A large number of concepts in artificial intelligence are difficult, or even impossible, to define precisely and concisely. The two most important reasons are that the concept of intelligence is extremely complex and eludes any attempt at definition, and that even parts of what constitutes intelligence – reasoning, thinking, learning, adaptability, the ability to generalize, the ability to abstract, and much more – are themselves very complex.

Therefore, this glossary should not be understood as a collection of definitions, but rather as one possible set of descriptions of the most important concepts (at the moment) related to the field of artificial intelligence. Even regarding these descriptions, there is no complete consensus within the research community. Thus for each concept listed in this glossary, a source that describes it is also provided.

The glossary also represents a kind of compromise between the desire to present only the currently most relevant concepts in artificial intelligence and to provide a shorter yet more thorough insight into the fundamental concepts in that field.

A

Agent (Intelligent agent) – An entity that perceives its environment, takes actions autonomously to achieve goals, and may improve its performance through Machine learning or by acquiring knowledge. Leading AI textbooks define artificial intelligence as the “study and design of intelligent agents,” emphasizing that goal-directed behavior is central to intelligence. A specialized subset of intelligent agents, agentic AI (also known as an AI agent or simply agent), expands this concept by proactively pursuing goals, making decisions, and taking actions over extended periods, thereby exemplifying a novel form of digital agency. (source)

Artificial general intelligence (AGI) – Sometimes known as general AI, strong AI or broad AI, this often refers to a theoretical form of AI that can achieve human-level or higher performance across most cognitive tasks. See also Superintelligence. (source)

Artificial intelligence (AI) – Algorithms and systems that can replicate, support or surpass human perceptual, linguistic and reasoning processes; learn, draw conclusions and make predictions based on large or small quantities of data; replicate or enhance human perception; support humans in diagnosis, planning, scheduling, resource allocation and decision making; and cooperate physically and intellectually with humans and other AI systems. (source)

Autonomous vehicles – Vehicles that can operate without human intervention, such as self-driving cars and trucks. (source)

D

Dataset (Training dataset) – The set of data used to train an AI system. Training datasets can be labelled (for example, pictures of cats and dogs labelled ‘cat’ or ‘dog’ accordingly) or unlabelled. See also Model training. (source)

F

Foundation model – A Мachine learning model trained on a vast amount of data so that it can easily be adapted for a wide range of general tasks, including being able to generate outputs (see also Generative AI and Large language model). (source)

G

Generative AI – An advanced technological approach that enables the creation of content including text, computer programs, synthetic data, audio, images, and videos. By analyzing and discerning patterns within extensive training datasets, generative AI can autonomously construct material that shares comparable characteristics to its training input. This capability stems from the AI’s understanding of data patterns and its ability to replicate or innovate based on these patterns. (source)

L

Large language model – A type of Foundation model that is trained on vast amounts of text to carry out Natural language processing tasks. During training phases, large language models learn parameters from factors such as the model size and training datasets. Parameters are then used by large language models to infer new content. Whilst there is no universally agreed figure for how large training datasets need to be, the biggest large language models (frontier AI) have been trained on billions or even trillions of bits of data. For example, the large language model underpinning ChatGPT 3.5 (released to the public in November 2022) was trained using 300 billion words obtained from Internet text. See also Natural language processing and Foundation models. (source)

M

Machine learning (ML) – A subset of AI that involves the study and use of algorithms and statistical models to enable machines to learn from experience or data, i.e. to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. (source 1, source 2)

Model training – Model training is the process of feeding a Machine learning system curated Datasets to evolve the accuracy of its output. The process may be lengthy, depending on the complexity of the AI model, the quality of the training datasets, and the volume of training data. (source)

N

Natural language processing (NLP) – A subfield of artificial intelligence and computational linguistics that focuses on enabling machines to understand, interpret, and generate human language to be understood by humans. NLP algorithms look for linguistic patterns in how sentences and paragraphs are constructed and how words, context and structure work together to create meaning. Applications include speech-to-text converters, online tools that summarise text, chatbots, speech recognition and translations. See also Large language model. (source 1, source 2)

Neural network – A neural network, modeled after the human brain, is a mathematical and a Machine learning system that actively learns skills by identifying and analyzing statistical patterns in data. This system features multiple layers of artificial neurons, which are computational models inspired by the neurons in our brain. These artificial neurons process information and transmit signals to other connected neurons. While the first layer processes the input data, the final layer delivers the results. Intriguingly, even the experts who meticulously design these neural networks often find themselves puzzled by the intricate processes occurring between the layers. (source)

R

Reinforcement learning – A way of training Machine learning systems for a specific application. An AI system is trained by being rewarded for following certain ‘correct’ strategies and punished if it follows the ‘wrong’ strategies. After completing a task, the AI system receives feedback, which can sometimes be given by humans (known as ‘reinforcement learning from human feedback’). In the feedback, positive values are assigned to ‘correct’ strategies to encourage the AI system to use them, and negative values are assigned to ‘wrong’ strategies to discourage them, with the classification of ‘correct’ and ‘wrong’ depending on a pre-established outcome. This type of learning is useful for tweaking an AI model to follow certain ‘correct’ behaviours, such as fine-tuning a chatbot to output a preferred style, tone or format of language. See also Supervised learning, Unsupervised learning and Model training. (source)

Robotics – A subfield of artificial intelligence and engineering that involves the conception, design, manufacture and operation of robots – machines that are capable of automatically carrying out a series of actions and moving in the physical world. Modern robots contain algorithms that typically, although not always, have some form of artificial intelligence. Applications include industrial robots used in manufacturing, medical robots for performing surgery, and self-navigating drones. The objective of the robotics field is to create intelligent machines that can assist humans in a variety of ways. (source 1, source 2)

S

Superintelligence – A theoretical form of AI that has intelligence greater than humans and exceeds their cognitive performance in most domains. See also Аrtificial general intelligence. (source)

Supervised learning – A way of training Machine learning systems for a specific application. In a training phase, a machine learning system is fed labelled data. The system trains from the input data, and the resulting model is then tested to see if it can correctly apply labels to new unlabelled data (such as if it can correctly label unlabelled pictures of cats and dogs accordingly). This type of learning is useful when it is clear what is being searched for, such as identifying spam mail. See also Unsupervised learning, Reinforcement learning and Model training. (source)

U

Unsupervised learning – A way of training Machine learning systems for a specific application. A machine learning system is fed large amounts of unlabelled data, in which it starts to recognize patterns of its own accord. This type of learning is useful when it is not clear what patterns are hidden in data, such as in online shopping basket recommendations (“customers who bought this item also bought the following items”). See also Supervised learning, Reinforcement learning and Model training. (source)