating realistic computer-generated images, refining low-quality images, and drawing new images from text descriptions.

Now that we know how Generative AI works let us look into the use cases of Generative AI.

Generative AI Use Cases

Use cases of generative AI can be categorized into different types based on the content it produces: visuals, audio, text or codes. Let us explore Generative AI use cases pertaining to these domains separately.

Use cases in visual content

1. Image generation and enhancement

Generative AI tools for image generation are usually text-to-image. Users can enter the text describing what images they want, and the tool will process them to produce realistic images. Users can specify a subject, setting, style, object or location to the AI tool, which will generate amazing images pertaining to your requirement.

In addition to text-to-image AI tools, which create realistic 3D models or realistic original artwork, there are tools available for image enhancement that modify existing images. These are some of the functions it can perform:

• Image completion: AI tools with this capability can generate missing parts of an image, like creating a realistic background for an object, filling in missing pixels, or fixing a torn photograph.
• Semantic image-to-photo translation: It involves creating a photo-realistic version of an image based on a sketch or a semantic image.
• Image manipulation: It includes modifying or altering an existing image, like transforming the external elements of an image, such as its style, lighting, color or form, while maintaining its original elements.
• Image super-resolution: Tools possessing this capability can enhance the resolution of an image without losing its specific details. For instance, users can improve the quality of an image captured on CCTV.

Examples of Image generation AI tools include Midjourney and DALL.E.

2. Video creation

Generative AI simplifies the process of video production by offering more efficient and flexible tools for generating high-quality video content. It can automate tedious tasks like video composing, adding special effects, animation, etc. Similar to image generation, AI tools for video production can generate videos from the ground up and be used for video manipulation, enhancing video resolution and completion. They can also perform the following tasks:

• Video prediction: It involves predicting future frames in a video, such as objects or characters moving in a scene, using generative models. It can understand a video’s temporal and spatial elements, produce the following sequence based on that information and discern between probable and non-probable sequences.
• Video style transfer: AI video generators with this capability can produce a new video that adheres to another video’s style or a reference image.

3. 3D shape generation

Generative AI tools can be used to create 3D shapes and models utilizing a generative model. This can be achieved through various techniques like VAEs, GANs, autoregressive models or neural implicit fields. AI tools for 3D shape generation are beneficial in creating detailed shapes that might not be possible when manually generating a 3D image. It can also be leveraged to boost the performance of 3D-based tasks like 3D printing, 3D scanning and virtual reality.

Generative AI use cases in an audio generation

1. Creating music

Generative AIs are beneficial in producing new music pieces. Generative AI-based tools can generate new music by learning the patterns and styles of input music and creating fresh compositions for advertisements or other purposes in the creative field. Copyright infringement, however, remains an obstacle when copyrighted artwork is included in training data.

2. Text-to-speech (TTS) generators

A GAN-based TTS generator can produce realistic speech audio from user-written text. Such AI tools enable the discriminators to serve as a trainer who modulates the voice or emphasizes the tone to produce realistic outcomes.

TTS AI uses extensive speech and text data to train machine learning models. The models can then be fine-tuned to generate high-quality speech from text. AI-based speech-to-text tools are used in various applications, such as speech-enabled devices, speech-based interfaces, and assistive technologies.

3. Speech-to-speech (STS) conversion

In audio-related AI applications, generative AI generates new voices using existing audio files. Utilizing STS conversion, professionals in the gaming and film industry can easily and swiftly create voiceovers.

Text generation

Text generative AI platforms like ChatGPT have become increasingly popular since their launch. Such platforms are highly efficient in generating content like articles or blog posts, dialogues, summarizing text, translating languages, completing a piece of text or automatically generating a text for a website and more. Systems are trained on large data sets to create authentic and updated content.

Most text-generation AI utilizes the Natural Language Processing (NLP) and Natural Language Understanding (NLU) techniques of AI to read a text prompt, understand the context and intend and produce intelligent responses to the users. Such tools are trained on large data sets to create authentic and updated content.

Other than generating new content, text-generative AI tools can efficiently perform numerous other language-related tasks like answering questions, completing an incomplete text, classifying text into different categories, rephrasing and improving content and engaging in human-like discussions on multiple topics. Generative AI models for text generation can be leveraged for the following:

• Creative writing: It can be utilized to write a piece of fiction like story, song lyrics or poems.
• Conversational agents: Generative AI models can be used to develop virtual assistants and chatbots that can automatically respond to user inquiries and hold natural conversations.
• Translation: Generative AI models can swiftly and accurately translate text from one language to another.
• Marketing and advertising: Marketing and advertisement materials like product descriptions, ad copy, content for social media promotion and catchphrases can be generated.

Code generation

Generative AI can be leveraged in software development thanks to its ability to generate code without manual coding. By automating the software creation process, these models reduce developers’ time and effort in writing, testing and fixing codes. Generative AI models for code generation can do the following:

• Code completion: Completing a code snippet is easy with generative AI models like ChatGPT that study the context of the code to suggest the next line of code.
• Code generation: Thanks to its natural language capabilities, a generative AI model can understand a text prompt to convert it into codes.
• Test case generation: Generative AI models can create test cases to assess the software’s functionality, confirming that it performs as intended.
• Automated bug fixing: Developers can enter the code into a generative AI tool model like GPT, which then identifies and fixes the bugs in the code.
• Model integration: With generative AI, developers can easily and quickly implement machine learning models in their software based on a specific model, such as a neural network or decision.

Collaboration

With the latest advancements in generative AI capabilities, personal productivity tools like email and word processing can now be augmented with automation to improve efficiency and accuracy. One notable example of the power of generative AI is Microsoft’s use of GPT-3.5 in the premium version of Teams. This powerful tool enhances meeting recordings by automatically dividing them into sections, generating titles, and adding personalized markers. It can even highlight mentions, making it easier for you to find the most important parts of the conversation.

But that’s not all. Start-up Jesper.ai takes automation to the next level with their AI-powered word processor that replaces tedious writing tasks with this revolutionary tool that automatically generates full text for marketing copy, job descriptions, and more. With generative AI, collaboration and productivity can soar to new heights, freeing up valuable time for more creative and strategic endeavors.

Enterprise search

Generative AI can help companies find information more easily within their own documents, which is known as enterprise search. Generative AI can securely read through all of a company’s documents, such as research reports or contracts, and then answer questions about them. It can also point out which parts of the documents are most important. This can help people in the company learn more and share information better.

Knowledge management

Generative AI models are revolutionizing the way we work with lengthy documents and data by summarizing them into concise paragraphs and providing citations to sources. But it doesn’t stop there. These models can also generate new content, including data analytics presented in charts and graphs, that can be seamlessly assembled from various systems of record. With the power of generative AI, businesses can streamline their operations, save time and resources, and unlock new insights that were previously hidden in mountains of data.

Popular Generative AI applications across industries

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