Top Applications of Generative AI for Individuals and Businesses

Trinh Nguyen

May 31, 2024

Page Content:
What Is Generative AI?
How Does Generative AI Work?
General Generative AI Applications
I. Visual Content Applications
1. Image Generation and Enhancement
2. Video Creation
3. 3D Shape Generation
II. Audio-Related AI Applications
1. Creating Music
2. Text-to-Speech (TTS) Generators
3. Speech-to-Speech (STS) Conversion
4. Speech-to-Text Models
III. Text-based Applications
IV. Code-based Applications of Generative AI
Industry-specific Generative AI Applications
1. Healthcare
2. Advertising and Marketing
3. Finance and banking

Generative artificial intelligence (GenAI) is not just a passing trend; it’s a transformative tool with vast practical applications. From creating new product designs to optimizing business processes and even creating entire virtual worlds, its potential is boundless.

The impact of generative AI solutions on the AI landscape is profound and extensive, and we’re only beginning to uncover its full potential. Gartner predicts that by 2026, over 100 million people will rely on this technology to streamline their work processes. McKinsey’s analysis also suggests that its widespread implementation could contribute a staggering $2.6 trillion to $4.4 trillion to the global economy.

Curious to learn more about GenAI? Keep reading to discover all the basics – from the definition and mechanisms to applications of generative AI and more.

What Is Generative AI?

Generative AI is a subset of artificial intelligence that harnesses machine learning methods like unsupervised learning algorithms to create new and unique content. In response to your prompts, you can create images, videos, music, speech, synthetic data, and software code. For example, in unsupervised learning, the model learns from datasets without labeled outputs, autonomously identifying patterns and structures. In the end, GenAI’s ultimate goal is to employ these models to analyze input data and produce novel content based on it.

Chances are you’ve come across at least one of the most prominent generative AI tools today, including ChatGPT, Google Gemini, Microsoft Copilot, and DALL-E.

How Does Generative AI Work?

Generative AI uses machine learning techniques, particularly a subset called deep learning. Basically, deep learning utilizes artificial neural networks, which mimic the structure and function of biological neural networks found in animals’ brains.

What sets deep learning apart is its capability to learn semi-supervised and unsupervised. This means that deep learning models can sift through vast amounts of unlabeled data with minimal human oversight. For instance, the model dissects and analyzes data independently in unsupervised learning, seeking patterns without predefined instructions. These foundational models serve as the groundwork for generative AI applications.

Via deep learning, generative AI models spot patterns within human-generated content, learn, and reproduce them.

Among the most common generative AI models are generative adversarial networks (GANs), transformer-based models, variational autoencoders (VAEs), and large language models (LLMs).

Generative AI offers a wide range of applications, from creating videos and audio to crafting text and writing code. Its versatility benefits both individuals and businesses across various domains.

General Generative AI Applications

Visual Content Applications

1. Image Generation and Enhancement

Generative AI solutions predominantly operate through text-to-image mechanisms. Users input descriptive text, specifying subjects, styles, objects, or locations, and the tool creates realistic images matching those descriptions.

Beyond text-to-image tools, there are options for image enhancement, offering functionalities like:

Image completion: Generative AI tools can fill in missing parts of images, like creating realistic backgrounds or fixing torn photographs.
Semantic image-to-photo translation: This involves generating photorealistic versions of images based on sketches or semantic descriptions.
Image manipulation: Users can modify existing images, altering elements like style, lighting, color, or form while retaining the original essence.
Image super-resolution: These solutions can increase the resolution of images without losing detail, improving the quality of images like those captured by CCTV cameras.

2. Video Creation

Generative AI models can also help streamline video production by providing efficient and flexible tools to generate high-quality video content. It automates time-consuming tasks like composing videos, adding special effects, and creating animations. Similar to image generation, these AI tools can create videos from scratch, manipulate existing videos, enhance video resolution, and complete incomplete videos.

Other advanced features of GenAI when it comes to video creation are:

Video prediction: This involves predicting future frames in a video, such as the movement of objects or characters. Once generative models understand a video’s temporal and spatial elements, they will be able to produce the next sequences based on that information and distinguish between probable and improbable sequences.
Video style transfer: AI video generators can create a new video that mimics the style of another video or a reference image, allowing for creative and consistent visual presentations.

