The days are gone when humans were the only intelligent species that could comprehend information and make a judgment or take action. Now we have a whole new species of tech that has the potential to do the same but with extraordinary capabilities. Yes, we are speaking non-other than Generative AI, the new sizzling sensation emerging as one of the greatest technologies.
This AI can generate data that can provide n number of benefits. From real-time image, video, and art creation to gene sequencing, it provides endless applications. As we enter 2023, the possibilities of this revolutionary AI are becoming endless, with plenty of exceptional use cases being discovered every day for every sector.
To give you a broader perspective of its applications, we have listed some of the top 25 use cases of generative AI in 2023 that are worth checking out. So let’s get things going.
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What is Generative AI?
Traditional AI and Generative AI are much different. The traditional AI’s abilities were limited to detecting patterns, making decisions, performing accurate analytics, and predicting flaws and improvements. Whereas the generative refers to nothing but data generation and is used for that exact purpose to generate text, audio, video, and other media.
How Does it Work?
The success story of generative AI follows two machine-learning classes
- Generative system- The one that generates text, video, audio, and other media using computer-controlled systems.
- Discriminative system- The one that draws the boundary between what is fake and real such as mistakes in texts, people in pictures, etc.
Both generative and discriminative systems are built on top of neural networks, and once the input is given, they produce a decent output. Further, based on the feedback on how good the output was, both generative and discriminative systems modify their internal operations and structure to produce the feedback-oriented output, as shown in the above image.
Today’s generative AI tools, such as ChatGPT, combines these two AI classes in the GAN (generative adversarial network) model, where different AIs help train and improve the performance and output of both systems to each other, resulting in a powerful machine learning model that learns, adapts and evolves with new information.
For instance, these GANs can be used to create realistic images where generative systems could synthesize the images. Meanwhile, a person could pass real images through a discriminative system to predict which is fake and which is not, ultimately training both models to perfection.
Generative AI Use Cases
Generative AI has endless possibilities and uses cases in every sector because this technology evolves as the sector advances, causing new ways to emerge and further enhancing their operations. On top of it, this technology improves personal experiences and even streamlines the workflows of researchers, engineers, creative artists, and scientists.
With upcoming advancements, it’s predicted that generative AI will transcend unlike any other technology and serve real-time use cases within each industry.
Top 25 Generative AI Use Cases
1. Image Synthesis
The generative AI has the power to convert text into images; for instance, the Dall-e tool soon became the choice of many to generate artistic images. These image synthesis tools let the user write prompts on what he wants in the image, for instance, type of art, color choice, details of the scene, location, etc., by which the image synthesis tools such as Dall-e generate the exact visual materials.
Now this might not be useful for a general user, but it could benefit marketing, sales, education, media, design, and advertising sectors to use these image synthesis tools for commercial operations.
The below image is an example of image synthesis; generated by Dall-e.
Super-resolution refers to a process where blurry images are processed and turned into high-quality images by introducing pixels with accurate color around the blurry areas of the image.
Generative AI possesses the same capability of enhancing images as it generates new data based on existing ones; this means the GAN model can utilize the generative and discriminative system to generate new pixels based on the existing image’s data, resulting in high-quality images.
This benefits an average user’s demands where he could easily turn his past blurry images into high-quality ones. Moreover, it is beneficial for medical use to get detailed imagery of organs for proper diagnosis and treatment.
3. Image-To-Photo Translation
Image-to-image or image-to-photo translation is a method of converting a semantic sketch or image into a realistic one. This is done exactly through the same generator and discriminator in the GAN model.
Using these tools, a person can draw sketches and get the realistic images he wants. For instance, healthcare professionals can use this tool to diagnose a patient, and police can use this tool to get variations of real images of a criminal to hunt him down.
4. Video Prediction
As the name suggests, video prediction is nothing but predicting the anomalies in the video. Using the video-based GAN prediction model, the generative AI can comprehend temporal and spatial elements present in the video, generate patterns or sequences based on the video data, and determine the probable situation that might happen.
This is useful in traffic surveillance cameras to identify accident areas.
Read Our Blog: Top 10 Generative AI Trends To Watch Out In 2023
5. Text Generation and Personalized Content Creation
Having trained over huge volumes of data sets, generative AI tools such as ChatGPT can now generate texts by following proper grammar, tense, and wording rules. This generated content is most beneficial for companies, such as for marketing propaganda to generate ads, social media posts, and scripts for marketing purposes.
A user working in the company can even personalize the content, for example, by writing a prompt that specifies its type, audience, and tone. For instance, the personalized content could be articles, any other website post, a recommendation list of products, software, etc.
