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What Is Generative AI? An Overview

By Steve Smith

The information presented here is true and accurate as of the date of publication. DeVry’s programmatic offerings and their accreditations are subject to change. Please refer to the current academic catalog for details.


April 2, 2024
7 min read

The ability of machines to learn things and get smarter as they go is a fascinating hallmark of artificial intelligence (AI) technology. There are various technologies, use cases and benefits that fall under the broad AI umbrella, including conversational AI and generative AI.

Generative AI’s content generation and process automation capabilities can be beneficial, but the technology can also be used to create misleading content or enhance the effectiveness of certain kinds of cyberattacks. This potential dark side of generative AI has serious institutional and societal implications in a global economy that is interconnected in more ways than ever before.

In this article, we will answer questions like "what is generative AI”, what are some of the potential benefits and disadvantages of it and whether or not there are substantial differences between this form of AI and others. 

What is Generative AI?

What is generative AI and how does it work? The term AI refers to technologies that can understand, learn and act based on information that is fed to it. Broadly, it’s the concept of computers performing tasks that would otherwise involve or require human intelligence, like decision-making or natural language processing. Generative AI (Gen AI) is a subfield of artificial intelligence that, as the name implies, enables users to generate content like text, images, animation and sounds from a set of informational inputs, often just given to the tool as typed-in text. In its simplest form, it’s often like how you might request information from a search engine. More advanced directions are communicated to the AI engine using engineered “prompts” which provide more direct, clear, and thorough directions for the engine to produce content better aligned to a user’s desires.

Like other forms of AI, Gen AI uses machine learning algorithms, deep learning and neural networks. Generative Pre-trained Transformers (GPT) are a family of neural network models using transformer architecture, which takes an input sequence in and transforms it into an output sequence, like the prompts and results you get when you use an app like OpenAI’s ChatGPT, Google’s Gemini, Microsoft’s Copilot, or

GPT model uses include generating social media content, converting text to different styles or languages, writing and learning code, producing learning materials and building interactive voice assistants, among many other applications.

How Does Generative AI Work?

To further explore the “what is generative AI” question, we’ll take a closer look at how the technology works. Generative AI large language models like ChatGPT and image generators like DALLE-E identify patterns and other structures in their training data using neural networks, then generate content based on predictions from what they’ve learned. A neural network is a machine learning program that makes decisions in a manner similar to the human brain, using processes that mimic the way biological neurons work together. Neural networks are continual learners, relying on training data to learn and improve their accuracy over time. If you’ve ever used Google to search for something, you’ve experienced a well-known example of a neural network, Google’s search algorithm, in action.

The Benefits of Generative AI

The use cases of generative artificial intelligence demonstrate substantial benefits in industries like financial services, healthcare, manufacturing, the automotive industry, and telecommunications to name a few. Operational and marketing-related benefits include: 

  • Enhancing research and innovation

    Generative AI algorithms can analyze complex data sets in new ways, enabling researchers to discover new trends and patterns they might otherwise not recognize. 

  • Increasing customer engagement

    AI-powered chatbots and virtual assistants can be used to enhance customer service, provide a more personalized shopping experience and communicate with consumers in a highly personalized manner.

  • Optimizing business processes

    Using AI and machine learning applications, businesses can pull and summarize data from any source for knowledge search functions. They can also optimize scenarios for things like cost reductions across many business verticals.

  • Improving employee workflows

    Generative AI models can contribute to employee productivity by acting as organization-wide assistants to support tasks, generate reports, suggesting software code or creating new content.

The Disadvantages of Generative AI

It’s a good idea in our “what is generative AI” discussion to look at the potential disadvantages of generative AI as well as the benefits of it. AI-driven technologies have advanced at such a rapid pace, they threaten to leave the regulatory and policymaking frameworks that could protect against their misuse lagging behind. The following disadvantages of generative AI highlight the need for a closer examination:

  • Malicious misuse

    The potential for generative AI to be misused to create harmful content has already been seen in deep fake videos and stories that spread misinformation. The ease with which convincing fake content can be created using generative AI is not only alarming but may have dire consequences as internet users are asked to differentiate between what is real and what is not. 

