Artificial general intelligence (AGI) is an AI system with a human-like (or beyond) ability to learn, reason, and apply knowledge on a wide range of tasks. This is considered an end goal of ongoing AI research.
Agentic AI refers to AI systems designed to act as autonomous or semi-autonomous agents. They can set their own goals, plan actions, call on tools, make decisions based on feedback, and adapt over time to complete tasks.
Artificial intelligence (AI) broadly covers computer systems that can perform tasks with human-like intelligence, like understanding language, recognizing images, learning from past data, and reasoning.
Chatbots are computer programs designed to simulate conversation with humans. More sophisticated AI-powered chatbots use natural language processing to generate human-like answers to various questions.
Data mining is the process of discovering patterns and trends in large datasets through machine learning and statistical patterns.
Deep learning is a type of machine learning which uses multi-layer neural networks to learn complex patterns from data, usually independently of human input. Deep learning powers various technologies like self-driving cars and speech recognition.
Foundation models are large-scale AI models, often transformers, which are trained on huge amounts of data. These models can be adapted to perform a wide range of tasks—a “foundation” for various AI applications.
Generative AI refers to AI systems that can create new content like text, images, video, audio, or software code. They generate new outputs that resemble the data they are trained on. Generative AI powers applications like chatbots and creative tools.
Hallucination refers to instances when an AI system generates responses that are incorrect, misleading, or entirely fabricated—but is nevertheless presented as factual. This often happens in generative AI models.
Human-in-the-loop are AI systems that include human feedback or intervention as an essential part of their operation. Human operators can provide guidance, correct mistakes or make final decisions.
Large language models (LLM) are AI systems training on massive amounts of text, built to understand and generate human-like language.
Machine learning (ML) is a branch of artificial intelligence that enables computers to learn patterns and make decisions without being explicitly programmed with rules. We see ML at work in spam filters, recommendation systems and voice assistants.
Multimodal AI refers to AI systems that can process, understand and generate multiple types of data simultaneously—say, it can generate text, video and audio all in one go.
Natural language processing (NLP) is a branch of artificial intelligence focused on enabling computers to understand, interpret and generate human language meaningfully. NLP is often used in AI summaries, chatbots, spell checking and translation.
Robotics is a field concerned with designing machines capable of physical tasks. With the advent of AI, these robots can now perceive their surroundings, make decisions and adapt to new situations without explicit programming for every scenario.
Transformers are neural network architectures that use “attention” to process and understand relationships between all parts of input data simultaneously, rather than sequentially. Transformers form the backbone of major LLMs like GPT, BERT and Claude.
This glossary is derived from the Stanford University Human-Centered Artificial Intelligence Institute’s online glossary.