AI Glossary

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. These intelligent machines can be trained to perform various tasks by processing large amounts of data and learning from it. AI systems can be classified into two main categories: narrow or general. Narrow AI systems are designed to perform specific tasks, while general AI systems are designed to perform any intellectual task that a human being can. The ultimate goal of AI research is to create systems that can understand, think, and act intelligently, in a way that is indistinguishable from a human being.

The set of all possible actions that an agent can take in a reinforcement learning problem.

A type of machine learning algorithm modeled after the structure and function of the human brain, consisting of layers of interconnected "neurons" that can process and transmit information.

A type of neural network used for dimensionality reduction and feature learning, consisting of an encoder and a decoder that learn to compress and reconstruct data respectively.

A machine learning ensemble technique in which multiple models are trained on different random subsets of the training data and combined to make predictions, with the goal of reducing the variance and improving the generalization of the model.

Large sets of data that can be analyzed and used to gain insights and make informed decisions.

A machine learning ensemble technique in which weak learners are combined to form a strong learner, with the goal of improving the overall prediction accuracy.

A chatbot by OpenAI built on top of their GPT3.5 large language model, trained with public data.

The process of categorizing data into predefined classes or groups.

The process of grouping data points together based on similarity or common characteristics.

The development of computer systems that can perform tasks that normally require human-like intelligence, such as learning and problem-solving.

The ability of a computer to interpret and understand visual data from the world, such as images and video.

A type of neural network specifically designed for image and video recognition tasks, using convolutional layers to learn and recognize patterns in the data.

A model evaluation technique in which the training data is split into multiple folds, and the model is trained and evaluated on each fold to obtain an estimate of its generalization performance.

The process of extracting useful patterns and knowledge from large datasets.

A flowchart-like tree structure used to make decisions based on a series of binary splits.

An image generation technique developed by Google that uses a convolutional neural network to generate dream-like images by amplifying the features of the network.

A subfield of machine learning that involves training multi-layered neural networks to learn and make decisions on their own.

The process of reducing the number of features or dimensions in a dataset while preserving as much information as possible.

The factor by which the future rewards are discounted in the reinforcement learning algorithm, to balance the tradeoff between the short-term and the long-term rewards.

A method of solving optimization problems by breaking them down into smaller subproblems and storing the solutions to these subproblems in a table or array.

A machine learning technique in which multiple models are trained and combined to make predictions, with the goal of improving the overall performance of the model.

The sequence of states, actions, and rewards that an agent experiences in a reinforcement learning problem before reaching a terminal state.

A set of algorithms that use principles of natural evolution, such as reproduction, mutation, and selection, to find solutions to problems.

A computer program that utilizes artificial intelligence techniques to mimic the decision-making abilities of a human expert in a specific field.

The tension in reinforcement learning between exploring new actions and exploiting the known good actions, in order to balance the learning and the reward-maximizing objectives of the agent.

The process of identifying and verifying individuals based on their facial features.

The process of selecting and creating informative and relevant features from raw data for use in a machine learning model.

The process of choosing a subset of the most relevant features from a larger set of features for use in a machine learning model.

A machine learning technique that involves adjusting the hyperparameters or parameters of a pre-trained model on a new dataset to optimize its performance for a specific task. Fine-tuning is commonly used when a pre-trained model is available for a related task, but the available data for the new task is limited or the target task is slightly different from the original task.

The use of a function to approximate the value function or the policy in a reinforcement learning problem, when the state or action spaces are too large to be represented explicitly.

A form of mathematical logic that allows for uncertainty and imprecision in the input and output of a system.

A type of generative model consisting of two competing neural networks, a generator and a discriminator, that learn to generate and recognize synthetic data respectively.

A type of neural network consisting of two competing networks, a generator and a discriminator, that learn to generate and recognize synthetic data respectively.

A machine learning model that learns the underlying distribution of the data and can generate new, synthetic samples from it.

A search algorithm that uses principles of natural evolution, such as reproduction, mutation, and selection, to find solutions to problems.

a large language model developed by OpenAI that uses transformer architecture and self-supervised learning to generate human-like text.

A problem-solving method that involves finding a solution through trial and error and learning from past experiences.

The process of adjusting the parameters of a machine learning model that are set prior to training, in order to improve its performance.

The process of labeling or annotating images with relevant information, such as object classes or bounding boxes.

The process of generating a natural language description of an image.

The process of assigning an image to one or more predefined categories or classes.

The process of adding colors to a grayscale image.

The process of improving the visual quality of an image, such as increasing its contrast or removing noise.

The process of creating new, synthetic images using artificial intelligence techniques.

The process of preparing the images for use

The process of repairing or restoring a degraded or damaged image.

The process of searching for and retrieving images from a large database based on their visual content.

The process of dividing an image into multiple regions or segments, each representing a different object or background.

The process of converting an image from one domain to another, such as translating a photograph to a painting or a sketch.

The process of deducing conclusions from premises using logical reasoning.

The process of repairing or filling in damaged or missing parts of an image.

An unsupervised machine learning algorithm used for clustering data points into a predefined number of clusters.

