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AI Glossary

A

AI (Artificial Intelligence)

AI refers to the stimulation of human brain processes by computers or machines. Artificial intelligence can mimic the human brain’s processing to learn things, such as communication, reasoning, decision-making, and logic.

Accuracy

The accuracy refers to the percentage of right classifications that a trained machine learning model attains. It is usually calculated as the correct number of predictions divided by the total number of predictions throughout all the classes. The term Accuracy also refers to ACC.

Algorithm

An algorithm is an absolute set of instructions that shows how a problem should be solved. Algorithms can perform various tasks, such as calculations, data processing, and automated reasoning.

Abductive Reasoning

Abductive reasoning involves taking some observations or evidence and making the best possible guess or hypothesis to explain them. It is about assuming the most likely explanation for the available data when you don’t have all the information.

Artificial Narrow Intelligence (ANI)

It’s a type of artificial intelligence that deals with specific types of tasks rather than emotional consciousness. These AI models are designed to perform specific tasks, such as collecting weather updates, generating data analytics reports by analyzing raw data or playing a game.

Action Description Language

It is a special language used to describe the actions an AI agent can take and their effects.

Action language

Action language is a formal language that is used to represent and reason about actions and their effects in a dynamic environment

Action Model Learning (AML)

Action Model Learning (AML) is a technique in artificial intelligence, particularly in machine learning and automated planning. In this method, the AI learns from the effects and preconditions of actions within a system.

Action Planning

It refers to finding the best suite of actions that yields the best outcome for a specific goal.

Action Selection

This terminology consists of AI deciding what to do next based on its goals and the current situation.

Activation Function

An activation function in artificial neural networks helps decide whether a neuron should be activated based on its input. It is like a switch or a filter that processes input and produces an output based on specific criteria.

Active learning

Active learning in AI is a special case of machine learning in which the AI can ask for more information or specific examples to improve its learning.

Actor-critic

It is a learning process in which one part of AI, the Actor, decides about an action to take, and the other part, the Critic, evaluates how this specific task is done. This means the Actor makes decisions, and the Critic evaluates those decisions, helping the Actor improve over time based on the feedback.

Adaptive algorithm

It refers to those algorithms that change their behavior based on the data they receive.

Admissible Heuristic

It is a fundamental principle in search algorithms that always estimate the cost of reaching the goal. This aids the AI in finding the optimal solution without wasting time exploring unfavorable options.

Adversarial machine learning

It is the study of how to make AI systems powerful against attacks that try to trick or mislead them.

Adversarial Search

It is a term for a technique in which an AI considers its opponent’s moves and tries to find the best strategy to win.

Agent

Agents are software programs that can interact with their environment, collect massive amounts of information (data), and use that data to perform pre-planned tasks.

Agent Architecture

Agent architecture is a blueprint that shows how an AI agent is built, including its sensors, actuators, and decision-making processes.

Agent-based model

This term refers to computer simulation that analyzes the behavior of individual agents and how they interact with each other and their environment. In addition, Agent-based modeling also studies the behavior of interactions between people, things, time, and places.

AI Accelerator

This terminology refers to hardware designed to speed up AI computations and make them run faster and smoother. 

AI Copilot

An AI Copilot is a conversational AI that uses large language models to allow users to perform various tasks within an organization. 

AI Alignment

The AI Alignment ensures that the AI systems remain aligned with the fundamental values and goals of humans. 

AI Art

This term refers to the art created with the help of artificial intelligence. 

AI Box

It is a hypothetical scenario in which a superintelligent AI is bound to a virtual environment to prevent it from harming the real world. 

AI-complete

It is a problem that is considered as hard to solve as creating true artificial intelligence.

AI Control Problem

This term refers to the challenge of ensuring that superintelligent AI systems remain under human control.

AI Effect

This refers to the phenomenon where once an AI successfully performs a task, it’s no longer considered a sign of true intelligence. In other words, as AI capabilities grow, our perception of what constitutes intelligence shifts. 

AI safety

AI safety is a field of research focused on ensuring that AI systems are safe and beneficial for humanity.

AI Plugin

AI plugins are specialized software components that integrate artificial intelligence capabilities into an existing interface or program. These modules increase system efficiency. 

Algorithm

A step-by-step set of instructions for solving a problem or completing a task.

