AI &amp; ML glossary .

Accuracy 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.

Activation Function

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

Active Learning

Active 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.

Adam Optimizer

Adam (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.

Adversarial Attack

An 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.

Adversarial Example

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

Agentic AI

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

AI Adoption

AI 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.

AI Agent

An 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.

AI Benchmark

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

AI Bias

AI Ethics

Systematic errors in AI systems that create unfair outcomes for certain groups.

AI Ethics

AI 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.

AI Governance

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

AI Impact Assessment

AI 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.

AI Literacy

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

AI Maturity

AI 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.

AI Policy

AI 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.

AI Readiness

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

AI Regulation

AI 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.

AI Roadmap

An 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.

AI Safety

AI 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.

AI Strategy

AI 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.

AI Transformation

AI 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.

AI Use Case

An 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.

Algorithm

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

Annotation

Annotation 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.

Anomaly Detection

Anomaly 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.

Artificial General Intelligence

Artificial 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.

Artificial Intelligence (AI)

The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.

Artificial Neural Network

An 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.

Artificial Superintelligence

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

Attention Mechanism

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

AUC

AUC, 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.

Auditability

Auditability 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.

Autoencoder

An 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.

Autonomous Agent

An 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.

Autonomous System

An 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.

Backpropagation

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

Batch Size

The number of training examples used in one iteration of model training.

Bayesian Network

A 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.

BERT

Bidirectional Encoder Representations from Transformers, a pre-trained language model for NLP tasks.

Bias in AI

Bias 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.

Calculus

Mathematics

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

Carbon Aware AI

Carbon 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.

Chain of Thought

Chain 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.

Classification

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

Cloud AI

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

Clustering

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

Collaborative Filtering

Collaborative 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.

Compute Budget

A 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.

Computer Vision

The field of AI enabling computers to interpret and understand visual information from the world.

Concept Drift

Concept 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.

Confusion Matrix

A 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.

Content Based Filtering

Content 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.

Context Window

A 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.

Continuous Learning

Continuous 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.

Convolutional Neural Network (CNN)

A neural network architecture designed for processing grid-like data such as images.

Cross-Validation

A technique to evaluate model performance by training and testing on different subsets of data.

Data Augmentation

Data Science

Artificially increasing training data by creating modified versions of existing data.

Data Drift

Data 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.

Data Governance

Data 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.

Data Parallelism

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

Data Pipeline

A 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.

Data Preprocessing

Data Science

Cleaning and transforming raw data into a format suitable for machine learning.

Deep Learning

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

Dense Model

A 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.

Differential Privacy

Differential 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.

Diffusion Model

Generative models that learn to create data by reversing a gradual noising process.

Dimensionality Reduction

Dimensionality 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.

Distributed Training

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

Dropout

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

Edge AI

Edge 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.

Embedding

An 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.

Encoder Decoder Architecture

The 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.

Energy Efficient AI

Energy 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.

Ensemble Learning

Combining multiple models to produce better predictions than any single model.

Epoch

One complete pass through the entire training dataset during model training.

Evaluation Metric

An 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.

Expert System

An 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.

Explainability

AI Ethics

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

Explainable AI

Explainable 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.

F1 Score

The 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.

Fairness in AI

Fairness 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.

Feature

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

Feature Engineering

The process of using domain knowledge to create new features that improve model performance.

Feature Store

A 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.

Federated Learning

Federated 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.

Few Shot Learning

Few 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.

Fine-Tuning

Adapting a pre-trained model to a new task by training on task-specific data.

Foundation Model

A 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.

Function Calling

Function 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.

Gated Recurrent Unit

A 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.

Generative Adversarial Network (GAN)

Two neural networks competing against each other to generate realistic synthetic data.

Generative AI

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

GPT

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

GPU Acceleration

GPU 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.

Gradient Descent

An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.

Green AI

Green 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.

Grounding

Grounding 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.

Hallucination

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

Hardware Accelerator

A 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.

Heuristic

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

Homomorphic Encryption

Homomorphic 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.

Hugging Face

A platform and library for sharing and using pre-trained ML models, especially for NLP.

Human in the Loop

Human 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.

Hybrid AI

Hybrid 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.

Hyperparameter

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

Hyperparameter Tuning

The process of finding the optimal hyperparameter values for a machine learning model.

Image Segmentation

Computer Vision

Partitioning an image into multiple segments or regions, classifying each pixel.

Inference

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

Inference Optimization

Inference 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.

Interpretable AI

Interpretable 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.

Knowledge Distillation

Knowledge 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.

Knowledge Graph

A 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.

Labeling

Labeling 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.

Large Language Model (LLM)

AI models trained on vast text data that can generate and understand human-like text.

Latency

Latency 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.

Latent Space

Latent 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.

