AI Glossary · 230+ AI & ML Terms Explained

[TOOL 04] — Reference index233+ AI terms explained

Your comprehensive guide to artificial intelligence terminology. From basic concepts to advanced techniques - all clearly explained.

[01]Term index

Showing 233 terms

A37 entries

A01AccuracyAI FundamentalsAccuracy is a metric used in machine learning to measure the percentage of correctly predicted instances in relation to the total number of instances evaluated. It is widely used to assess the performance of classification models.

A02Activation FunctionNeural NetworksA mathematical function that determines the output of a neuron based on its weighted input sum.

A03Active LearningAI FundamentalsActive learning is a machine learning approach where the algorithm selectively queries a human expert to label new data points with the goal of improving the model's performance with minimal labeled data.

A04Adam OptimizerAI FundamentalsAdam (Adaptive Moment Estimation) is an optimization algorithm used in training machine learning models, particularly neural networks. It combines the advantages of two other extensions of stochastic gradient descent, specifically AdaGrad and RMSProp, to adaptively adjust the learning rate of each parameter.

A05Adversarial AttackAI FundamentalsAn adversarial attack is a deliberate attempt to manipulate the inputs to an AI model in order to cause it to make errors or incorrect predictions, often by introducing subtle perturbations that are imperceptible to humans.

A06Adversarial ExampleAI FundamentalsAn adversarial example is a specially crafted input designed to deceive a machine learning model, causing it to make an incorrect prediction or classification.

A07Agentic AIAI FundamentalsAgentic AI refers to artificial intelligence systems designed to perceive their environment, make decisions, and take actions autonomously to achieve specific goals.

A08AI AdoptionAI FundamentalsAI adoption refers to the process by which organizations and individuals incorporate artificial intelligence technologies into their operations, products, or services to improve efficiency, decision-making, and innovation.

A09AI AgentAI FundamentalsAn AI agent is an entity capable of perceiving its environment through sensors and acting upon that environment through actuators to achieve specific goals using intelligent behavior.

A10AI BenchmarkAI FundamentalsAn AI benchmark refers to standardized tests and datasets used to evaluate the performance, accuracy, and efficiency of artificial intelligence models and systems.

A11AI BiasAI EthicsSystematic errors in AI systems that create unfair outcomes for certain groups.

A12AI EthicsAI FundamentalsAI Ethics refers to the moral principles and practices that govern the development, deployment, and use of artificial intelligence systems, ensuring they are designed and implemented in ways that are fair, transparent, and safe.

A13AI GovernanceAI FundamentalsAI governance refers to the framework of policies, regulations, and practices that guide the ethical and effective development, deployment, and management of artificial intelligence systems.

A14AI Impact AssessmentAI FundamentalsAI Impact Assessment is a systematic process used to evaluate the potential effects and implications of deploying artificial intelligence systems in various contexts, focusing on ethical, social, and economic impacts.

A15AI LiteracyAI FundamentalsAI Literacy refers to the understanding and ability to use AI technologies effectively and responsibly, encompassing knowledge of AI concepts, tools, and their societal implications.

A16AI MaturityAI FundamentalsAI Maturity refers to the level of an organization's readiness and ability to effectively implement and leverage artificial intelligence technologies in their operations, decision-making, and strategy.

A17AI PolicyAI FundamentalsAI policy refers to the set of regulations, guidelines, and strategies developed by governments, organizations, or institutions to manage and govern the development, deployment, and use of artificial intelligence technologies.

A18AI ReadinessAI FundamentalsAI Readiness refers to the preparedness of an organization or system to adopt and integrate artificial intelligence technologies into their operations effectively.

A19AI RegulationAI FundamentalsAI regulation refers to the legal frameworks, policies, and guidelines designed to govern the development, deployment, and use of artificial intelligence technologies to ensure they are safe, ethical, and beneficial to society.

A20AI RoadmapAI FundamentalsAn AI roadmap is a strategic plan that outlines the steps and milestones for integrating artificial intelligence into an organization or project, guiding the development and deployment of AI technologies to achieve specific goals.

A21AI SafetyAI FundamentalsAI Safety refers to the study and practice of ensuring that artificial intelligence systems operate safely and reliably, minimizing risks and potential harm to humans and the environment.

A22AI StrategyAI FundamentalsAI Strategy is a structured plan that organizations develop to integrate artificial intelligence into their operations, aiming to enhance performance, drive innovation, and achieve strategic goals.

A23AI TransformationAI FundamentalsAI Transformation refers to the comprehensive integration and adoption of artificial intelligence technologies within an organization to improve efficiency, drive innovation, and enhance decision-making processes.

A24AI Use CaseAI FundamentalsAn AI use case refers to a specific application or scenario where artificial intelligence technologies are employed to solve a problem or enhance processes within various industries.

A25AlgorithmAI FundamentalsA set of step-by-step instructions or rules followed to solve a problem or perform a computation.

A26AnnotationAI FundamentalsAnnotation in AI and machine learning refers to the process of labeling data with informative tags or notes, which are used to train algorithms and models to recognize patterns and make predictions.