  • Diminished human creativity

    New ethical questions are being raised around the use of generative AI and its ability to mimic human creators. If generative AI can produce art, music and literature, there is the potential that human-produced creativity will lose much of its value.

  • Ability to enhance cyberattacks

    While AI has the potential to do a lot of good in the area of cyber security, helping information security professionals to conduct risk assessments and penetration testing, it also presents serious potential risks. The widespread use of AI systems could increase the potential impact of security breaches, as it could be used to make malicious, social engineering-driven email phishing schemes more personalized, and therefore more convincing.

There are also real concerns about intellectual property (IP). AI relies on real information to train it, but what if a portion of its training data is copyright-protected content? In one high-profile case from December 2023, the New York Times sued ChatGPT maker OpenAI and its biggest backer, Microsoft, over copyright infringement, claiming that OpenAI used the newspaper’s material without permission to train its widely used chatbot after the company’s licensing negotiations with the Times broke down.

Popular Generative AI Interfaces

These widely used generative AI tools provide user-friendly features, are available at low or no cost, and demonstrate their developers’ assertive go-to-market strategies:

  • ChatGPT

    The most widely used generative AI tool to date, ChatGPT by OpenAI allows users to create simple, AI-generated content. The tool is optimized for human dialogue with a machine learning technique called reinforcement learning from human feedback (RLHF).

  • DALL-E 2

    The newest version of OpenAI’s image generation tool, DALL-E 2 can be used to make fresh images by typing descriptions, or by uploading an existing picture and typing instructions to change the image. This tool also uses natural language inputs, and outpainting and inpainting features make it easy to expand on existing images or request edits to an image.

  • Type Studio

    This web-based video editor eliminates the need to edit videos using a timeline. Operating within your web browser, Type Studio can be used to convert spoken words into text or be used to easily add subtitles to edited videos.

  • GitHub Copilot

    Using Microsoft’s Copilot technology, GitHub Copilot is constructed from the data collections of OpenAI Codex and GitHub’s open code repositories. This Generative AI tool converts prompts written in everyday language into suggestions for code and will block any suggestions that match public code.

Generative AI FAQs

What’s the difference between machine learning and artificial intelligence?

Machine learning and artificial intelligence are different but related segments of computer science. AI is the broader field, focusing on creating intelligent software that mimics human intelligence. Machine learning is a subfield of AI that uses algorithms and data to teach machines to do tasks without being explicitly programmed to do so.

What is generative AI vs. normal AI?

There are several differences between generative AI and conversational AI. Conversational AI focuses on helping machines sound and behave more naturally like humans in conversations, and is used mainly in applications like customer service, virtual assistants and chatbots. Generative AI focuses on content creation and can be used to write content or create digital assets like images and sounds.

Another difference is in the learning and training data they use. Conversational AI is trained on large data sets with human input, conversations, queries and responses, while generative AI is trained on different sets of data to create content with predictive patterns.   

Is GPT generative AI?

Yes, generative pre-trained transformers (GPT) are a family of neural network models that powers generative AI applications such as ChatGPT. They give applications the ability to create human-mimicking text and content, and answer questions in a conversational manner. 

Why is generative AI important?

The importance of generative AI lies mainly in the ways the technology can be utilized to benefit humans at home and at work. It can be used to automate repetitive tasks, reduce labor costs, achieve efficiencies in processes and help businesses make more efficient use of their human capital. AI tools bring similar benefits to content creation for research, audience engagement, marketing and other aspects of business, reducing the time required to perform certain tasks.

At DeVry, Discover Academics Driven by Technology

Here at DeVry, our philosophy of Tech-Powered Learning is at the core of everything we do, and helps our students put themselves in position to prepare to pursue many roles in the expanding and ever-evolving tech landscape.

An expanding group of AI and analytics courses provide our students with skills in data gathering and analysis, process automation and optimization, and software development helping them to understand the many ways AI can be used to solve technical challenges, business problems and support decision-making.

Courses like AI-Driven Business Application Coding, Predictive Analytics, Applied AI for Cyber Security and Introduction to Artificial Intelligence and Machine Learning are just a few examples of how we’ve begun integrating this rapidly evolving technology into our programs. Stay tuned for additional coursework that continues to build skills in software development and coding, innovation and more.

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