6. Sentiment Analysis, Text Classification
Since the rise of generative AI, humans are no longer the only ones who can understand the emotions hidden in words, that’s right Generative AI can do this too, and it’s known as sentiment analysis.
It is also known as opinion mining and relies on NLP and text mining to identify the emotional context hidden within the words and accordingly categories/label them in three categories; positive, negative, or neutral.
Furthermore, a person can even command a generative AI tool to write words with certain sentiments, such as expressing appreciation, condemning any activity, etc.
This is mostly useful for companies where they can introduce a support chatbot to answer customer queries and experiences. For instance, if the customer expresses product defectiveness, the chatbot can automatically answer in a tone that shows appreciation and also pitch apologetic sentences to retain customers. This makes companies emotionally involved with the customers to get their attention and avoid losing them, especially when they do something wrong.
7. Code Generation, Review, Debugging, and Style Check
Code generation is another capability of generative AIs. Tools such as ChatGPT and others have shown their potential to write accurate codes for specific programs. This means professionals no longer have to worry about programs that must be added repeatedly; instead, generative AI can do that job.
Moreover, the AI tools can help review the codes to identify flaws, debug programs, refract codes, and style checking to properly structure codes as per standards.
8. Data Synthesis
The generative AI can create data statistically similar to any real-world data; however, it doesn’t show any real-world parameters but can be used to train ML models, ensuring data privacy and creating new data from the customer’s inputs.
9. Text-To-Speech Generator
Text-to-speech has been used for decades now, but it always has produced unnatural voices, which never really made any sense. However, with GAN and generative AI, text-to-speech is much more natural and sounds like a human.
A person can simply write the script and get an engaging human voice. This could benefit various media, education, podcasting, video generation, and marketing businesses, as they can create appealing content to attract potential audiences.
10. Music Generation
Music generation is another use case that generative AI has. AI-powered music generation tools can create music just how a user wants by specifying its genre, culture, and type. This will mostly benefit the media and entertainment industry as they can create sounds and music for ads, video intros, outros, and other video and audio content.
Industry-Specific Use Cases
11. Drug Discovery And Development
Generative AI can create drug candidates for clinical trials to test the efficacy with computer simulation to accelerate the process of discovering and developing new drugs for critical diseases.
This all started when Exscientia brought an AI-designed drug candidate to a clinical trial in 2021; from that day, many other companies are also considering AI-designed drug candidates for specific clinical trials.
12. Genetics Research
We often criticize that AI will take over the world and often forget the good part it offers, and one of them is genetic research. Generative AIs have become widely used in gene research, where they help researchers find out how gene expression will change in response to specific changes in genes. This helps reduce the time taken to research and accelerates the development of gene therapies, and even enhances the therapy and treatment by predicting which treatment the patient’s genes best respond to.
13. Enhanced Medical Imaging
Generative AI is now being considered for medical imaging such as CT scan that predicts the inner anomalies of a human body. By utilizing the power of machine learning algorithms, medical imaging is done much more accurately, which helps with the proper diagnosis.
14. Creative Fashion Designing For Designers
Generative AI tools such as Dall-e can generate images that just look real; similarly, there are other tools emerging in the fashion domain that generate creative fashion designs for designers to keep up with the trending fashion.
It also helps fashion designers to add new resources in generative AI models to optimize their choice of design further.
15. Generating Fashion Models
Fashion brands require fashion models who could portray their clothing; however, it is a time-consuming process involving properly sized clothing, various photoshoots, and finally, showcasing their fashionable clothing.
Generative AI is simplifying this tedious process with a tool to generate fashion models. The fashion brand can easily render 3D models to showcase their fashionable clothing better.
16. Fashion Industry Market And Trend Analysis
The generative AI tools can be configured to know the customer’s personalized choices, which then helps understand their changing clothing demands. Here machine learning and probabilistic programming can play a key role in determining customer desires and generating personalized choices of designs and products for targeted customers.
By analyzing the trends, the brands can also ask generative AI tools to build strategies for marketing purposes, such as email marketing to push personalized fashionable clothing insights for each target audience. Similarly, this marketing strategy can be used on social and websites to get customers’ attention and increase sales.
17. Personalized Lessons, Course Planning, And Design
Generative AI tools can suggest personalized lessons for students based on their previous track record and performance for effective and tailored education. Moreover, based on the students’ improvements, the AI can predict their grasping ability and accordingly plan and design the curriculum and assessments with difficulty levels, simplifying each student’s learning.
