What Is Generative AI? An Overview

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 Perplexity.ai.

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.

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 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: 

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:

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:

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.

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.   

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. 

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.