What is Generative AI: Exploring Examples, Use Cases, and Models
AI-powered generative technology is more than just a buzzword. It’s a cutting-edge tool that transforms business operations by automating key activities like content creation, image generation, and coding.
The adoption of generative AI is increasing across business domains, and why not? After all, if harnessed well, it can significantly reduce the overall time, effort, and cost needed to run the business. The fashion industry, for example, is leveraging AI to produce visually stunning one-of-a-kind designs.
In December 2022, designers at Laboratory for Artificial Intelligence in Design (AiDLab) in Hong Kong organized a fashion show named “Fashion X AI.” Each design showcased in this fashion show was designed by AI using mood boards, color palettes, and ideas uploaded by designers.
Similarly, there are tons of other use cases for generative AI. This article will shed light on generative AI, its use cases, and practical examples to improve ROI for your projects.
What is generative AI?
Generative AI is a technology that uses data sets to produce something new in response to a prompt entered by a human. The output could include poetry, a physics explanation, an image, or even new music.
You can also use generative AI models to create data and insights for your business activities. For example, using your proprietary data, a generative AI model can craft specific questionnaires for your CRM platforms to gather user feedback.
Advantages of generative AI models
- Improved decision-making: Generative AI models can provide insights and predictions based on data analysis, helping businesses make more informed decisions.
- Innovation: Businesses can generate ideas for new products and processes using generative AI. These practices foster innovation and help teams stay ahead of the curve.
- Personalization: Generative AI models can be trained on individual user data to facilitate personalized customer experiences.
- Accelerated TAT- Generative AI helps generate new data faster, improving turnaround time for your projects. This is also helpful in improving business agility and adapting to changing customer demands.
Disadvantages of generative AI models
- Resource intensive: Training a generative AI model is an extremely complex process requiring much time and resources. Even then, ensuring high accuracy in its output is not easy.
- Slower responses-Generative AI can have higher latency due to the massive scale of operations. This means slower responses in real-time interactive apps like chatbots or AI voice assistants.
- Unreliable results: Generative AI relies on high-quality data to make accurate predictions. So incomplete, inaccurate, or outdated data can lead to unreliable results.
- Security argument- Many organizations have argued on using user data to train AI models. There are concerns regarding the security of user data, privacy, and accessibility.
- Bias and ethical concerns: If the training data contains bias or prejudice, generative AI models can perpetuate those biases in their output.Plus, using generative AI models for creating fake content raises ethical concerns about the potential for misinformation and manipulation.
Uses of generative models
#1. Text generation
This is a use case of generative AI contributing the most to the rising popularity of AI adoption in content creation. Generative AI tools like ChatGPT are widely used by individuals and businesses alike.
Text generation with generative AI models reduces the time and effort required to create new content. This is especially helpful for marketing campaigns where businesses must produce large amounts of content quickly and efficiently.
Moreover, these tools can also help create text-based reports and perform complex business calculations.
The image above showcases an example of using chatGPT to calculate the return on ad spending through a prompt. Similarly, you can use it for business reports and calculations.
#2. Sentiment analysis
Sentiment analysis is another use of generative AI, which involves text analysis to determine the user’s sentiment or emotion. This can be useful for companies that want to monitor customer sentiment toward their products or services. Sentiment analysis can also be used in social media monitoring, market research, and more.
Take an example of a generative AI tool that helps analyze the sentiment behind any written text. It analyzes the syntax and context of the text to provide whether the sentiment is positive or negative.
#3. Image generation and enhancement
You can use generative AI for image generation and enhancement. Such generative AI tools use machine learning algorithms to create everything from abstract art to photorealistic landscapes. Moreover, they can also enhance images by improving image quality, such as removing noise or improving color balance.
Here is an example of a generative AI tool you can use to upscale images by 200 or 400%.
#4. Video creation
Using machine learning algorithms, generative AI tools can also create videos based on your text prompts or data inputs.
The video creation feature is particularly useful to advertising, entertainment, and education businesses. Marketers can also use tools based on AI models to create everything from short advertisements to full-length feature films.
Check out this super helpful generative AI tool that helps you create videos and customize them in a jiffy.
#5. Code generation
Code generation is another exciting use of this wonderful technology. Generative AI models like GPT-3 can be trained on large amounts of code from various programming languages to create new code. AI-assisted code generation can be used to automate the process of creating website templates, building API clients, or even developing entire software applications.