A27Anomaly DetectionAI FundamentalsAnomaly detection is the process of identifying rare items, events, or observations that raise suspicions by differing significantly from the majority of the data. It is often used in various domains to detect deviations from a norm, which may indicate critical incidents, faults, or fraudulent activities.

A28Artificial General IntelligenceAI FundamentalsArtificial General Intelligence (AGI) refers to a form of AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to human intelligence.

A29Artificial Intelligence (AI)AI FundamentalsThe simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.

A30Artificial Neural NetworkAI FundamentalsAn artificial neural network (ANN) is a computational model inspired by the way biological neural networks in the human brain process information. ANNs consist of interconnected groups of artificial neurons that work together to solve complex problems.

A31Artificial SuperintelligenceAI FundamentalsArtificial Superintelligence (ASI) refers to a hypothetical AI system that surpasses human intelligence across all domains, including creativity, problem-solving, and emotional understanding.

A32Attention MechanismDeep LearningA technique that allows models to focus on relevant parts of the input when producing output.

A33AUCAI FundamentalsAUC, or Area Under the Curve, is a performance measurement for classification models at various threshold settings. It measures the entire two-dimensional area underneath the entire ROC curve, providing an aggregate measure of model performance across all classification thresholds.

A34AuditabilityAI FundamentalsAuditability in AI refers to the ability to trace, understand, and verify the decision-making processes and data used by AI systems, ensuring transparency and accountability.

A35AutoencoderAI FundamentalsAn autoencoder is a type of artificial neural network used to learn efficient codings of input data, commonly for the purpose of dimensionality reduction or feature learning.

A36Autonomous AgentAI FundamentalsAn autonomous agent is a system that can operate independently in an environment to achieve certain goals, often using sensors to perceive its surroundings and actuators to take action.

A37Autonomous SystemAI FundamentalsAn autonomous system is a self-governing entity capable of operating independently to perform tasks or make decisions without human intervention, often utilizing AI and machine learning algorithms.

B05 entries

B01BackpropagationNeural NetworksThe algorithm for calculating gradients of the loss function with respect to network weights.

B02Batch SizeNeural NetworksThe number of training examples used in one iteration of model training.

B03Bayesian NetworkAI FundamentalsA Bayesian Network is a probabilistic graphical model that represents a set of variables and their conditional dependencies through a directed acyclic graph (DAG). It is used to model uncertainty in complex systems and to perform inference and learning.

B04BERTNatural Language ProcessingBidirectional Encoder Representations from Transformers, a pre-trained language model for NLP tasks.

B05Bias in AIAI FundamentalsBias in AI refers to systematic errors or prejudices in AI systems that lead to unfair outcomes, often due to flawed data or algorithms. It can manifest in various forms, such as gender, racial, or socioeconomic biases, affecting the fairness and reliability of AI decisions.

C16 entries

C01CalculusMathematicsThe mathematical study of continuous change, including derivatives and integrals.

C02Carbon Aware AIAI FundamentalsCarbon Aware AI refers to the practice of designing and deploying artificial intelligence systems with consideration for their carbon footprint, aiming to minimize the environmental impact associated with energy consumption during AI model training and inference.

C03Chain of ThoughtAI FundamentalsChain of Thought (CoT) is a reasoning approach in AI where a model generates a series of intermediate steps or explanations to arrive at a solution, enhancing its decision-making capabilities.

C04ClassificationMachine LearningPredicting which category or class an input belongs to from a set of predefined categories.

C05Cloud AIAI FundamentalsCloud AI refers to the use of cloud computing resources to deliver artificial intelligence services and capabilities, enabling scalable, flexible, and accessible AI solutions.

C06ClusteringAI FundamentalsClustering is a machine learning technique used to group similar data points together based on certain characteristics, without requiring pre-labeled data.

C07Collaborative FilteringAI FundamentalsCollaborative filtering is a technique used in recommendation systems to predict the preferences of a user by collecting preferences from many users. It identifies patterns in user behavior and leverages the knowledge of like-minded individuals to make recommendations.

C08Compute BudgetAI FundamentalsA compute budget refers to the allocated computational resources, such as processing power and memory, that are used to run AI and machine learning models. It is a critical consideration in model training and deployment, impacting performance, scalability, and cost.

C09Computer VisionComputer VisionThe field of AI enabling computers to interpret and understand visual information from the world.

C10Concept DriftAI FundamentalsConcept drift refers to the change in the statistical properties of the target variable that a model is trying to predict, which can occur over time. This phenomenon can degrade the performance of a machine learning model if it is not updated to accommodate these changes.

C11Confusion MatrixAI FundamentalsA confusion matrix is a table used to evaluate the performance of a classification algorithm, displaying the counts of true positives, true negatives, false positives, and false negatives.

C12Content Based FilteringAI FundamentalsContent Based Filtering is a recommendation system approach that suggests items to users by analyzing the attributes of items that a user has previously interacted with, focusing on the characteristics of the items rather than user behavior patterns.

C13Context WindowAI FundamentalsA context window refers to the portion of input data that a machine learning model, particularly in natural language processing, can consider at once. It affects how much surrounding information is used to make predictions or decisions.

