Top 100+ Generative AI Applications Use Cases in 2023

The offers that appear on site are from partnerships from which this site receives compensation. This compensation may impact how and where listings appear and this site does not include all offers available in the marketplace. ChatGPT is a new tool from OpenAI that allows you to have a conversation with a chatbot. Knowing how to write prompts correctly is the key to helping you use generative AIs. Based on the Google Design Sprint methodology, the GPT Design Workshop lets you learn, map out ideas, and test a prototype to form a vision within two business days.

Lawyers are burdened with paperwork and research and benefit from the robust capabilities of generative AI. Instead of searching for precedents or specific legal terms in archived documents, they can use AI to do the heavy lifting and concentrate on the creative parts of building the case. Instead of subscribing to a fixed syllabus, they learn from personalized lessons that educators prepare with AI solutions. Moreover, AI allows teachers and coaches to assess students in real-time by analyzing assignments and summarizing the results. There’s much to consider when planning a vacation, including the weather, flights, hotel, and places of interest.

A. Advancements in generative model architectures and techniques

As generative AI matures and develops, it has the potential to extend the reach of AI across this industry even further. One example where generative AI is beginning to make its mark as an image generator is interior design. The tool InteriorAI has been endorsed by interior designer Isabella Penichet Orsi, who wrote on LinkedIn, “Generative A.I. Seeping into interior design with I would highly encourage interior designers to play around with this tool”.

• In addition, you can always rely on generative AI for addressing other creative projects.
• For example, your request for a data-driven bar chart might be answered with alternative graphics the model suspects you could use.
• Trained on vast swathes of the internet, it can produce human-like text that is almost indistinguishable from a text written by a person.

Video Generation involves deep learning methods such as GANs and Video Diffusion to generate new videos by predicting frames based on previous frames. Video Generation can be used in various fields, such as entertainment, sports analysis, and autonomous driving. Speech Generation can be used in text-to-speech conversion, virtual assistants, and voice cloning. Downloadable AI models from sites like Hugging Face must be trained with business-specific datasets before they are helpful for your apps.

#3. Transformer-based model

AI has the potential to rapidly accelerate research for drug discovery and development by generating and testing molecule solutions, speeding up the R&D process. Pfizer used AI to run vaccine trials during the coronavirus pandemic1, for example. Notably, some AI-enabled robots are already at work assisting ocean-cleaning efforts. For now, it’s probably best to look for these tools as the sources of test ideas and to generate template code to drive events. Tools already exist to generate synthetic test data, for accessibility testing, for combinatorial ideas.

These tools can be used in live chat boxes for real-time conversations with customers or to create product descriptions, articles, and social media content. Transformer-based models are a type of deep learning architecture that has gained significant popularity and success in natural language processing (NLP) tasks. A. Generative AI tools are software or systems that employ artificial intelligence to autonomously create content based on patterns and data, like text, images, music, or code.

Discriminative modeling is used to classify existing data points (e.g., images of cats and guinea pigs into respective categories). While both AI and Generative AI are types of artificial intelligence, they differ in their focus on task performance versus content creation. While we’re simply relieved to have basic AI handle many tasks, the truth is we’re only just beginning to tap into the potential of AI with it focused more on content creation. With the ongoing development of these technologies, we can expect to see continued advancements and exciting new applications in the future. Another popular generative AI application is turning text into images and generating realistic images based on specific settings, subjects, styles, or locations.

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.

These models are ‘trained’ (by feeding them the datasets) to facilitate this learning. 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.

On the other hand, GANs work for generative multimedia and visual content from images and text. Generative AI is a form of artificial intelligence in which algorithms automatically produce content in the form of text, images, audio and video. These systems have been trained on massive amounts of data, and work by predicting the next word or pixel to produce a creation. Essentially, transformer models predict what word comes next in a sequence of words to simulate human speech.

One such machine learning model is the Convolutional Neural Network(CNN), which can produce new 3D designs by examining existing ones. These tools can be of great help when you want to generate new data sets for machine learning algorithms to improve efficiency. Generative AI is an exciting and rapidly evolving field of artificial intelligence that is being used to revolutionize the way we interact with technology.

Manufacturers are starting to turn to generative AI solutions to help with product design, quality control, and predictive maintenance. Generative AI can be used to analyze historical data to improve machine failure predictions and help manufacturers with maintenance planning. According to research conducted by Capgemini, more than half of European manufacturers are implementing some AI solutions (although so far, these aren’t generative AI solutions). This is largely because the sheer amount of manufacturing data is easier for machines to analyze at speed than humans.

It is the engine behind most of the current AI applications that are optimizing efficiencies across industries. The main difference between traditional AI and generative AI lies in their capabilities and application. Traditional AI systems are primarily used to analyze data and make predictions, while generative AI goes a step further by creating new Yakov Livshits data similar to its training data. As we already mentioned NVIDIA is making many breakthroughs in generative AI technologies. One of them is a neural network trained on videos of cities to render urban environments. DLSS samples multiple lower-resolution images and uses motion data and feedback from prior frames to reconstruct native-quality images.

Unlike decision-tree-based chatbots and legacy AI like Dialoglow generative AI chatbots develop high-quality, conversational, context-aware responses. Flow-based models have applications in image generation, density estimation, and anomaly detection. They offer advantages such as tractable likelihood evaluation, exact sampling, and flexible latent space modeling. Auto-regressive models are commonly used in text generation, language modeling, and music composition.

Human resource management involves regular performance reviews, where managers provide employees with personalized recommendations and development plans. For example, Plai, an online HR management solution, uses generative AI to provide recommendations and suggest follow-up actions based on individual feedback. 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.

When building the application, we consider scalability, data security, and error handling. Understandably, generative AI systems require immense computing power, and we might recommend shifting to GPU or TPU-based machines. Generative AI can be trained with the range of products an online retail store offers. When deployed as a chat agent, they can also style conversations with the preferred brand voice. This way, customers can initiate a request conversationally, such as ‘Show me a dress for casual dining”, and the chatbot will return the appropriate products.