Ultimately, code generated by a generative AI model can speed up the development process and reduce the need for manual coding. A simple example is Open AI’s Playground which lets you create programmable commands through text prompts.
#6. Speech to Speech conversion (STS)
Speech-to-speech conversion is an impactful feature of most generative AI models. It involves the conversion of one natural language to another in real-time. This can be useful for various applications, such as language translation and interpretation.
#7. Text-to-Speech generation (TTS)
Text-to-speech generation refers to converting written text into spoken audio using natural language processing. This feature can automate tasks such as creating audiobooks, building voice assistants, and more.
#8. Audio generation
Using machine and deep learning models, you can use generative AI to create new audio content. With just a few clicks, you can use AI models to create everything from music to sound effects to voiceovers.
#9. Synthetic data generation and augmentation
Synthetic data generation involves creating unique data from the input of the original dataset. This is useful when there is not enough data to train a machine-learning model or when it is difficult to obtain new data.
Synthetic data can generate images of objects that do not exist in the real world, such as a new type of car or a fictional creature. For example, Dall-E uses multiple models, including a transformer, a latent representation model(LRM), and CLIP, to translate English phrases into code.
Further, this code generates images and ranks existing images based on how closely they relate to the given phrase.
Use cases of generative AI models across domains
Generative AI technology automates text or image generation, offering intelligent recommendations in healthcare, arts, social media marketing, and other domains.
Generative artificial intelligence has made significant advancements in the healthcare industry. For example, AI scrutinizes medical records, symptoms, and images, to aid medical professionals in accurately diagnosing illnesses.
Healthcare professionals can use generative AI to create personalized patient plans based on their medical history, genetic makeup, and personal preferences. They can also integrate it with IOT or wearable devices to monitor patients’ health and offer instant recommendations.
#2. Art and Animation
Advanced AI technology has dramatically changed how the art and animation industry operates. Art creation has become easy, and a text prompt is enough to create artistic pieces. Generative model StyleGAN, for example, has made it possible to create real human faces and unique works of art in different styles.
Moreover, generative AI can assist artists and animators by providing them with new ideas and exhaustive features to enhance their artwork. One easy but very useful use case is generating many variations of an artwork.
A popular example of the use of generative AI in the field of animation is “The Dog and the Boy,” a short anime movie created by Netflix Japan. Do give it a watch, it’s super impressive!
#3. Marketing and Sales
You can leverage generative AI for marketing and sales campaigns to create personalized content without compromising users’ privacy.
For example, Markov chain models can analyze past purchase histories to provide product recommendations customized to each customer’s preferences.
Not only does this increase sales, but it also enhances customer satisfaction. Generative language models can also help businesses with customized advertising copy and product descriptions.
#4. Software Programming
The generative AI technology can help automate software programming tasks using LSTM (Long Short-Term Memory) network, which generates new code based on existing code.
This model can significantly improve the speed and efficiency of programming large language models. You can use generative AI models to create new software applications.
These tools can be of great help when you want to generate new data sets for machine learning algorithms to improve efficiency.
Generative AI models can generate new financial data or conduct automated financial analysis tasks. One example is the Variational Autoencoder model, which can create artificial financial data to train machine learning models for financial analysis.
Such models can help fintech companies produce innovative trading strategies and predict future market trends.
The manufacturing industry can benefit from machine learning models to enhance production processes and create product designs. One such machine learning model is the Convolutional Neural Network(CNN), which can produce new 3D designs by examining existing ones.
This technology, used in tandem with generative AI models like GANs, can aid designers in producing innovative products that meet customer demands.
There are powerful generative AI tools that media houses and entertainment companies use to generate original content automatically. One of the more popular tools is the Recurrent Neural Network(RNN).
It is a powerful technology for creating new music by analyzing existing musical patterns. Additionally, generative models can produce fresh video game content, including levels and characters, and generate new video content for streaming platforms.
How do generative AI models work?
Generative AI models work by learning the patterns in a dataset and then using that knowledge to create new content similar to the original data. These models are ‘trained’ (by feeding them the datasets) to facilitate this learning.
Depending on the type of data set being used and the desired outcome, generative AI training techniques can involve deep learning, adversarial learning, reinforcement learning, and more.
#1. Generative vs. Discriminating models
Generative models differ from discriminating models designed to classify or label text based on pre-defined categories. Discriminating models are often used in areas like facial recognition, where they are trained to recognize specific features or characteristics of a person’s face.
Generative models try to model the underlying distribution of the data. In other words, they try to understand the structure of the data and use that understanding to generate new data similar to the original data.