3. 3D Shape Generation

Generative AI capabilities also excel at creating intricate 3D shapes and models. It uses various techniques, such as VAEs, GANs, autoregressive models, and neural implicit fields.

These AI systems are particularly useful for generating detailed shapes that would be difficult to produce manually. Moreover, they enhance the efficiency and performance of 3D-based tasks, including 3D printing, 3D scanning, and virtual reality applications.

1. Creating Music

You may not notice, but the generative AI model is a powerful tool for producing new music compositions. By analyzing patterns and styles from existing music, these genAI-based tools can yield new pieces suitable for different creative endeavors like advertisements. However, pay attention to copyright infringement since it can be a big issue when copyrighted music is used in the training data.

2. Text-to-Speech (TTS) Generators

Text-to-speech generators, often based on GANs, can transform written text into realistic speech audio. Basically, they use discriminators to refine the voice output, ensuring it sounds natural and expressive. Plus, being trained on extensive speech and text datasets, TTS models can then produce high-quality speech. These solutions are widely used in speech-enabled devices, voice-based interfaces, and assistive technologies, making them highly versatile.

Some TTS models allow us to export human-like speech audio depending on the nuance of the sentence. Then, they will translate the audio into other languages, too.

3. Speech-to-Speech (STS) Conversion

Generative AI is able to convert one speech style to another using existing audio files. This capability proves especially beneficial in the gaming and film industries, where professionals can quickly and efficiently create voiceovers.

Just by leveraging STS conversion, they can produce a variety of voices without needing multiple voice actors, thereby saving great time and resources.

4. Speech-to-Text Models

Have you ever told Siri to make a call automatically or ask Alexa for the latest weather updates? It’s about the magic of speech-to-text technology. These models enable users to input their voice to make requests, and then they provide responses instantly. Some common STT applications include GPS systems for spoken direction guidance, public announcements at airports or railway stations, or telecommunication for reading out text messages.

Text-based Applications

Generative AI platforms like ChatGPT have gained immense popularity since their launch. These platforms excel at content creation in various formats. You can write articles, website pages, and dialogues, summarize content, translate text to other languages, and even complete a sentence. Gemini, in particular, takes the text generation application to the next level by providing multiple versions of an article or email for you to choose from.

Apart from new content creation, text-generative AI tools are adept at handling numerous language-related tasks. They can answer questions, complete unfinished texts, categorize text into different groups, rephrase and enhance content, and engage in human-like conversations on a wide range of topics.

In general, here are some key areas where generative AI models for text generation can be leveraged:

Creative writing: Generative AI can craft fictional pieces such as stories, song lyrics, or poems, making it a valuable tool for writers and artists seeking inspiration or assistance.
Conversational agents: These models are used to develop virtual assistants and chatbots capable of automatically responding to user inputs and maintaining natural, fluid conversations.
Language translation: Generative AI models can quickly and accurately translate text from one language to another, making communication across language barriers much easier.
Marketing and advertising: In marketing, generative AI can create product descriptions, ad copy, social media posts, and catchy slogans, enhancing the efficiency and creativity of marketing campaigns.

Code-based Applications of Generative AI

Generative AI use cases also extend to software development when it assists in code generation processes, thus reducing developers’ workload and streamlining the software creation process.

Overall, these models are capable of the following:

Code completion: Generative AI models are great at analyzing the context of code snippets to suggest the next line of code, facilitating seamless code completion.
Code generation: Leveraging natural language processing capabilities, generative AI models can understand text prompts and translate them into executable code. No matter the programming languages you require, it can generate code without hassles.
Test case generation: Generative AI can create comprehensive test cases to evaluate software functionality, ensuring the software performs as expected and meets quality standards.
Automated bug fixing: Developers can input code into generative AI models like GPT, which then identifies and fixes bugs automatically, reducing manual debugging efforts.
Model integration: Using generative AI, developers can seamlessly integrate machine learning models into their software based on specific requirements, such as neural networks or decision trees, enabling rapid implementation and deployment.

Industry-specific Generative AI Applications

Generative artificial intelligence finds its footing in diverse sectors like healthcare, marketing, and finance.

