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Generative AI (GenAI) is a type of artificial intelligence designed to create new content by learning patterns from existing data. It can produce realistic and imaginative text, images, audio, and other media, transforming industries such as entertainment, marketing, and healthcare.

Artificial intelligence (AI) refers to the broad field of technologies that enable machines to perform tasks that typically require human intelligence. Generative AI, in contrast, specifically focuses on creating new content based on the data it was trained on, often producing results that go beyond that original data. Today, most AI interactions experienced by everyday users, like using chatbots, are actually examples of generative AI.

A large language model (LLM) is a type of deep learning AI that uses transformer architectures, comprising encoder-decoder neural networks, to understand and generate human-like text. Trained on massive datasets, LLMs use self-attention mechanisms to capture relationships within language, making them powerful tools for generative AI applications.

AI chatbots are conversational programs enhanced with artificial intelligence. They leverage machine learning to improve responses over time and use natural language processing (NLP) and natural language understanding (NLU) to interpret user inputs accurately. By learning from interactions, AI chatbots deliver more natural, intuitive conversations and provide increasingly relevant responses.

AI agents are autonomous software systems capable of perceiving their environment, making decisions, and acting independently to achieve specific goals. They can adapt to new information over time, reducing the need for constant human oversight. AI agents are commonly used in customer service, workflow automation, and data analysis.

Machine learning (ML) is a branch of AI that builds models from data to identify patterns and make predictions. Unlike traditional programming, where rules are explicitly coded, ML systems learn from experience. Models allow the AI to make predictions, such as estimating a person’s weight based on height and gender. Through continuous training and exposure to new data, machine learning systems improve over time, mimicking aspects of human learning.