C14Continuous LearningAI FundamentalsContinuous learning in AI refers to the capability of a system to learn incrementally, adapting to new data without forgetting previously acquired knowledge. This concept is essential for developing AI systems that remain relevant and accurate over time in dynamic environments.

C15Convolutional Neural Network (CNN)Deep LearningA neural network architecture designed for processing grid-like data such as images.

C16Cross-ValidationMachine LearningA technique to evaluate model performance by training and testing on different subsets of data.

D13 entries

D01Data AugmentationData ScienceArtificially increasing training data by creating modified versions of existing data.

D02Data DriftAI FundamentalsData drift refers to the phenomenon where the statistical properties of the input data change over time, which can affect the performance of machine learning models. It is crucial to monitor and manage data drift to maintain model accuracy.

D03Data GovernanceAI FundamentalsData governance refers to the overall management of the availability, usability, integrity, and security of the data employed in an organization. It involves a set of processes and frameworks that ensure data is handled in a consistent, secure, and compliant manner across the organization.

D04Data ParallelismAI FundamentalsData parallelism is a parallel computing model that involves distributing data across multiple processors or machines to perform computations simultaneously, improving processing speed and efficiency.

D05Data PipelineAI FundamentalsA data pipeline is an automated sequence of processes that collects, transforms, and moves data from various sources to a destination, such as a data warehouse, for analysis or further processing.

D06Data PreprocessingData ScienceCleaning and transforming raw data into a format suitable for machine learning.

D07Deep LearningAI FundamentalsA subset of machine learning using artificial neural networks with multiple layers to model complex patterns.

D08Dense ModelAI FundamentalsA dense model in AI and machine learning is a type of neural network architecture where every neuron in one layer is connected to every neuron in the following layer, often referred to as a fully connected or dense layer.

D09Differential PrivacyAI FundamentalsDifferential Privacy is a privacy-preserving technique ensuring that the output of a computation does not significantly change when a single individual's data is added or removed, protecting individual privacy in datasets.

D10Diffusion ModelGenerative AIGenerative models that learn to create data by reversing a gradual noising process.

D11Dimensionality ReductionAI FundamentalsDimensionality reduction is a technique used in data processing and machine learning to reduce the number of input variables or features in a dataset while preserving its essential information.

D12Distributed TrainingAI FundamentalsDistributed training refers to the practice of training machine learning models across multiple machines or processors, allowing for faster processing and handling of large datasets.

D13DropoutDeep LearningA regularization technique that randomly drops neurons during training to prevent overfitting.

E10 entries

E01Edge AIAI FundamentalsEdge AI refers to deploying artificial intelligence algorithms on local devices or edge servers, close to the data source, for faster processing and real-time decision-making without relying heavily on cloud infrastructure.

E02EmbeddingAI FundamentalsAn embedding is a representation of data, such as words or items, in a continuous vector space where similar data points are closer together, facilitating efficient processing and analysis in machine learning models.

E03Encoder Decoder ArchitectureAI FundamentalsThe encoder-decoder architecture is a neural network design pattern commonly used in tasks that require transforming one sequence into another, such as translation or summarization. It consists of two main components: an encoder that processes input data and a decoder that generates the output.

E04Energy Efficient AIAI FundamentalsEnergy Efficient AI refers to the development and deployment of artificial intelligence systems that are designed to minimize energy consumption while maintaining or improving performance. This approach is crucial in reducing the environmental impact of AI technologies.

E05Ensemble LearningMachine LearningCombining multiple models to produce better predictions than any single model.

E06EpochNeural NetworksOne complete pass through the entire training dataset during model training.

E07Evaluation MetricAI FundamentalsAn evaluation metric is a quantifiable measure used to assess the performance of a machine learning model, providing insights into its accuracy, effectiveness, and reliability in making predictions or classifications.

E08Expert SystemAI FundamentalsAn expert system is a computer program that uses artificial intelligence to simulate the decision-making ability of a human expert, often relying on a knowledge base and inference rules to solve complex problems within a specific domain.

E09ExplainabilityAI EthicsThe ability to understand and interpret how an AI model makes its decisions.

E10Explainable AIAI FundamentalsExplainable AI (XAI) refers to methods and techniques in artificial intelligence that enable human users to comprehend and trust the results and outputs created by machine learning algorithms.

F10 entries

F01F1 ScoreAI FundamentalsThe F1 Score is a metric used to evaluate the accuracy of a classification model, balancing precision and recall through their harmonic mean, especially useful in scenarios with imbalanced datasets.

F02Fairness in AIAI FundamentalsFairness in AI refers to the principle of designing algorithms and systems that make unbiased decisions, ensuring equitable outcomes across different groups, particularly marginalized or disadvantaged populations.

F03FeatureMachine LearningAn individual measurable property or characteristic of data used as input to a machine learning model.

F04Feature EngineeringMachine LearningThe process of using domain knowledge to create new features that improve model performance.

F05Feature StoreAI FundamentalsA feature store is a centralized repository designed to store, manage, and serve machine learning features, facilitating the reuse of features across different models and ensuring consistency between training and serving environments.

F06Federated LearningAI FundamentalsFederated Learning is a collaborative machine learning approach where models are trained across many decentralized devices or servers holding local data samples, without exchanging them. This method enhances privacy and security by keeping data localized.