Discriminative models, on the other hand, focus on the differences between the data. They try to learn a boundary that separates the different classes or categories of data.
#2. Generative Adaptive Network (GAN)
Generative Adaptive Networks, or GANs, are also a type of neural network used in machine learning to generate new data from existing information. There are two main components of GANs – a generator and a discriminator.
- Generator –It creates new data by analyzing the data pattern based on user input.
- Discriminator – This component evaluates whether the data is real or fake.
Whenever there is user input/prompt, the generator will generate new data, and the discriminator will analyze it for authenticity. Feedback from the discriminator enables algorithms to adjust the generator parameters and refine the output.
This process continues until the generator produces data indistinguishable from the input information.
GANs have several practical applications across business domains, and so, many popular tools leverage this model to offer useful features:
- StyleGAN –It’s a type of GAN that helps generate high-quality images with realistic fixtures and details in image synthesis.
- CycleGAN – This tooluses GANs for image-to-image translation and converts one style to another.
- DALL-E –It is an AI tool that uses GANs to convert text prompts into images.
- GauGAN-Nvidia has developed this tool which uses GANs to generate realistic landscapes from simple sketches.
#3. Transformer-based model
A Transformer-based model is a type of neural network used for various natural language processing tasks such as machine translation, text summarization, and language understanding.
The Transformer model consists of two main components: the encoder and the decoder. The encoder processes the input sequence while the decoder generates the output sequence.
One example of a Transformer-based model is the GPT-3 language model, which can generate coherent and contextually relevant text when given a prompt.
The transformer model uses a mechanism called “self-attention” to identify the relevance of each word in a prompt and how they relate to each other in the context of the input sequence.
Upon understanding logical relationships between words in the prompt, these models are able to understand the instructions well and produce a coherent output.
Prominent examples of generative AI tools
Generative tools have transformed the way content gets created for different business requirements. However, it is not restricted to text generation and there are generative AI tools for different use cases like code generation, data synthesis, video creation, and more.
ChatGPT is a significantly popular language model trained by OpenAI that can be used for various natural languages processing tasks, such as text generation, question answering, and language translation.ChatGPT has been pivotal for AI-based agent development.
It uses generative AI technology to generate text responses for human prompts. Open AI offers ChatGPT APIs that you can use to build customized chatbots.
#2. GitHub Copilot
GitHub Copilot is a tool that helps developers write code faster by suggesting pieces of code that fit with what they’re writing.
It uses Codex technology, a deep learning model trained on a large data set of source code. GitHub Copilot can significantly save time and reduce errors in the coding process.
Firefly can create high-quality images and stunning text effects from just textual inputs. Adobe powers it to help users create image art for different purposes.
Using Firefly, you can create designs across Creative Cloud, Document Cloud, Experience Cloud, and Adobe Express workflows.
Pictory.AI is a generative AI tool that can create short-form videos from long-form content. You can use Pictory.AI to transform long Youtube videos into shorts or reels for Instagram.
Midjourney is an AI image generator that uses a Discord bot to create realistic and artistic images based on text prompts. Users can edit, upscale, and download their creations. It also helps generate descriptions for images based on user input.
This tool generates “pretty images” that are aesthetically pleasing rather than just functional.
Wordtune is an AI writing assistant that helps users improve their writing style, tone, clarity, and fluency. It can rewrite sentences, suggest alternative words, generate questions, and more.
Wordtune is powered by natural language understanding and generation technologies developed by AI21 Labs.
Gretel is an AI platform that helps users create synthetic data for testing, training, and sharing purposes.
It can anonymize, label, augment, and synthesize data from various sources and formats. Gretel aims to make data safe and accessible for everyone.
#8. Genie AI
Genie AI summarizes long texts into shorter, easy-to-read synopsis. It uses advanced NLP techniques to identify key themes and ideas in the text and create accurate summaries.
You can also customize the kind of output you want; for example, whether you need a brief overview of the text or a detailed summary.
How does Simform power AI-based virtual agents for businesses?
Artificial Intelligence algorithms are not new, but generative AI has been empowering a new way of using this technology for business automation. Companies can now generate unique data rapidly, engage customers, and provide personalized content.
The most attractive use case of generative AI is a virtual agent that offers natural language conversation with customers.
Simform has been at the forefront of developing AI-based agents which help businesses personalize user interactions. If you want to integrate the power of generative AI into your business, contact us for a free 30-minute consultation.