Top 10 Generative AI Applications Use Cases & Examples 2023

It can also work with rough sketches or wireframes and offer a finalized version of the design. This first wave of Yakov Livshits resembles the mobile application landscape when the iPhone first came out—somewhat gimmicky and thin, with unclear competitive differentiation and business models. However, some of these applications provide an interesting glimpse into what the future may hold. Once you see a machine produce complex functioning code or brilliant images, it’s hard to imagine a future where machines don’t play a fundamental role in how we work and create. Downloadable AI models from sites like Hugging Face must be trained with business-specific datasets before they are helpful for your apps.

Generative AI can be deployed in the banking sectors for various processes with the help of these AI tools the banks can detect the fraud that happens within the banking operation. This AI tool improves data privacy by holding away data from third persons and other employees in the banks. The AI knowledge will be used to calculate the risk of the funds that are deposited and the funds the bank manages regularly. Other than this banks leverage generative AI for KYC, processing loans, answering customer needs, and all other related processes for the customers.

Video and speech Generation

This helps ensure that each student, especially those with disabilities, is receiving an individualized experience designed to maximize success. It can also be used to generate text that is specifically designed to have a certain sentiment. For example, a generative AI system could be used to generate social media posts that are intentionally positive or negative in order to influence public opinion or shape the sentiment of a particular conversation.

• It helps you determine if it truly makes an impact on your operation and does not merely serve as a good-to-have accessory.
• Machine learning is the ability to train computer software to make predictions based on data.
• Maket is an AI tool that empowers architects, designers, builders, contractors, and developers in the residential industry.
• What used to be a physical process (cameras, actors, studios…) has now transitioned into a fully digital realm, making video creation convenient and accessible to all.
• The insurance companies can use these scenarios to understand potential future outcomes and make better decisions.

Synthetic data can be used to create shareable data in place of customer data that cannot be shared due to privacy concerns and data protection laws. Further, synthetic customer data are ideal for training ML models to assist banks determine whether a customer is eligible for a credit or mortgage loan, and how much can be offered. Another use case of generative AI involves generating responses to user input in the form of natural language. Generative AI models can generate realistic test data based on the input parameters, such as creating valid email addresses, names, locations, and other test data that conform to specific patterns or requirements. An audio-related application of generative AI involves voice generation using existing voice sources. With STS conversion, voice overs can be easily and quickly created which is advantageous for industries such as gaming and film.

Why use generative AI tools?

ChatGPT and other similar tools can analyze test results and provide a summary, including the number of passed/failed tests, test coverage, and potential issues. Tools like ChatGPT can convert natural language descriptions into test automation scripts. Understanding the requirements described in plain language can translate them into specific commands or code snippets in the desired programming language or test automation framework.

Additionally, it can help detect and resolve bugs in the generated code by analyzing code patterns and identifying potential issues before suggesting solutions. Furthermore, generative AI can ensure that the code adheres to style guidelines, promoting consistency and readability throughout the codebase. Generative AI has found a new application in software development, where it is being used to create code without the need for manual input. This exciting new technology makes coding accessible to both professionals and non-technical individuals alike. The process of creating images involves changing the external aspects of the image, such as its color, medium, or form while retaining its core elements. Super-Resolution GANs, which are based on GAN (Generative Adversarial Network) technology, can be used to produce high-resolution versions of images.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Use cases for generative AI, by industry

Hundreds of other industry employees have also put their names to the statement, along with celebrities that include musician Grimes and podcaster Sam Harris. Many youngsters are looking at the Generative AI use cases where they can implement these AI models in multiple areas and for various industries. Training a top-performing generative AI model requires annotation, effective visualization, and curation Yakov Livshits of voluminous datasets (hundreds of thousands of data items and beyond). Though it is extremely challenging to find a single tool that covers all stages of the pipeline individually — this is where SuperAnnotate steps in to offer an all-in-one solution for all your data needs. By leveraging SuperGen, you can add diversity to your data and potentially minimize dataset bias before it goes to training.

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. 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.

The eye-catching image gracing the top of this page showcases Midjourney’s capabilities, having been created with this promising platform. It’s also worth noting that generative AI capabilities will increasingly be built into the software products you likely use everyday, like Bing, Office 365, Microsoft 365 Copilot and Google Workspace. This is effectively a “free” tier, though vendors will ultimately pass on costs to customers as part of bundled incremental price increases to their products. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

These models are trained on massive datasets to understand patterns and underlying structures. The models learn to create new instances that mirror the training data by capturing the statistical distribution of the input data throughout the training phase. Once trained, these models can generate new text that is similar in style and tone to the input data. You should understand the working of generative AI models to find out their impact on the existing digital landscape.

The goal is to increase the diversity of training data and avoid overfitting, which can lead to better performance of machine learning models. Together with cinema, the video game industry is another entertainment realm that relies on moving images, and generative AI can lend a helping hand as well. Software developers’ efforts can be lightened, Yakov Livshits and development duration considerably reduced when AI algorithms generate 3D models utilized in computer games. Such models can be absolutely new or stem from 2D images previously entered into it. And 2D pictures can also be generated by the technology to find further use in specific game and cartoon genres, for instance, anime.