F07Few Shot LearningAI FundamentalsFew Shot Learning is an AI approach that enables models to learn new tasks from a limited number of examples, often as few as one or a handful. It addresses the challenge of training models in scenarios where data is scarce, making them more flexible and efficient.

F08Fine-TuningDeep LearningAdapting a pre-trained model to a new task by training on task-specific data.

F09Foundation ModelAI FundamentalsA foundation model is a large-scale, pre-trained deep learning model that can be adapted to a wide range of downstream tasks with minimal fine-tuning. These models are typically built using vast amounts of data and computational resources.

F10Function CallingAI FundamentalsFunction calling is a fundamental programming concept where a specific set of instructions or code block, defined as a function, is executed by referencing its name. In AI and machine learning, function calling is essential for modularizing code, reusing operations, and structuring algorithms efficiently.

G08 entries

G01Gated Recurrent UnitAI FundamentalsA Gated Recurrent Unit (GRU) is a type of recurrent neural network architecture designed to efficiently handle sequences of data by using gating mechanisms to control the flow of information through the network.

G02Generative Adversarial Network (GAN)Generative AITwo neural networks competing against each other to generate realistic synthetic data.

G03Generative AIAI FundamentalsGenerative AI refers to systems that use machine learning models to generate new data that resembles a given dataset, such as images, text, or sounds.

G04GPTGenerative AIGenerative Pre-trained Transformer, a family of large language models trained to generate text.

G05GPU AccelerationAI FundamentalsGPU acceleration refers to the use of a Graphics Processing Unit (GPU) to speed up the execution of computationally intensive tasks, particularly in parallelizable processes like those in AI and machine learning.

G06Gradient DescentMachine LearningAn optimization algorithm that iteratively adjusts model parameters to minimize the loss function.

G07Green AIAI FundamentalsGreen AI refers to the pursuit of more environmentally sustainable AI systems by reducing energy consumption and carbon footprint during the development and deployment of AI technologies.

G08GroundingAI FundamentalsGrounding in AI refers to the process of ensuring that the representations and outputs produced by an AI system are connected to real-world meanings and contexts, making them interpretable and relevant to human users.

H09 entries

H01HallucinationGenerative AIWhen AI models generate plausible-sounding but factually incorrect or fabricated information.

H02Hardware AcceleratorAI FundamentalsA hardware accelerator is a specialized hardware device or a software application that performs certain computing tasks more efficiently than general-purpose processors, especially in AI and machine learning applications.

H03HeuristicAI FundamentalsA heuristic is a practical method or approach used to make problem-solving or decision-making more efficient when an exhaustive search is impractical.

H04Homomorphic EncryptionAI FundamentalsHomomorphic encryption is a form of encryption that allows computation on ciphertexts, generating an encrypted result that, when decrypted, matches the result of operations performed on the plaintext. It is a key technology for ensuring data privacy and security in various applications.

H05Hugging FaceAI ToolsA platform and library for sharing and using pre-trained ML models, especially for NLP.

H06Human in the LoopAI FundamentalsHuman in the Loop (HITL) is an AI system design approach where human judgment is integrated into the model's decision-making process, allowing for human intervention and oversight to improve system accuracy and reliability.

H07Hybrid AIAI FundamentalsHybrid AI is an approach that combines multiple artificial intelligence techniques, such as symbolic AI and machine learning, to leverage the strengths of each and create more robust and capable systems.

H08HyperparameterMachine LearningConfiguration settings set before training that control the learning process, not learned from data.

H09Hyperparameter TuningMachine LearningThe process of finding the optimal hyperparameter values for a machine learning model.

I04 entries

I01Image SegmentationComputer VisionPartitioning an image into multiple segments or regions, classifying each pixel.

I02InferenceAI FundamentalsThe process of using a trained model to make predictions on new, unseen data.

I03Inference OptimizationAI FundamentalsInference optimization refers to the process of enhancing the efficiency and performance of AI models during the inference phase, where the model makes predictions based on input data.

I04Interpretable AIAI FundamentalsInterpretable AI refers to artificial intelligence systems whose actions can be easily understood by humans. It emphasizes transparency in AI decision-making processes, allowing users to comprehend how outputs are derived from inputs.

K02 entries

K01Knowledge DistillationAI FundamentalsKnowledge Distillation is a technique in machine learning that involves transferring knowledge from a large, complex model (teacher) to a smaller, simpler model (student) without significant loss of performance.

K02Knowledge GraphAI FundamentalsA knowledge graph is a structured representation of real-world entities and their interconnections, designed to integrate, manage, and utilize complex relational data. It serves as a powerful tool for organizing information and facilitating tasks like data retrieval, semantic search, and AI reasoning.

L10 entries

L01LabelingAI FundamentalsLabeling in AI and machine learning refers to the process of assigning meaningful tags or annotations to data, which helps algorithms learn to make predictions or classifications.

L02Large Language Model (LLM)Generative AIAI models trained on vast text data that can generate and understand human-like text.

L03LatencyAI FundamentalsLatency refers to the delay between a user's action or request and the response or outcome provided by a system, often measured in milliseconds. In AI and computing, it is a critical factor affecting the performance and user experience of real-time applications.