18. Content, Image, And Video Creation For Lessons
Generative AI tools such as ChatGPT, Dall-e, and other AI-powered animating tools can help teachers create appealing and eye-catching lessons with plenty of simplified content, images, and videos for easy understanding of complex concepts to uplift learning experiences.
On top of it, teachers could use various materials from different textbooks and research publications and combine them into one to provide students with diverse knowledge.
Students of all grades can interact with a chatbot or a virtual chatbot for tuition. The generative AI tool, ChatGPT, is a brilliant example that can explain the most difficult concepts to people of all ages. This way, students don’t have to rely on private tuition and can instead use chatbots as their tutors.
20. Fraud Detection
Today, scams are everywhere, and these cyber criminals always develop new techniques to pull off fraudulent transactions. Generative AI can put an end to these activities with a tool to detect fraudulent transactions or any malicious activity within any account.
This could be done by training GAN and machine learning models with fraudulent sets of transactions so the AI can learn, detect and prevent these changing frauds.
21. Risk Management
Using the generative AIs GAN models, banks can create scenarios such as loss with near real-life data. It’s nothing but predicting market forecasts to prepare for bitter market crashes and ready a plan to survive even in the volatility.
22. Contract Generation And Compliance
The generative AIs such as the ChatGPT can generate a legal contract based upon the criteria and terms on which involved parties agree. This saves time, effort, and money for everyone and streamlines processes between two parties to begin the execution of the contract early without any hassles.
23. Supply Chain Management And Demand Forecasting
Generative AI models can help companies predict the demand for their products and accordingly plan their supply chain operations based on the number of customers, product type, and other parameters to bear the min loss or get the best business results. This helps companies reduce wastage and unnecessary spending on the supply chain and improve delivery time.
Travel and Tourism
24. Identity Verification
Generative AI has been used to generate images, manipulate their elements and change certain conditions. Similarly, generative AI can help in the identity verification of tourists in airports’ and everywhere. This is possible with GAN and machine learning modules that process the tourist’s ID image from different angles to verify that it is him.
25. Destination Recommendations
The user can use generative AI tools such as ChatGPT to get the best destination recommendation based on their past journey, personal opinions, geographical location, and culture. This would allow them to spend the money on the right destination and bring back memorable experiences.
There you go! The list of top generative AI use cases in 2023. The list includes general and industry-specific use cases to give you a better idea of how it’s helping sectors evolve and better serve humanity.
That being said, Generative AI is still in its infancy. But if we look at its track record, within a year, it reached a level no one expected, and it will, from here on, jump to a whole new level where we will soon see sectors advancing like never before, disrupting the entire human world.
However, admiring how it’s evolving daily won’t bring you the ripest fruits. You must invest in this technology and get a generative AI built specifically for your business operations from a capable Generative AI development company to get the unimagined benefits. Because ready on not the battle to capture the market is on, there is no denying that generative AI will be everyone’s weapon of choice to do so.
SoluLab, a renowned Generative AI development company, offers a comprehensive suite of Generative AI development services tailored to diverse industries and business verticals. Their team of skilled and experienced Artificial Intelligence developers harnesses cutting-edge Generative AI technology, software, and tools to create bespoke solutions that cater to unique business requirements. From streamlining business operations to optimizing processes and elevating user experiences, SoluLab’s Generative AI solutions unlock new possibilities for businesses seeking a competitive edge. For custom, high-quality content that sets businesses apart from their competitors, they provide expertise in AI technologies such as ChatGPT, DALL-E, and Midjurney. Companies looking to leverage these tools can hire Generative AI developers from SoluLab and discover the transformative potential of their AI-driven offerings. To explore the possibilities, reach out to SoluLab today.
1. What are the use cases of Generative AI?
The use cases of generative AI go way beyond human comprehension, but for the time being, its best use cases are nothing but to generate data of any kind, from audio, video, text, and others. Apart from this, it also has industry-specific use cases such as gene sequencing in genetics research, generating creative fashion designs in the fashion industry, personalized lessons for students’ education, etc.
2. Is Generative AI the future?
Yes, generative AI is the new revolution that will dominate every sector to advance and accelerate their operations; for instance, it will be responsible for 30% of new drug discoveries and development in the pharmaceutical sector by 2025. This means we may finally have a cure for some chronic diseases.
3. What are the trends for Generative AI 2023?
Here are some trends of generative AI in 2023:
- AI-assisted creativity
- AI for advanced scientific research
- NLP applications
- AI-generated content for marketing
- Advancements in GANs
4. What is the smartest AI 2023?
ChatGPT is the smartest AI of 2023 and then comes the rest, such as Deepmind (Alpha Go), Watson by IBM, Tesla autopilot, Sophia, etc.