See the image above? That's a generative AI use case.
It's created using Picsart, an app that generates pictures based on written descriptions.
Mind-blowing, isn't it? Let's look at more such use cases, but first, what is generative AI?
What is generative AI?
Generative AI is a machine learning subfield that uses algorithms to generate new data, such as images, text, or sounds. It's like a virtual artist or writer churning original art and literature. Except, you know, it's not an artist or writer - just a bunch of clever algorithms working their magic behind the scenes.
Currently, there are two main generative AI models: GANs (Generative Adversarial Networks) and transformer-based models.
GANs are great at creating visual and multimedia content from images and text. Transformer-based models, like GPT (Generative Pre-Trained) language models, can take in information from the Internet and generate all sorts of text, from website articles to press releases to whitepapers.
Why should you care about generative AI?
Well, there are so many reasons. Here are the top three:
- One: it can generate brand-new data that doesn't exist yet. Imagine all the endless possibilities for research and testing!
- Two: it can improve existing algorithms by creating training data for new neural networks or evolving top-notch deep learning architectures.
- Three: it's essentially a machine that designs better machines.
But that's not all.
Gartner has declared generative AI as one of the most disruptive and rapidly evolving technologies in their 2022 Emerging Technologies and Trends Impact Radar report.
And get this - they've made some pretty bold predictions about its future impact.
By 2025, generative AI is expected to generate 10% of all data (currently, less than 1%) and 20% of all test data for consumer-facing applications. Plus, it'll be used in 50% of drug discovery and development projects by 2025.
And by 2027, a whopping 30% of manufacturers will be using it to improve their product development process.
Generative AI is making waves. So, pretty important stuff, right?
Also read: Leading in the Age of Generative AI
Generative AI use cases in different industries
Generative AI has the potential to revolutionize several different industries. Here are a few examples of how it is being used:
Logistics and transportation
Generative AI can accurately convert satellite images into map views, enabling the exploration of previously unknown locations. This can be especially useful for logistics and transportation companies looking to navigate new areas.
Generative AI can help with face identification and verification systems at airports. By creating a full-face picture of a passenger from photos taken from different angles, the technology can make it easier to identify and verify the identity of travelers.
Generative AI can convert X-rays and CT scans into more realistic images, which can be helpful for diagnosis. For example, by using GANs (Generative Adversarial Networks) to perform sketches-to-photo translation, doctors can get a clearer, more detailed view of the inside of a patient's body. This can be especially useful for catching dangerous diseases like cancer in their early stages.
Generative AI can help with client segmentation, predicting the response of a target group to advertisements and marketing campaigns. This can be a valuable tool for companies targeting specific audiences and increasing sales.
You can also synthetically generate outbound marketing messages, enhancing upselling and cross-selling strategies. GPT-3-powered tools like Fireflies AI notetaker lets you get personalized notes tailored to your role in sales, marketing, customer service, or any other area.
Simply ask the bot as many questions as you need to get quick answers without having to sift through the entire transcript. And once you've got your notes, you can use them to automatically generate emails, reports, blogs, and scorecards, saving you even more time and effort.
Top 10 generative AI use cases
Apart from the industries mentioned above, generative AI has multiple use cases, including algorithm invention, neural network design, text, image and music generation, artificial creativity, and creative question-asking.
One generative AI application is to improve data quality by artificially augmenting a data set with additional information similar to the original data set but not seen before. This can help improve the performance of deep learning algorithms, which often require large amounts of high-quality data to work effectively.
Automate the invention of new machine learning algorithms because who has time to do it all by hand? This can save time and resources by allowing the AI to search through possible algorithm combinations and identify promising ones for further development.
Another use case of generative AI is text generation.
Generative AI is like having a personal assistant who can crank out written content for you on demand—your own robot scribe that can generate summaries of articles, product descriptions, or even entire blog posts.
Just feed it some data and let it work its magic. Just think, no more struggling to come up with the perfect words or condense a lengthy article into a digestible summary. With text generation, the possibilities are endless (and so is your free time).
Design neural network
In simpler terms, neural networks are a type of artificial intelligence made up of lots of little brain cells (neurons) connected to each other. These connections can be adjusted (tuned) to help the neural network perform a specific task.
One generative AI application is that it can help figure out which connections work best by searching through different configurations and finding the ones that work the best. This is like giving the AI a set of puzzle pieces and asking it to figure out how to put them together to make the best picture.
Text-to-speech generation uses GANs to create realistic speech audio. The AI is trained to accentuate, tone, and modulate the voice to make it more realistic.
It's like your personal robot voice actor and has a ton of practical uses, from education and marketing to podcasting and advertising. It's cost-effective, flexible, and can even speak in multiple languages.
Plus, it never complains about long recording sessions or demanding directors. Sounds like a win-win to us!
Ready to become the next big music producer? Generative AI can help you create original tunes for advertisements or whatever creative project you have in mind.
Remember that using copyrighted material in your training data can lead to copyright infringement issues. But hey, nothing a little legal advice can't fix! Let generative AI be your muse and get those creative juices flowing.
Another popular generative AI application is turning text into images and generating realistic images based on specific settings, subjects, styles, or locations. This means you can quickly and easily create the visual materials you need.
And you can use these AI-generated images for commercial purposes in media, design, advertising, marketing, education, and more. So, if you're a graphic designer needing the perfect image, let generative AI be your trusty image generator!
Creative question asking
Are you tired of coming up with your questions all the time? Let generative AI take the reins and create some creative ones for you (just like Gmail's Smart Reply feature).
CQA (Creative Question Asking) is about generating thought-provoking questions to stimulate your mind. And the best part? It improves over time by incorporating previous answers into future generations of questioning.
So next time you're struggling to come up with a good follow-up question in that Zoom meeting, let generative AI do the heavy lifting. Don't be surprised if it asks something you never even thought of before.
What is AI-generated art?
One of the most common generative AI use cases is getting creative and coming up with new things that haven't existed before.
Artificial creativity is different from image generation. While image generation and artificial creativity are both generative AI use cases, they have other goals. Image generation aims to generate new images, while artificial creativity seeks to create something new and original without human input.
Take abstract paintings, for instance. Instead of relying on a human artist to develop something unique and original, generative AI can do it all on its own! And if fiction writing is more your thing, generative AI can even crank out a novel without human input. How cool is that?
Generative AI can make videos, from short clips to full-length movies. It can do this by using image generation to create the visual content, text generation to create a script or storyboard, and music generation to create a soundtrack.
It can take all sorts of input data (like images, blogs or articles, and music) and combine and manipulate it creatively to produce something new and unique.
It's like a robot director but with superpowers. So, if you've ever wanted to see a video of a giant robot fighting a giant octopus set to a death metal soundtrack, generative AI might be the way to go.
Generative AI has multiple use cases. It is a compelling and rapidly evolving technology that is revolutionizing several industries and changing how we work.
Whether creating new video games, generating text and images, or improving image recognition systems, generative AI already has a significant impact, and there's no telling what it will do next.
So, buckle up and get ready for the ride because the future of generative AI is looking bright!