L04Latent SpaceAI FundamentalsLatent space refers to a lower-dimensional representation of data where similar data points are closer together, often used to capture the underlying patterns or features in datasets.

L05Linear AlgebraMathematicsThe branch of mathematics dealing with vectors, matrices, and linear transformations.

L06LLMsAI FundamentalsLLMs, or large language models, are artificial intelligence systems trained on very large collections of text to understand and generate human-like language. They learn statistical patterns among words, tokens, and concepts, allowing them to perform tasks such as answering questions, summarizing documents, translating languages, writing code, and producing original text.

L07Long Short Term MemoryAI FundamentalsLong Short Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to effectively learn long-range dependencies and overcome the vanishing gradient problem in sequences of data.

L08Long Term MemoryAI FundamentalsLong Term Memory (LTM) refers to a component of an artificial intelligence system designed to store and retrieve information over extended periods, mimicking human cognitive processes to retain knowledge and learn from past experiences.

L09Loss FunctionMachine LearningA function that measures how far model predictions are from actual values.

L10LSTM (Long Short-Term Memory)Deep LearningAn RNN variant with gates that control information flow, enabling learning of long-term dependencies.

M20 entries

M01Machine LearningAI FundamentalsA subset of AI where systems automatically learn and improve from experience without being explicitly programmed.

M02Markov Decision ProcessAI FundamentalsA Markov Decision Process (MDP) is a mathematical framework for modeling decision-making in environments where outcomes are partly random and partly under the control of a decision-maker. It is used to model problems in reinforcement learning and provides a formalism for defining the dynamics of an environment.

M03Memory Augmented ModelAI FundamentalsMemory Augmented Models are a class of models in artificial intelligence that incorporate external memory structures to improve learning and inference capabilities, mimicking some aspects of human memory processes.

M04Mixture of ExpertsAI FundamentalsMixture of Experts (MoE) is a machine learning model architecture that uses an ensemble of expert models and a gating mechanism to dynamically select and combine the outputs of these experts based on input data, optimizing for performance and efficiency.

M05MLflowAI ToolsAn open-source platform for managing the ML lifecycle including experimentation, reproducibility, and deployment.

M06MLOpsAI FundamentalsMLOps, or Machine Learning Operations, is a practice that aims to streamline and automate the deployment, monitoring, and maintenance of machine learning models in production environments, similar to DevOps for software development.

M07ModelAI FundamentalsA mathematical representation learned from data that can make predictions or decisions on new, unseen data.

M08Model AlignmentAI FundamentalsModel alignment refers to the process of ensuring that an artificial intelligence model's actions and objectives are consistent with human intentions and ethical standards, minimizing undesired outcomes.

M09Model CompressionAI FundamentalsModel compression refers to techniques used to reduce the size and computational requirements of machine learning models while maintaining their performance and accuracy.

M10Model DeploymentAI FundamentalsModel deployment is the process of integrating a machine learning model into a production environment where it can be used to make predictions or decisions in real-time for end-users or applications.

M11Model HallucinationAI FundamentalsModel hallucination refers to instances where AI models, particularly generative models, produce information or outputs that are not based on reality or the data they were trained on, essentially 'imagining' or fabricating content.

M12Model InferenceAI FundamentalsModel inference is the process of using a trained machine learning model to make predictions or generate outputs based on new, unseen data.

M13Model MonitoringAI FundamentalsModel monitoring is the process of continuously observing and evaluating machine learning models in production to ensure they perform as expected and adapt to changes in data and environment.

M14Model ParallelismAI FundamentalsModel parallelism is a technique used in deep learning where a large model is divided across multiple devices, such as GPUs or computers, enabling efficient computation by parallelizing the workload across these devices.

M15Model SecurityAI FundamentalsModel security refers to the protection of AI models from various vulnerabilities and threats that can compromise their integrity, confidentiality, and availability.

M16Model TrainingAI FundamentalsModel training is the process of teaching an AI or machine learning model to make predictions or decisions by adjusting its parameters based on input data. It involves optimizing the model to improve its performance on a given task.

M17Model TransparencyAI FundamentalsModel transparency refers to the extent to which a machine learning model's processes and decision-making criteria are understandable to humans. It is crucial for ensuring accountability, fairness, and trust in AI systems.

M18Multi Agent SystemAI FundamentalsA Multi-Agent System (MAS) is a system composed of several interacting intelligent agents, which can be autonomous, cooperative, or competitive, working towards individual or collective goals.

M19Multi-Head AttentionAI FundamentalsMulti-Head Attention is a mechanism in neural networks that improves model performance by allowing the model to focus on different parts of the input data simultaneously, often used in transformer architectures.

M20Multimodal AIAI FundamentalsMultimodal AI refers to systems designed to process and integrate multiple types of data inputs, such as text, images, and audio, to perform complex tasks that require understanding across different modalities.

N07 entries

N01Named Entity Recognition (NER)Natural Language ProcessingIdentifying and classifying named entities in text into categories like person, organization, location.

N02Natural Language GenerationAI FundamentalsNatural Language Generation (NLG) is a subfield of artificial intelligence focused on the creation of human-like text from computer data, enabling machines to communicate more naturally with humans.

