AI Terms - Comprehensive Guide to Key AI Concepts

The field of artificial intelligence is filled with specialized AI terms and concepts that can be challenging to navigate. This comprehensive guide is designed to help you understand key AI terms, providing clear and concise definitions across a broad range of topics.

Explore each essential AI concept to deepen your understanding of this rapidly evolving field. Whether you’re new to AI or experienced, this collection of AI terms will clarify the language of artificial intelligence, helping you stay informed and confident in the AI landscape.

  • A technology used to store complex structured and unstructured information used by a computer system.

  • The process of assigning labels to data points so that supervised learning algorithms can learn from them.

  • A hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated.

  • A statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome.

  • A method of data analysis that automates analytical model building.

  • A field of AI that focuses on the interaction between computers and humans through natural language.

  • A series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates

  • The process of making a system as effective or functional as possible.

  • A modeling error in machine learning that occurs when a function is too closely aligned to a limited set of data points.

  • The recognition of patterns and regularities in data.

  • The use of statistics and modeling techniques to determine future performance based on current and historical data.

  • An area of machine learning concerned with how agents ought to take actions in an environment to maximize some notion of cumulative reward.

  • A type of machine learning where the model is trained on labeled data.

  • A supervised machine learning algorithm which can be used for both classification or regression challenges.

  • An open-source software library for dataflow and differentiable programming across a range of tasks.

  • A subset of the data set used to train a model.

  • A subset of the data set used to assess the performance of a model trained on a training set.

  • Any characteristic, number, or quantity that can be measured or quantified.

  • A type of AI that is designed and trained for a particular task. Also known as Narrow AI, it contrasts with AGI, which can perform any intellectual task that a human can.

  • A step-by-step procedure for solving a problem or performing a task.

  • AI with the ability to understand, learn, and apply knowledge across a wide range of tasks at a human level.

  • The simulation of human intelligence processes by machines, particularly computer systems.

  • Computing systems inspired by the biological neural networks that constitute animal brains.

  • A method used in neural networks to calculate the gradient of the loss function and update the weights.

  • A graphical model that represents the probabilistic relationships among a set of variables.

  • Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.

  • A computer program designed to simulate conversation with human users, especially over the Internet.

  • The process of predicting the category or class of a given data point within a data set.

  • The task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.

  • A field of AI that trains computers to interpret and understand the visual world.

  • The practice of examining large databases to generate new information and find hidden patterns.

  • A subset of machine learning in which artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.

  • AI that uses a knowledge base of human expertise to solve specific problems within a certain domain.

  • The process of transforming raw data into a set of features that can be used in machine learning models.

  • An inference method used in AI systems, starting with known facts and applying inference rules to extract more data until a goal is reached.

  • A class of machine learning frameworks where two neural networks contest with each other in a game.

  • A rule of thumb that helps in problem-solving or learning.

  • A component of an expert system that applies logical rules to the knowledge base to deduce new information.