Linear Algebra

Mathematics

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

Long Short Term Memory

Long 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.

Long Term Memory

Long 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.

Loss Function

A function that measures how far model predictions are from actual values.

LSTM (Long Short-Term Memory)

An RNN variant with gates that control information flow, enabling learning of long-term dependencies.

Machine Learning

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

Markov Decision Process

A 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.

Memory Augmented Model

Memory 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.

Mixture of Experts

Mixture 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.

MLflow

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

MLOps

MLOps, 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.

Model

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

Model Alignment

Model 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.

Model Compression

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

Model Deployment

Model 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.

Model Hallucination

Model 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.

Model Inference

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

Model Monitoring

Model 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.

Model Parallelism

Model 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.

Model Security

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

Model Training

Model 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.

Model Transparency

Model 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.

Multi Agent System

A 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.

Multi-Head Attention

Multi-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.

Multimodal AI

Multimodal 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.

Named Entity Recognition (NER)

Identifying and classifying named entities in text into categories like person, organization, location.

Natural Language Generation

Natural 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.

Natural Language Processing (NLP)

The field of AI focused on enabling computers to understand, interpret, and generate human language.

Natural Language Understanding

Natural 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.

Neural Network

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

Neural Processing Unit

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

Neuron

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

Object Detection

Computer Vision

Identifying and localizing objects within an image by drawing bounding boxes around them.

Ontology

In 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.

Optimization Algorithm

An 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.

Overfitting

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

Parameter

A 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.

Pathfinding

Pathfinding 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.

Planning Agent

A 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.

Planning Algorithm

A 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.

Policy Gradient

Reinforcement Learning

Policy 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.

Post-Training

Post-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.

Pre Training

Pre-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.

Precision

In 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.

Principal Component Analysis

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

Privacy Preserving AI

Privacy 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.

Probabilistic Model

A 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.

Probability

Mathematics

The mathematical study of uncertainty and random events.

Prompt

In 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.

Prompt Engineering

The practice of crafting effective inputs to get desired outputs from AI models.

Pruning

Pruning 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.

PyTorch

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

Q-Learning

Reinforcement Learning

A reinforcement learning algorithm that learns the value of actions in states to determine optimal behavior.

Quantization

Quantization 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.

RAG (Retrieval-Augmented Generation)

Combining retrieval systems with language models to generate responses grounded in external knowledge.

Ranking Model

A 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.

Reasoning Model

A 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.

Recall

Recall 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.

Recommendation System

A 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.

Recurrent Neural Network (RNN)

A neural network architecture designed for sequential data with connections between nodes forming cycles.

Regression

Predicting a continuous numerical value based on input features.

Regularization

Regularization 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.

Reinforcement Learning

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

Reinforcement Learning from Human Feedback

Reinforcement 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.

ReLU

ReLU, 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.

Responsible AI

AI Ethics

Developing and deploying AI systems that are ethical, fair, transparent, and accountable.

Retrieval Augmented Generation

Retrieval 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.

Robotics

Robotics 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.

Robustness

Robustness 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.

ROC Curve

A 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.

Rule Based System

A 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.

Scalability

Scalability 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.

Search Algorithm

A 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.

Secure Computation

Secure 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.

Self Attention

Self-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.

Self Driving System

A 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.

Self-Supervised Learning

Self-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.

Semantic Search

Semantic 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.

Semi-Supervised Learning

Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.

Sentiment Analysis

Determining the emotional tone or opinion expressed in text, typically positive, negative, or neutral.

Sequence to Sequence Model

A 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.

Sigmoid

The 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.

Softmax

Softmax 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.

Sparse Model

A 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.

Speech Recognition

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

Speech Synthesis

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

Speech to Text

Speech 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.

Stochastic Gradient Descent

Stochastic 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.

Supervised Learning

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

Symbolic AI

Symbolic 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.

Synthetic Data

Synthetic 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.

System Prompt

A 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.

TensorFlow

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

Text to Speech

Text 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.

Throughput

Throughput 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.

Token

In 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.

Token Limit

A 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.

Tokenization

Breaking text into smaller units (tokens) such as words, subwords, or characters.

Tool Calling

Tool 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.

TPU

A 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.

Train-Test Split

Data Science

Dividing data into separate sets for training and evaluating model performance.

Training

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

Transfer Learning

Using knowledge learned from one task to improve performance on a different but related task.

Transformer

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

Tree of Thought

A 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.

Trustworthy AI

Trustworthy 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.

Underfitting

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

Unsupervised Learning

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

Variational Autoencoder

A 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.

Vector Database

A 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.

Vector Embedding

Vector 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.

Vision Transformer

A 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.

Word Embedding

Dense vector representations of words that capture semantic meaning and relationships.

Zero Shot Learning