N03Natural Language Processing (NLP)Natural Language ProcessingThe field of AI focused on enabling computers to understand, interpret, and generate human language.

N04Natural Language UnderstandingAI FundamentalsNatural Language Understanding (NLU) is a subfield of artificial intelligence that focuses on a machine's ability to understand and interpret human language in a way that is both meaningful and useful.

N05Neural NetworkNeural NetworksA computing system inspired by biological neural networks, consisting of interconnected nodes (neurons).

N06Neural Processing UnitAI FundamentalsA Neural Processing Unit (NPU) is a specialized hardware designed to accelerate the computation of neural networks, enhancing the efficiency and performance of AI tasks.

N07NeuronNeural NetworksA basic computational unit in a neural network that receives inputs, applies weights and activation, and produces output.

O04 entries

O01Object DetectionComputer VisionIdentifying and localizing objects within an image by drawing bounding boxes around them.

O02OntologyAI FundamentalsIn AI, ontology refers to a structured framework that categorizes and defines the relationships between concepts within a specific domain, facilitating better understanding, organization, and communication of information by AI systems.

O03Optimization AlgorithmAI FundamentalsAn optimization algorithm is a mathematical method used to find the best possible solution or outcome, often a maximum or minimum value, for a particular problem under given constraints. These algorithms are essential in AI and machine learning for improving the performance of models.

O04OverfittingMachine LearningWhen a model learns training data too well, including noise, and performs poorly on new data.

P16 entries

P01ParameterAI FundamentalsA parameter is a variable in a model that the learning algorithm adjusts during training to improve the model's performance. Parameters are essential for defining how the model processes input data and predicts outputs.

P02PathfindingAI FundamentalsPathfinding is the computational process of finding the shortest route or path from a starting point to a destination within a defined space, often used in AI for navigation and movement in virtual or real-world environments.

P03Planning AgentAI FundamentalsA planning agent is an AI system designed to make decisions and take actions to achieve specific goals by constructing and evaluating plans. It utilizes algorithms to explore potential future actions and their outcomes to optimize performance.

P04Planning AlgorithmAI FundamentalsA planning algorithm is a computational procedure used to generate a sequence of actions or steps to achieve a specified goal, often in the context of robotics, AI, or autonomous systems.

P05Policy GradientReinforcement LearningPolicy Gradient methods are a class of algorithms in reinforcement learning that optimize the policy directly by using the gradient of the expected reward with respect to the policy parameters.

P06Post-TrainingAI FundamentalsPost-training refers to the processes and techniques applied to a machine learning model after the initial training phase to improve its performance, efficiency, or interpretability without further modifying the core learning algorithm.

P07Pre TrainingAI FundamentalsPre-training refers to the process of training an AI model on a large dataset before fine-tuning it on a specific task. It's a foundational step in transfer learning, helping models learn general features that can be adapted to various applications.

P08PrecisionAI FundamentalsIn the context of AI and machine learning, precision is a metric that quantifies the number of true positive results divided by the number of all positive results returned by the model, indicating its accuracy in identifying relevant instances.

P09Principal Component AnalysisAI FundamentalsPrincipal Component Analysis (PCA) is a statistical technique used to simplify a dataset by reducing its dimensionality while preserving as much variability as possible.

P10Privacy Preserving AIAI FundamentalsPrivacy Preserving AI refers to methods and technologies in artificial intelligence that aim to protect individual and organizational privacy while still enabling the effective use of data. It involves techniques that ensure data privacy and security such as differential privacy, federated learning, and homomorphic encryption.

P11Probabilistic ModelAI FundamentalsA probabilistic model is a statistical framework used to predict outcomes by accounting for uncertainty and variability in data. It represents the relationships between variables using probability distributions.

P12ProbabilityMathematicsThe mathematical study of uncertainty and random events.

P13PromptAI FundamentalsIn AI, a 'prompt' refers to the input or set of instructions given to an AI model, particularly in natural language processing (NLP) tasks, to elicit a desired response or behavior.

P14Prompt EngineeringGenerative AIThe practice of crafting effective inputs to get desired outputs from AI models.

P15PruningAI FundamentalsPruning in machine learning and artificial intelligence refers to the process of removing redundant or non-essential components from a model, such as neurons or weights, to improve efficiency and performance without significantly impacting accuracy.

P16PyTorchAI ToolsAn open-source machine learning framework developed by Meta AI, popular in research.

Q02 entries

Q01Q-LearningReinforcement LearningA reinforcement learning algorithm that learns the value of actions in states to determine optimal behavior.

Q02QuantizationAI FundamentalsQuantization in AI and machine learning refers to the process of reducing the precision of the numbers used to represent a model’s parameters, often to reduce the model size and increase computational efficiency.

R17 entries

R01RAG (Retrieval-Augmented Generation)Generative AICombining retrieval systems with language models to generate responses grounded in external knowledge.

R02Ranking ModelAI FundamentalsA ranking model is a type of algorithm used to order or prioritize items based on certain criteria, commonly used in search engines and recommendation systems.

R03Reasoning ModelAI FundamentalsA reasoning model in AI is a system or algorithm designed to mimic human reasoning and decision-making processes in order to solve complex problems and draw logical conclusions from given data or information.