Algorithmic Bias

It is a systematic error in an AI system’s output that results in unfair or discriminatory outcomes.

Algorithmic Efficiency

This term refers to how well an algorithm uses resources like time and memory. A more efficient algorithm gets the job done faster and with less effort.

AlphaGo

It is a famous AI program developed by DeepMind that beat a world-champion Go player.

AlphaZero

An updated, more generalized version of AlphaGo that can learn to play other games like chess and shogi without human knowledge.

Ambient Intelligence

This term refers to the artificial intelligence that is seamlessly integrated into our surroundings, making our lives easier and more convenient.

Analysis of Algorithms

It is the study of how efficient different algorithms are and how they compare to each other.

Anytime Algorithm

It is an algorithm that can provide a solution at any time, even if it hasn’t finished running completely. The longer it runs, the better the solution gets.

Artificial General Intelligence (AGI)

It is an AI that can perform any intellectual task that a human being can.

Artificial Immune System

This term refers to an AI inspired by the human immune system that detects and responds to cyberattacks.

Artificial life

It is the study of creating and simulating life-like systems using computers.

Artificial Moral Agent

This term refers to an AI system that can make moral judgments and decisions.

Artificial Neural Network (ANN)

It is a term that refers to a method in which AI teaches a computer program to process data in a way that is inspired by the human brain.

Artificial Neural Network (ANN)

It is a term that refers to a method in which AI teaches a computer program to process data in a way that is inspired by the human brain.

Agentic AI

Agentic AI is an AI system designed to pursue complex tasks and workflows automatically and with the least human supervision. They exhibit a higher degree of autonomy and decision-making capabilities.

B

Backpropagation

Backpropagation is a method to train a neural network. It involves adjusting the weights of connections between neurons based on the errors between the neuron’s outputs and the desired outputs. In this scenario, neural networks learn from their mistakes.

Backpropagation Through Time

A variation of backpropagation is used for training recurrent neural networks, which process sequences of data. 

Backward Chaining

It refers to a reasoning method that starts with the goal and works backward to find the steps needed to achieve it

Base Case

A base case typically refers to the simplest or most fundamental instance of a problem or data structure. It is the starting point or simplest form of a problem that can be solved directly and is often used as a termination condition for recursive functions or algorithms.

Baseline In Machine Learning

A baseline model is a simple model used as a reference point to evaluate the performance of more complex models. It serves as a benchmark to determine if the sophisticated models are truly providing an improvement.

Batch Normalization

Batch normalization is a technique that is used in neural networks to make the training of an AI model faster and more stable by normalizing the inputs to each layer.

Bayes Classifier

A Bayes classifier is a machine learning algorithm that predicts the probability of a data point belonging to a particular class based on its features. It calculates these probabilities using Bayes’ theorem – a mathematical formula used to calculate the probability of an event occurring based on new data.

Bayes Error Rate

This term refers to the theoretical lowest possible error rate for a given classification problem.

Benchmark or Benchmarking In Machine Learning

Benchmarking is a standard dataset and set of evaluation metrics used to compare the performance of different machine-learning algorithms.

Bias In AI

Business is a phenomenon in which an AI model produces results that reflect or amplify the biases based on the data it was trained on.

Bias–Variance Tradeoff

The bias-variance tradeoff is a fundamental concept in machine learning that describes the relationship between a model’s complexity, its accuracy on training data, and its ability to generalize to new and unseen data.

BERT

Google uses Bidirectional Encoder Representation (BERT) from Transformers. It is a large-scale pre-trained model that was first trained on a massive amount of unannotated data and then transferred to Natural Language Processing functions.

Boolean Data Type

This term refers to a data type that can only have two values, such as true or false.

Big Data

Big Data refers to massive datasets that are complex and rapidly growing, making them difficult to process using traditional data-processing applications. In the world of AI, Big Data serves as the fuel that powers machine learning algorithms.

C

Capsule Neural Network (CapsNet)

It is a type of neural network that tries to understand the relationships between different parts of an object better, making it stronger and allowing for changes in viewpoint or orientation.

Case-Based Reasoning (CBR)

It is a terminology that refers to solving new problems by finding similar problems that have been solved before and adapting their solutions.

Catastrophic Forgetting

It is a phenomenon in which a neural network forgets previously learned information when it learns new information. It’s like trying to learn a new language and forgetting your native tongue in the process.

