What Is Generative AI? Definition, Applications, and Impact

Generative artificial intelligence (AI) is a type of AI that generates images, text, videos, and other media in response to inputted prompts.

AI generators like ChatGPT and DALL-E are gaining worldwide popularity. These programs respond to prompts input by users. Submit a text prompt, and the generator will produce an output, whether it is a story or outline from ChatGPT or a monkey painted in a Victorian style by DALL-E.

In this article, we’ll outline what you should know about this growing field, how it works, uses cases, and more. Afterward, if you're interested in building foundational generative AI skills, consider enrolling in DeepLearning.AI's Generative AI for Everyone course.

What is generative AI?

Generative AI, also referred to as GenAI, allows users to input a variety of prompts to generate new content, such as text, images, videos, sounds, code, 3D designs, and other media. It “learns” and is trained on documents and artifacts that already exist online.

Generative AI evolves as it continues to train on more data. It operates on AI models and algorithms that are trained on large unlabeled data sets, which require complex math and lots of computing power to create. These data sets train the AI to predict outcomes in the same ways humans might act or create on their own.

Read more: Artificial Intelligence (AI) Terms: A to Z Glossary

The rise of generative AI is largely due to the fact that people can use natural language to prompt AI now, so the use cases for it have multiplied. Across different industries, AI generators are now being used as a companion for writing, research, coding, designing, and more.

What is generative AI vs AI?

Generative AI is a type of machine learning focused on building generative models that are capable of producing a wide range of AI-generated content, including human-like text, images, and audio. As a result, it's a subset of AI in general, which is essentially defined as the pursuit of creating machines capable of exhibiting (or exceeding) human intelligence.

Generative models, also known as "large language models (LLMs)," are a type of deep learning neural network designed to mimic the human brain. They are produced by training complex algorithms on massive datasets. While traditional AI is typically designed to perform a narrow range of tasks repetitively, GenAI is capable of producing original content in response to a wide variety of user inputs, allowing it to respond dynamically to users' needs in real-time.

Read more: AI vs. Generative AI: The Differences Explained

How does generative AI work?

Generative AI models use neural networks to identify patterns in existing data to generate new content. Trained on unsupervised and semi-supervised learning approaches, organizations can create foundation models from large, unlabeled data sets, essentially forming a base for AI systems to perform tasks [1].

Some examples of foundation models include LLMs, GANs, VAEs, and Multimodal, which power tools like ChatGPT, DALL-E, and more. ChatGPT draws data from GPT-3.5 or 4 and enables users to generate text based on a prompt. Another foundation model Stable Diffusion enables users to generate realistic images based on text input [2].

Hear more about how generative AI works in this lecture from DeepLearning.AI's Generative AI for Everyone course:

Popular AI generators

There are several generative AI platforms you can become familiar with. You may find them helpful for automating certain processes in your workflow.

• ChatGPT: This language model has a foundation of GPT architecture that generates text that resembles something a human would produce. It's a helpful companion for research, strategy, and content creation.

• DALL-E3: This model generates images from text prompts, so creatives can create vibrant illustrations and concept art that’s a useful accompaniment to content marketing. 

• Microsoft Copilot: This model, based on ChatGPT, is integrated into Microsoft's popular productivity suite of applications and is also available as a stand-alone product offering. 

• Google Gemini: Google's answer to ChatGPT, Gemini is integrated into Google's own suite of productivity applications but is also accessible as a stand-alone chatbot.

Read more: 6 Artificial Intelligence (AI) Jobs to Consider

Generative AI use cases

Once you’ve decided which AI generator suits your needs, these use cases may help you get the creative juices flowing for ways generative AI can benefit you and your business.

• Writing or improving content by producing a draft text in a specific style or length 

• Adding subtitles or dubbing educational content, films, and other content in different languages

• Outlining briefs, resumes, term papers, and more

• Receiving a generic code to edit or improve upon

• Summarizing articles, emails, and reports

• Improving demonstration or explanation videos

• Creating music in a specific tone or style

Generative AI has many use cases that can benefit the way we work, by speeding up the content creation process or reducing the effort put into crafting an initial outline for a survey or email. However, generative AI also has limitations that may cause concern if it remains unregulated.

Concerns about generative AI

Generative AI’s popularity is accompanied by concerns of ethics, misuse, and quality control. Because it is trained on existing sources, including those that are unverified on the internet, generative AI can provide misleading, inaccurate, and fake information. Even when a source is provided, that source might have incorrect information or may be falsely linked. 

Since generators such as ChatGPT allow humans to input prompts with everyday language, it has become easier to use– so much so, that university students might use it to plagiarize or generate essays, and content creators may be accused of stealing from original artists. Falsified information can make it easier to impersonate people for cyber attacks.

Read more: AI Ethics: What It Is and Why It Matters

Build your generative AI skills on Coursera

Starting building your AI skills with one of these courses or specializations on Coursera:

For a quick, one-hour introduction to generative AI, consider enrolling in Google Cloud’s Introduction to Generative AI. Learn what it is, how it’s used, and why it is different from other machine learning methods.

To get deeper into generative AI, you can take IBM’s Generative AI Fundamentals Specialization course and learn the potential of this new AI technology. This course is designed for beginners with no prior experience and covers topics like prompt engineering, generative AI careers, foundation models, and more.

To master ChatGPT and boost your productivity, explore Vanderbilt University's ChatGPT ChatGPT: Master Free AI Tools to Supercharge Productivity Specialization. Learn how to leverage ChatGPT to excel at planning, project management, writing, data analytics, and more.