R04RecallAI FundamentalsRecall is a performance metric used in classification problems to measure the ability of a model to identify all relevant instances correctly. It is defined as the ratio of true positive predictions to the total number of actual positive instances.

R05Recommendation SystemAI FundamentalsA recommendation system is a type of information filtering system that seeks to predict the rating or preference a user would give to an item, leveraging data about users and items to make suggestions.

R06Recurrent Neural Network (RNN)Deep LearningA neural network architecture designed for sequential data with connections between nodes forming cycles.

R07RegressionMachine LearningPredicting a continuous numerical value based on input features.

R08RegularizationAI FundamentalsRegularization is a technique used in machine learning to prevent overfitting by adding additional information or constraints to a model. This helps improve the model's generalization ability on unseen data.

R09Reinforcement LearningReinforcement LearningMachine learning where an agent learns to make decisions by taking actions and receiving rewards or penalties.

R10Reinforcement Learning from Human FeedbackAI FundamentalsReinforcement Learning from Human Feedback (RLHF) is a machine learning approach where an AI agent learns to make decisions by receiving feedback from humans, enhancing its ability to align with human preferences and values.

R11ReLUAI FundamentalsReLU, or Rectified Linear Unit, is an activation function used in neural networks that outputs the input directly if it is positive, otherwise, it outputs zero. This simple yet effective function helps neural networks learn complex patterns by introducing non-linearity.

R12Responsible AIAI EthicsDeveloping and deploying AI systems that are ethical, fair, transparent, and accountable.

R13Retrieval Augmented GenerationAI FundamentalsRetrieval Augmented Generation (RAG) is a technique in artificial intelligence that combines information retrieval methods with generative models to produce more accurate and contextually relevant outputs.

R14RoboticsAI FundamentalsRobotics is a multidisciplinary field that involves the design, construction, operation, and use of robots, integrating technologies from computer science, engineering, and artificial intelligence to create machines that can assist, augment, or replicate human activities.

R15RobustnessAI FundamentalsRobustness in AI refers to the ability of a model to maintain its performance when exposed to variations, noise, or adversarial examples in input data, ensuring reliability and stability.

R16ROC CurveAI FundamentalsA ROC (Receiver Operating Characteristic) curve is a graphical representation that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. It plots the True Positive Rate (TPR) against the False Positive Rate (FPR) at various threshold settings.

R17Rule Based SystemAI FundamentalsA Rule Based System is an AI system that uses predefined rules to process data and make decisions or solve problems. These systems rely on a set of 'if-then' rules to derive conclusions or actions based on input data.

S21 entries

S01ScalabilityAI FundamentalsScalability is the capability of an AI system, model, or process to effectively handle increasing volumes of work or its potential to be enlarged to accommodate that growth.

S02Search AlgorithmAI FundamentalsA search algorithm is a step-by-step procedure used to locate specific data among a collection of data or to find optimal solutions to problems by exploring possible configurations or paths.

S03Secure ComputationAI FundamentalsSecure computation refers to cryptographic techniques that enable multiple parties to jointly compute a function over their inputs while keeping those inputs private. It ensures data privacy and integrity during computation, crucial for sensitive data processing.

S04Self AttentionAI FundamentalsSelf-attention is a mechanism in neural networks that allows for the weighting of different positions of a sequence to effectively capture context and dependencies for tasks such as language processing.

S05Self Driving SystemAI FundamentalsA self-driving system is an integrated set of technologies that enable a vehicle to operate without human intervention by using sensors, cameras, and AI algorithms to perceive the environment and make driving decisions.

S06Self-Supervised LearningAI FundamentalsSelf-supervised learning is a form of unsupervised learning where a model is trained on data without explicit labels, using the data itself to generate supervisory signals. It leverages the inherent structure of the data to create tasks that the model can learn from, enabling it to understand complex patterns and representations.

S07Semantic SearchAI FundamentalsSemantic search is a search technique that aims to improve search accuracy by understanding the intent and contextual meaning of search queries and the relationships between words, beyond simple keyword matching.

S08Semi-Supervised LearningMachine LearningMachine learning approach using a small amount of labeled data with a large amount of unlabeled data.

S09Sentiment AnalysisNatural Language ProcessingDetermining the emotional tone or opinion expressed in text, typically positive, negative, or neutral.

S10Sequence to Sequence ModelAI FundamentalsA sequence to sequence model is a type of neural network architecture designed to transform one sequence into another, often used in tasks such as language translation, where it converts a sequence of words in one language to another.

S11SigmoidAI FundamentalsThe sigmoid function is an S-shaped mathematical function often used in artificial neural networks to introduce non-linearity, helping networks learn complex patterns. It maps input values to an output between 0 and 1, making it suitable for binary classification tasks.

S12SoftmaxAI FundamentalsSoftmax is a mathematical function that converts a vector of real numbers into a probability distribution, where each value is between 0 and 1, and the sum of all values is 1. It is commonly used in machine learning, especially in classification tasks, to predict the probability of each class.

S13Sparse ModelAI FundamentalsA sparse model in AI and machine learning refers to a model that has a significant number of parameters set to zero, which reduces complexity and improves efficiency without compromising accuracy significantly.