Chatbot

This term refers to AI-powered programs designed to simulate conversations with humans when asked to perform a task.

ChatGPT

It refers to a Chat-based Generative Pre-trained Transformer, a specific type of chatbot that uses a large language model to generate human-like text in response to a wide range of prompts and questions.

Classification

Classification is a supervised machine learning technique in which the AI model learns to predict a categorical label based on input data. It is like sorting objects into different boxes based on their characteristics.

Clustering

Clustering terminology refers to an unsupervised machine-learning technique that involves grouping similar data points without any predefined labels.

Clustering High-Dimensional Data

It is a process of making groups of similar data points together when the data has many features or dimensions.

Cognitive Architecture

It is a theoretical framework that is used to understand how the human mind works and is often used in AI research to design intelligent agents.

Cognitive Map

A cognitive map is an internal representation of an environment that allows an AI agent to understand, navigate, and make decisions within it. It’s essentially a mental model of the world that helps the agent form expectations, make predictions, and plan actions.

Cognitive Computing

It is a field of artificial intelligence that focuses on creating systems that can learn and reason like humans.

Cataphora

Cataphora in AI, particularly in the field of Natural Language Processing (NLP), refers to a linguistic phenomenon in which a pronoun or noun is used before the noun or phrase it refers to.

For example, in the sentence “If he can do it, so can I,” “he” is a cataphora that refers to a previously unspecified person.

Computational Creativity

This term refers to the use of computers to generate creative works, such as art, music, or literature.

Computational Learning Theory

It is a term used for theoretical study of machine learning algorithms and their performance guarantees.

Computational Linguistics

Computational linguistics is the use of computational methods to study and process human language.

Computer Audition (CA)

It is the field of AI focuses on enabling computers to understand and interpret sounds, such as speech and music

Computer Vision

This term refers to a field of artificial intelligence that enables computers to interpret and understand visual information from the world around them.
It involves teaching computers to “see” and understand images and videos in the same way humans do.

Convergence Machine Learning

It is a process by which a machine learning algorithm’s performance on a training set improves over time, providing more accurate results.

Cross-Entropy Method

This term refers to optimizing algorithms used in machine learning, especially for training classification models.

Computer Science

Computer Science is the systematic study of computational processes and their application. It encompasses the theoretical foundations of information and computation, as well as the practical techniques for designing and building software and hardware systems.

D

Data

It is the raw information, like facts, numbers, measurements, etc., that AI systems use to learn, make predictions, and perform tasks.

Data Augmentation

This term refers to creating new, artificial data from existing data, usually by making slight changes or transformations.

Data Cleaning

It is the process of fixing or removing errors, inconsistencies, and missing values from data.

Data Compression

This terminology refers to reducing the size of data while keeping the essential information.

Data Mining

It is a process of discovering patterns and insights in large datasets using AI and statistical techniques.

Data Quality

This term refers to how accurate, complete, and reliable the data is. High-quality data is like a clear map that guides the AI to the correct destination, while low-quality data is like a foggy map that can lead the AI wrong.

Data set

It is a collection of related data that is usually organized in a table or similar structure. 

Data structure

It is a way of organizing and storing data in a computer so that it can be accessed and used efficiently whenever AI needs it.

Data Warehouse

A large repository of data collected from various sources within an organization. The AI can access this giant library and can access historical and current information for analysis and decision-making.

Decision intelligence

This term refers to the use of data, analytics, and AI to improve decision-making. This intelligence enables AI to make informed choices based on evidence and insights.

Decision Theory

It is a branch of mathematics that studies how to make optimal decisions under uncertainty. This theory’s strategies can help AI systems make the best choices even when they don’t have all the information.

Decision Tree

A decision tree is a tree-like model that represents decisions and their possible consequences. With this methodology, artificial intelligence follows different branches based on the input data to conclude.

Decision Tree Learning

It is a machine-learning technique that builds a decision tree from labeled training data. Decision tree learning is a supervised machine learning method used to create models. These models predict the value of a target variable by learning simple decision rules inferred from the data features.

Deep Blue

It was the first computer chess-playing system to beat a reigning world chess champion under regular time controls. This landmark achievement in AI demonstrated the potential for machines to excel at complex tasks previously thought to be the domain of human intelligence.