The way in which knowledge is encoded and stored in a computer system.

A language model in artificial intelligence is a statistical model that is used to predict the likelihood of a sequence of words or tokens in a language. Language models are commonly used in natural language processing tasks to generate coherent and appropriate text.

A large language model in artificial intelligence is a type of statistical model that is trained on a very large dataset of text and uses machine learning techniques to learn the patterns and structure of the language. Large language models are commonly used for tasks such as language generation, machine translation, and text classification, and are able to generate human-like text with a high degree of coherence and fluency.

A method of training algorithms using large amounts of data and allowing the algorithm to learn and improve on its own without explicit programming.

A mathematical framework used in reinforcement learning to model the decision-making process of an agent in a sequential, uncertain environment.

The property of a state in a Markov decision process that says that the future is independent of the past, given the present state.

A Markov decision process that only has a reward component, without any decision-making involved.

A model is a representation of a problem or a system that can be used to make predictions, decisions, or learn patterns in data. Models are a fundamental part of machine learning and are used to perform various tasks, such as classification, regression, clustering, or dimensionality reduction. Models can be trained on a dataset using various machine learning algorithms, such as supervised learning, unsupervised learning, or reinforcement learning, to learn the relationships and patterns in the data and make predictions or decisions based on them.

The process of making a machine learning model available for use in production environments, such as deploying it to a web server or integrating it into an application.

The process of measuring the performance of a machine learning model on a specific task, using metrics such as accuracy, precision, and recall.

The process of choosing the best model among a set of candidate models for a given task.

A reinforcement learning algorithm that uses random sampling to estimate the value function or the optimal policy.

A reinforcement learning problem in which an agent must choose among a set of actions, each with an unknown reward distribution, and learn which actions are the most rewarding through trial and error.

A machine learning algorithm used for classification tasks based on the Bayes theorem of probability.

The ability of a computer to understand, interpret, and generate human language.

A type of machine learning algorithm modeled after the structure and function of the human brain, consisting of layers of interconnected "neurons" that can process and transmit information.

The process of scaling the values of a feature or a dataset to a common range, such as [0, 1] or [-1, 1].

The process of identifying and localizing objects in an image or a video.

A system that represents the relationships and categories within a particular domain of knowledge.

The policy that maximizes the expected cumulative reward in a reinforcement learning problem.

The value function that corresponds to the optimal policy in a reinforcement learning problem.

A condition in which a machine learning model performs well on the training data but poorly on new, unseen data, due to being too complex and fitting the noise in the training data.

The ability to identify patterns or regularities in data.

The process of determining a course of action to achieve a specific goal.

A reinforcement learning algorithm that involves alternating between evaluating the current policy and improving it based on the learned value function.

The process of preparing the data for use in a machine learning model, including cleaning, transforming, and scaling the data.

A reinforcement learning algorithm that learns an action-value function, also known as a Q-function, which estimates the expected future rewards for each action in a given state.

The process of drawing conclusions based on evidence and logical arguments.

A type of neural network specifically designed for processing sequential data, using feedback connections to allow the network to remember and make use of past information.

A machine learning technique used to predict a continuous numerical value based on a set of input features.

A type of machine learning in which an agent learns through trial and error by interacting with its environment and receiving rewards or punishments for its actions.

A type of machine learning technique in which an artificial intelligence (AI) system learns from the feedback or rewards provided by a human user or trainer

The function that defines the reward or the punishment that an agent receives for its actions in a reinforcement learning problem.

The study and application of robots and automation.

A reinforcement learning algorithm that learns an action-value function using the expected reward and the value of the next action, rather than the final reward as in Q-learning.

An extension of the World Wide Web that enables machines to understand the meaning of the data on the web.

Stable Diffusion is a text-to-image model based on deep learning that can generate highly detailed images based on text descriptions.

The process of transforming the values of a feature or a dataset to have zero mean and unit variance.

The set of all possible states in a reinforcement learning problem.

The movement of the agent from one state to another state in a reinforcement learning problem, based on an action and the transition probabilities of the environment.

The process of transferring the style of one image to another image, while preserving the content of the second image.

The process of increasing the resolution of an image or a video by filling in the missing details.

A machine learning algorithm used for classification and regression tasks.

A reinforcement learning algorithm that uses the temporal difference error, the difference between the estimated and the actual future rewards, to update the action-value function.

A machine learning system that generates images based on natural language descriptions.

Training refers to the process of learning from data to improve the performance of a model or system. Training is a fundamental part of machine learning and involves feeding a model with a dataset and adjusting its parameters or weights to optimize its performance for a specific task.

The sequence of states and actions that an agent follows in a reinforcement learning problem.

A machine learning technique in which a model trained on one task is fine-tuned or adapted for a related task.

A condition in which a machine learning model performs poorly on both the training and new, unseen data, due to being too simple and unable to capture the underlying patterns in the data.

A reinforcement learning algorithm that involves iteratively improving the value function until it converges to the optimal value function.

A type of generative model that consists of an encoder network that maps the input data to a latent representation and a decoder network that maps the latent representation back to the original data space.