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From LLMs to hallucinations, here's a simple guide to common AI terms
Artificial intelligence is a deep and convoluted world. The scientists who work in this field often rely on jargon and lingo to explain what they’re working on. As a result, we frequently have to use those technical terms in our coverage of the artificial intelligence industry. That’s why we thought it would be helpful to put together a glossary with definitions of some of the most important words and phrases that we use in our articles. We will regularly update this glossary to add new entries as researchers continually uncover novel methods to push the frontier of artificial intelligence while identifying emerging safety risks. AGI Artificial general intelligence, or AGI, is a nebulous term. But it generally refers to AI that’s more capable than the average human at many, if not most, tasks. OpenAI CEO Sam Altman recently described AGI as the “equivalent of a median human that you could hire as a co-worker.” Meanwhile, OpenAI’s charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind’s understanding differs slightly from these two definitions; the lab views AGI as “AI that’s at least as capable as humans at most cognitive tasks.” Confused? Not to worry — so are experts at the forefront of AI research. AI agent An AI agent refers to a tool that uses AI technologies to perform a series of tasks on your behalf — beyond what a more basic AI chatbot could do — such as filing expenses, booking tickets or a table at a restaurant, or even writing and maintaining code. However, as we’ve explained before, there are lots of moving pieces in this emergent space, so “AI agent” might mean different things to different people. Infrastructure is also still being built out to deliver on its envisaged capabilities. But the basic concept implies an autonomous system that may draw on multiple AI systems to carry out multistep tasks. Chain of thought Given a simple question, a human brain can answer without even thinking too much about it — things like “which animal is taller, a giraffe or a cat?” But in many cases, you often need a pen and paper to come up with the right answer because there are intermediary steps. For instance, if a farmer has chickens and cows, and together they have 40 heads and 120 legs, you might need to write down a simple equation to come up with the answer (20 chickens and 20 cows). In an AI context, chain-of-thought reasoning for large language models means breaking down a...
AI Glossary: 565 Essential AI Definitions & Terms [2026]
This comprehensive AI glossary covers 565 key terms in artificial intelligence, machine learning, generative AI, and automation. Whether you are a business leader exploring AI adoption, a developer building AI systems, or a student learning the fundamentals, this glossary provides clear definitions for the most important AI terminology in 2026. Each term includes a practical definition focused on real world applications. Use the alphabetical navigation below to jump to any section. A Abductive Neural Reasoning The AI makes its best guess about what probably happened when it only sees part of the story. Abductive Neural Reasoning allows AI to infer the most plausible explanation for observed data, even with missing or incomplete information. It integrates neural models with abductive logic (inference to the best explanation). Active Inference Active inference is like teaching a robot to guess what you’ll do next and then act accordingly, it helps robots make smart decisions! Active inference combines prediction and action, enabling agents to minimize uncertainty by interacting with their environment and updating beliefs based on observations. Active Learning Detailed Explanation: Active learning involves selecting the most informative data points for labeling, reducing the amount of required training data while improving model accuracy. Real-World Applications: Adaptive Cognitive Offloading It’s like using a notebook when your brain gets full. AI knows when to use outside help to think better without getting overwhelmed. Adaptive Cognitive Offloading refers to AI systems offloading complex computations or memory tasks to external modules (e.g., databases, tools, or human-AI collaboration), based on task difficulty and resource availability. This improves efficiency and reduces computational burden. Adaptive Concept Forgetting It’s like cleaning out old school notes you no longer need. AI learns what to forget so it can stay sharp and efficient. Adaptive Concept Forgetting allows AI models to selectively discard outdated or irrelevant information during continual learning, reducing interference between past and present tasks while improving adaptability. Adaptive Context Synthesis It’s like quickly figuring out what story someone is telling based on the conversation around it. AI systems integrate contextual signals from different sources to build a holistic understanding for better predictions. Adaptive Control Adaptive control is like teaching a ro...
AI Terminology 2026: 50+ Key Concepts Every Business Must Know
AI Terminology refers to the collection of concepts, definitions, and specialized vocabulary used to describe the field of artificial intelligence. It serves as ...
Artificial Intelligence - Artificial Intelligence (AI) Guide ...
Artificial Intelligence (AI) is a branch of computer science. AI systems use hardware, algorithms, and data to create "intelligence" to do things like make decisions, discover patterns, and perform some sort of action. AI is a general term and there are more specific terms used in the field of AI. AI systems can be built in different ways.