Deep Learning

A type of machine learning that uses artificial neural networks with many layers to learn complex patterns from data. The human brain’s structure inspires it and works by processing information in stages.

DeepMind

DeepMind is a leading AI research company, now part of Google, known for developing groundbreaking AI systems like AlphaGo and AlphaZero.

Discriminative Model

It is a type of machine learning model that focuses on directly predicting the most likely output or label given the input data without explicitly modeling the underlying probability distribution. It focuses on finding the decision boundary that best separates different classes.

Distributed Artificial Intelligence (DAI)

It is a sub-branch of AI that involves multiple intelligent agents working together to solve a problem. DAI also refers to decentralized AI, which is like a team of specialized AI agents, each with its skills, collaborating to complete a complex task.

Dropout Neural Networks

It is a technique used during training in which some neurons are randomly “dropped out” or ignored. This helps prevent overfitting and memorizing the training data too well and improves the model’s ability to generalize to new data.

E

Ethics in AI

Ethics in AI involve ensuring that AI is developed and used responsibly. They also involve considering the potential impact of AI on society, individuals, and the environment. This includes topics like fairness, privacy, safety, and accountability.

Expert System

An expert system is a computer program designed to simulate the decision-making ability of a human expert in a specific field. It uses a knowledge base and inference engine to provide advice or solutions to problems.

Enterprise AI

Enterprise AI refers to the application of AI technologies across an entire organization to improve efficiency, decision-making, and customer experiences. It involves integrating AI into various business processes and systems.

Explainability

Explainability in AI is the ability to understand and interpret how an AI model arrives at a particular decision or prediction. It is about making AI models transparent and accountable.

Extensibility

Extensibility refers to the ability of an AI system to be expanded or modified to accommodate new features, data, or use cases without significantly changing the core neural network architecture.

Extraction

Extraction in AI refers to the process of extracting specific information or features from data. This can involve extracting text from documents, features from images, or knowledge from text.

Embedding

Embeddings are numerical representations of words, phrases, or other pieces of text in a high-dimensional space. They capture semantic and syntactic relationships between words and are used in various NLP tasks, such as machine translation, text summarization, and question answering.

Emergent Behavior

Emergent behavior in AI occurs when a complex system exhibits unexpected behaviors that arise from the interactions of its components. These behaviors are not explicitly programmed but emerge from the system’s dynamics.

End-to-End Learning

End-to-end learning is a machine learning pattern where a model is trained to perform a complete task from raw input data to desired output without requiring intermediate handcrafted features.

Explainable AI (XAI)

Explainable AI (XAI) is about making AI models understandable to humans. It’s like adding subtitles to a complex movie so everyone can follow the story. By understanding how an AI reaches a decision, we can trust it more and use it better.

Edge Mode

An edge model is an AI model that runs on a device rather than a cloud server. This makes it faster and allows it to work without an internet connection.

Embedding

An embedding is a numerical representation of a piece of data, like a word or an image. It’s like turning things into numbers that computers can understand and process. Embeddings are used in many AI applications, from search engines to language translation.

Emotion AI (Affective Computing

Emotion AI is about teaching computers to understand and respond to human emotions. It’s like giving computers the ability to read people’s feelings based on facial expressions, voice tone, or text messages.

Entity

In AI, an entity is a real-world object or concept that can be identified and tracked. It could be a person, place, thing, or event. For example, in a sentence like “John loves to eat apples,” “John,” “apples,” and “eat” are entities.

Environmental, Social, and Governance (ESG)

ESG is a framework used to evaluate a company’s performance in terms of environmental, social, and governance factors. It’s becoming increasingly crucial in AI as companies consider the ethical implications of their technology.

ETL (Entity Recognition, Extraction)

ETL involves identifying and extracting specific pieces of information (entities) from text data, such as finding names, dates, locations, or organizations in a news article.

Extraction or Keyphrase Extraction

It is a process of identifying and extracting important words or phrases from a text document. It’s like summarizing a text by picking out the main points.

F

Facial Recognition

Facial recognition is a technology that identifies or verifies a person based on their facial image. It compares a face’s features to a database of known faces.

Foundation Model

A foundation model is a large AI model trained on massive amounts of data that can be adapted to perform various tasks. It’s like a versatile tool that can be specialized for different jobs.

Forward Propagation

This term refers to the process of passing data through a neural network to generate an output. It is like feeding information into a machine and getting a result.

