Definition and Overview

Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic data. It is a branch of AI that enables machines to learn patterns from vast datasets and then to autonomously produce new content based on those patterns. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics1,2, [9]].

Evolution and Impact

Generative AI was introduced in the 1960s with chatbots, but it wasn’t until 2014, with the introduction of generative adversarial networks (GANs), that generative AI could create convincingly authentic images, videos, and audio of real people. Innovations in multimodal AI now enable teams to generate content across multiple types of media, including text, graphics, and video1.

How Generative AI Works

Generative AI is powered by foundation models (large AI models) that can multi-task and perform out-of-the-box tasks, including summarization, Q&A, classification, and more. The most common way to train a generative AI model is to use supervised learning – the model is given a set of human-created content and corresponding labels. It then learns to generate content that is similar to the human-created content and labeled with the same labels. Generative AI processes vast content, creating insights and answers via text, images, and user-friendly formats3.

Applications and Limitations

Generative AI has the potential to change how a range of jobs are performed. For example, it can be used to navigate complex systems like healthcare by providing conversational search experiences. However, many generative models are sensitive to how their instructions are formatted, which has inspired a new AI discipline known as prompt engineering4,5, [5]].

Future of Generative AI

The full scope of the impact of generative AI is still unknown, as are the risks. However, it’s clear that AI adoption has more than doubled over the past five years, and investment in AI is increasing apace. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics, and videos in a matter of seconds4,1.


Is generative AI different from other AI?

Key Differences Between Generative AI and Traditional AI

Generative AI and traditional AI represent two distinct approaches to artificial intelligence, each with its own strengths and weaknesses.

Traditional AI systems are primarily used to analyze data and make predictions. They excel at pattern recognition and are often used to follow specific rules, do a particular job, and do it well. Examples of traditional AI include voice assistants like Siri or Alexa, recommendation engines on Netflix or Amazon, or Google’s search algorithm. These AI systems have been trained to follow specific rules and are like master strategists who can make smart decisions within a specific set of rules. However, traditional AI is limited to its programmed rules and lacks adaptability. In new or unexpected situations, it may not produce desired outcomes and requires manual adjustments or reprogramming to handle novel scenarios.

On the other hand, Generative AI goes a step further by creating new data similar to its training data. It excels at pattern creation. Generative AI uses neural networks to identify patterns and other structures in its training data. It then generates new content based on predictions from these learned patterns. Generative AI leverages data-driven learning to provide creativity, adaptability, and the potential for generalization. It is used in image generation (GANs), music composition, text generation (GPT-3), virtual architecture, deepfake technology, and drug discovery, among other applications. Because of its creativity, generative AI is seen as the most disruptive form of AI.

In summary, while traditional AI is focused on detecting patterns, generating insights, automation, and prediction, generative AI starts with a prompt that lets a user submit a question along with any relevant data to guide content generation. It’s not inventing new ways to play chess but selecting from strategies it was programmed with. That’s traditional AI. Generative AI, however, is able to create something new.

AI Generated Courtesy of You.com