S14Speech RecognitionAI FundamentalsSpeech recognition is a technology that enables the conversion of spoken language into text by computers, allowing for voice-driven applications and interfaces.

S15Speech SynthesisAI FundamentalsSpeech synthesis is the artificial production of human speech, often implemented using computer systems to convert text into spoken words.

S16Speech to TextAI FundamentalsSpeech to Text refers to the technology that converts spoken language into written text, using algorithms and machine learning models to process audio signals and transcribe them into readable text formats.

S17Stochastic Gradient DescentAI FundamentalsStochastic Gradient Descent (SGD) is an optimization algorithm used to minimize the loss function in machine learning models by iteratively updating the model parameters. Unlike traditional gradient descent, which uses the entire dataset, SGD updates parameters using a single data point or a small batch at a time, offering faster computation and convergence.

S18Supervised LearningMachine LearningMachine learning approach where models learn from labeled training data to predict outcomes.

S19Symbolic AIAI FundamentalsSymbolic AI, also known as classical AI, is an approach to artificial intelligence that represents knowledge through symbols and logical rules to simulate human reasoning and problem-solving.

S20Synthetic DataAI FundamentalsSynthetic data is artificially generated data that mimics real-world data. It is used for training machine learning models when real data is scarce, sensitive, or costly to obtain.

S21System PromptAI FundamentalsA system prompt is a predefined instruction or set of instructions given to an artificial intelligence model to guide its behavior, responses, or outputs during interaction with users.

T14 entries

T01TensorFlowAI ToolsAn open-source machine learning framework developed by Google for building and deploying ML models.

T02Text to SpeechAI FundamentalsText to Speech (TTS) is a technology that converts written text into spoken voice output, utilizing various computational techniques to synthesize natural-sounding speech from text input.

T03ThroughputAI FundamentalsThroughput refers to the rate at which a system can process data or complete tasks over a specific period, often used to measure the efficiency and capacity of systems in AI, computing, and networking.

T04TokenAI FundamentalsIn the context of AI and natural language processing, a token is a unit of text that serves as the smallest element for processing by an AI model. It can be a word, character, or sub-word, depending on the tokenization strategy used.

T05Token LimitAI FundamentalsA token limit refers to the maximum number of tokens a language model can process in a single input. Tokens are pieces of text, such as words or characters, that are used in natural language processing.

T06TokenizationNatural Language ProcessingBreaking text into smaller units (tokens) such as words, subwords, or characters.

T07Tool CallingAI FundamentalsTool calling refers to the process by which an AI system, often through a natural language interface, requests the use of external tools or services to accomplish specific tasks beyond its native capabilities.

T08TPUAI FundamentalsA TPU, or Tensor Processing Unit, is a type of application-specific integrated circuit (ASIC) developed by Google specifically for accelerating machine learning workloads, particularly those involving neural networks.

T09Train-Test SplitData ScienceDividing data into separate sets for training and evaluating model performance.

T10TrainingAI FundamentalsThe process of teaching a machine learning model to make predictions by exposing it to data and adjusting its parameters.

T11Transfer LearningDeep LearningUsing knowledge learned from one task to improve performance on a different but related task.

T12TransformerDeep LearningA neural network architecture based on self-attention mechanisms, powering modern language models.

T13Tree of ThoughtAI FundamentalsA Tree of Thought is a conceptual framework used to model decision-making processes in artificial intelligence, where each node represents a possible state or decision point and the branches represent the possible actions or decisions leading to new states.

T14Trustworthy AIAI FundamentalsTrustworthy AI refers to artificial intelligence systems that are designed and deployed in a manner that ensures they are reliable, transparent, fair, and aligned with ethical guidelines, fostering trust among users and stakeholders.

U02 entries

U01UnderfittingMachine LearningWhen a model is too simple to capture the underlying patterns in the data.

U02Unsupervised LearningMachine LearningMachine learning approach where models find patterns in data without labeled examples.

V04 entries

V01Variational AutoencoderAI FundamentalsA Variational Autoencoder (VAE) is a type of generative model that uses neural networks to encode input data into a probabilistic latent space and then reconstructs the data from this latent space, enabling both generation and transformation of data.

V02Vector DatabaseAI FundamentalsA vector database is a specialized database designed to store, index, and query vector data efficiently, often used in AI applications to manage high-dimensional data like embeddings from machine learning models.

V03Vector EmbeddingAI FundamentalsVector embedding is a technique in machine learning where entities, such as words or images, are represented as vectors in a continuous vector space, preserving semantic relationships.

V04Vision TransformerAI FundamentalsA Vision Transformer (ViT) is a type of model architecture that leverages the transformer model, traditionally used in natural language processing, for image classification tasks by treating image patches as sequences of tokens.

W01 entry

W01Word EmbeddingNatural Language ProcessingDense vector representations of words that capture semantic meaning and relationships.

Z01 entry

Z01Zero Shot LearningAI FundamentalsZero Shot Learning (ZSL) is a machine learning paradigm where a model can correctly make predictions for classes it has not been explicitly trained on by leveraging semantic information about the class labels.

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