Let’s see how professionals in these industries harness the power of generative AI in their respective fields.

1. Healthcare Applications

Generative AI is expected to revolutionize the healthcare and pharmaceutical industries, offering a host of solutions from drug discovery to personalized patient treatment plans and predictive disease imaging.

Here’s a closer look at its potential applications in healthcare:

Enhancing medical images: Generative AI can improve medical imaging, from augmenting X-rays and MRIs to synthesizing and reconstructing images. Predictive models can even generate images to illustrate disease progression over time.
Discovering new drugs: Researchers apply generative AI algorithms, often through generative design, to explore and develop innovative medicines. According to Gartner, by 2025, around 30% of newly developed drugs will make use of generative design principles.
Streamlining patient documentation: Generative AI is also a big help since it can streamline documentation tasks by summarizing patient information, transcribing verbal notes, and extracting important details from medical records with greater efficiency than manual efforts.
Personalized treatment plans: Generative AI analyzes vast patient data, including medical images and genetic testing results, to tailor treatment plans to individual patient needs, optimizing healthcare outcomes.
Telehealth and remote patient monitoring: In the age of telehealth, generative AI supports remote patient monitoring by analyzing real-time health data from wearables and other devices. This enables healthcare professionals to monitor patients’ well-being remotely and intervene promptly when necessary, ensuring seamless continuity of care.

2. Advertising and Marketing

Generative AI and natural language processing provide numerous innovative solutions for professionals in advertising and marketing, enhancing how they create relevant content and interact with customers:

Generating marketing text and images: Generative AI assists marketing professionals in crafting consistent, on-brand text and images for campaigns. This technology also provides language translation tools to expand marketing messages into new markets. Gartner predicts that generative AI will be used to create 30% of outbound marketing materials by 2025.
Producing personalized recommendations: Generative AI fuels powerful recommendation engines, helping customers discover products tailored to their preferences. This interactive process enhances customer engagement and satisfaction.
Creating product descriptions: Generative AI alleviates the burden of creating new product descriptions, providing businesses with efficient solutions to make engaging and informative content for countless products.
Enhancing search engine optimization: SEO specialists use generative AI for optimizing image tags, crafting page titles, and generating content drafts. Tools like ChatGPT or Google Gemini can even recommend changes to improve SEO rankings, streamlining their optimization process.

3. Finance and banking applications

Generative AI has the potential to transform the finance and banking industries, potentially adding $200 billion to $340 billion of value annually, according to McKinsey. Indeed, Fintech companies and banks can adopt generative AI solutions to automate repetitive tasks, boost productivity, and make more informed decisions.

Here are some key potential applications:

Real-time fraud detection: Generative AI can inspect vast amounts of transaction data to detect and intercept fraudulent activities. By identifying patterns and anomalies, it can effectively prevent fraud before it affects customers.
Customized banking experiences: Via customer data visualization, generative AI can offer personalized financial advice, product recommendations, and tailored services, helping improve customer satisfaction and engagement in the banking sector.
Generative AI for credit scoring: GenAI models can also analyze various data points, such as income, employment history, and credit history, to accurately predict an individual’s or entity’s creditworthiness, improving the credit scoring process.
Risk management and fraud detection: Generative AI aids in managing credit, market, and operational risks by examining historical data to identify patterns and anticipate future risks, enhancing overall risk management strategies.
Robotic process automation: Generative AI automates repetitive tasks like data entry and compliance checks, ultimately boosting efficiency and reducing operational costs for financial institutions.

Unlock the true potential of generative AI with Neurond

Generative AI tools are reshaping our world in the near future, driving innovation across diverse industries and applications. From the creative fields of art and music to the precision-focused sectors of healthcare and finance, generative AI’s versatility expands the boundaries of what we can achieve. This technology is not just about automating tasks; it serves as a powerful catalyst for innovation, enabling us to solve complex problems and devise once-unimaginable solutions.

At Neurond, our generative AI consultant services are fueled by deep expertise and relentless innovation, allowing us to deliver customized solutions to organizations around the world. Our team of AI experts leverages extensive industry experience to drive business transformation, harnessing the full potential of generative AI and natural language processing in alignment with customer needs.