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Computational models inspired by the human brain, used in AI to approximate functions and patterns. Example: Image recognition systems.

An area of machine learning where an agent learns to make decisions by taking actions in an environment to achieve maximum cumulative reward. Example: A robotics system that learns to navigate obstacles.

AI that revolves around ideas that are defined and manipulated as symbols—entities that represent something else. Example: Expert System rules encoding medical knowledge.

A type of machine learning where the model is trained on labeled data. Example: Spam filter trained to recognize spam emails from a labeled dataset.

A class of neural networks where connections between nodes form a directed graph along a temporal sequence, allowing it to exhibit temporal dynamic behavior. Example: Text generation.

AI models that involve networks competing against each other, typically a generator and a discriminator. Example: Generative Adversarial Networks (GANs) for creating realistic images.

The subfield of AI which produces written or spoken narrative from a data source. Example: A system that generates weather reports from meteorological data.

A class of machine learning utilizing deep neural networks with multiple layers. Example: Google's AlphaGo.

Predictive models that map observations about an item to conclusions about the item's target value using a tree-like structure. Example: Credit risk assessment.

A deep learning algorithm which can take in an input image, assign importance to aspects (objects) in the image, and be able to differentiate one from the other. Example: Handwriting recognition.

Systems that mimic human thought processes in a computerized model, aiming to help in decision-making. Example: IBM's Watson participating in the game show Jeopardy!.

Search heuristics inspired by the process of natural selection to generate solutions to optimization and search problems. Example: Evolving neural network structures.

Field of AI that enables computers to interpret and analyze visual information from the world. Example: Autonomous vehicle navigation.

The field of AI focused on the interaction between computers and human languages. Example: Virtual assistants like Siri or Alexa.

A machine learning approach involving a combination of a small amount of labeled and a large amount of unlabeled data during training. Example: Enhancing speech recognition models with limited transcribed audio data.

Graphical models that represent the probabilistic relationships among a set of variables. Example: Medical diagnosis systems.

A programming paradigm where program statements express facts and rules about problems within a system of formal logic. Example: Prolog used for creating an AI agent.

Supervised learning models used for classification and regression tasks by finding the best hyperplane that separates data points of different classes. Example: Spam detection in emails.

An ensemble learning method involving a multitude of decision trees, improving prediction accuracy. Example: Stock market behavior prediction.

Search strategies using domain knowledge to find solutions more efficiently than classic methods. Example: A* algorithm for pathfinding in games.

Computer systems that emulate the decision-making ability of a human expert by following a set of rules. Example: Medical diagnosis assistant.

AI technique that involves finding a sequence of actions that leads from the initial state to the desired goal state. Example: Path planning for autonomous robots.

An area of machine learning where an agent learns to make decisions by taking actions in an environment to achieve maximum cumulative reward. Example: A chess-playing algorithm.

A technique where a model developed for a task is reused as the starting point for a model on a second task. Example: Pre-trained image recognition models used for new specific tasks.

An area in AI and cognitive computing that's concerned with the study and development of algorithms and software that reason automatically. Example: Solving geometry problems.

A form of many-valued logic dealing with reasoning that is approximate rather than fixed and exact. Example: Climate control systems.

Systems that perceive their environment and take actions to maximize their chances of success at some goal. Example: Roomba vacuum cleaner.

A subset of AI that involves algorithms allowing computers to learn from data and improve over time. Example: A recommendation system on a streaming service.

Machine learning using information that is neither classified nor labeled, and allowing the algorithm to act on that information without guidance. Example: Grouping customers in market segmentation.