Feature Extraction

Feature extraction is the process of identifying and selecting relevant information from data. It allows us to find the most critical details in a puzzle.

Fine-tuning

It is a process of taking a pre-trained machine learning model and improving its functionalities for a specific task-based function. For this, a separate dataset is also provided to the ML model, which allows it to perform those tasks efficiently.

Few-Shot Learning

It is a terminology used to describe a machine-learning approach in which models can learn with a few labeled examples, sometimes even less than 5 for each category.

Few-Shot Learning

Federated Learning is a way to train AI models without sharing data. Instead of sending data to a central server, models are trained locally on individual devices, and their updates are shared to improve the global model.

G

Generative Pre-trained Transformer (GPT)

This term refers to a powerful AI model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

GPT-3

A specific version of the Generative Pre-Trained Transformer model is known for its ability to generate highly coherent and detailed text.

GPT-4

The latest version of GPT offers improved performance on various tasks, including a better understanding of context and nuances.

Generative Adversarial Networks (GANs)

A type of AI system involves two competing neural networks: The generator generates new data instances, and another discriminator tries to distinguish between accurate and generated data.

Gradient Descent

An optimization algorithm is used to minimize a function by iteratively moving in the direction of the steepest descent. It’s commonly used to train machine learning models.

Generalized Model

A machine learning model that can be applied to a wide range of tasks or datasets without significant modifications.

Generation

The process of creating new content, such as text, images, or music, using AI algorithms.

Generative AI

A type of AI that can create new content, such as text, images, or music, rather than simply analyzing existing data.

Grounding

Connecting AI models to real-world data and concepts to improve their understanding and performance.

Guardrails

This term refers to safety measures and guidelines implemented to control AI models’ behavior and prevent harmful or unintended outputs.

H

Heuristic Algorithms

It is a terminology used for finding solutions to complex and tedious problems when conventional methods become too slow to deal with a specific type of problem.

Hallucination in AI

Hallucination in AI occurs when an AI model generates incorrect or misleading information—it’s like the AI is making up things. This can happen due to limitations in the training data or the model’s architecture.

Hyperparameter Tuning

Hyperparameter tuning involves adjusting the parameters of a machine-learning model to optimize its performance. This type of tuning can achieve maximum productivity.

Hybrid AI

It is a way of combining multiple AI techniques to solve complex problems. It’s like using different tools to build something better, such as combining rule-based systems with machine learning.

Hyper-heuristic

It is a heuristic search method that uses more straightforward rules and machine learning to solve complex problems efficiently.

I

Image Recognition

This term refers to the process by which a computer system identifies and classifies objects, people, or other elements within a digital image.

Instruction-tuning

It is a technique for enhancing AI models by providing specific instructions to improve their responses to user queries or tasks. This tuning enhances a machine learning system’s capabilities and efficiency.

Intelligence Augmentation (IA)

This term refers to enhancing human intelligence and decision-making capabilities through the use of technology and AI systems.

Interpretability

It is the ability to understand and explain how an AI model makes its decisions or predictions.

Inference

Inferences are the process in AI where a trained model makes predictions or decisions based on new data.

Intelligent Document Processing (IDP)

This term refers to using AI to automatically extract and process information from documents, making it easier to handle and analyze large volumes of data.

Instruction Tuning

It is the process of refining AI models using detailed guidelines to ensure they provide accurate and relevant responses to specific queries.

Intelligence Amplification

The use of AI tools to enhance and extend human cognitive abilities, making it easier to process and analyze complex information, is called intelligence amplification.

J

Junction Tree Algorithm

The Junction Tree Algorithm is a method used in computer science and artificial intelligence to perform probabilistic reasoning efficiently.

K

Knowledge Generation

This term refers to training a language model on extensively large datasets, which allows them to answer queries more effectively.

K-Shot Learning

The term K-Shot Learning refers to a machine learning approach where the AI models learn from only K-labeled examples per class. The K represents the small numbers from 1 to 5.

Knowledge Engineering

Knowledge engineering is a method of helping computer programs replicate human-like knowledge. In this method, knowledge engineers build logic in knowledge-based systems by acquiring, modeling, and integrating general knowledge into a model. 

Knowledge Graph

A Knowledge Graph is a structured representation of information where relationships connect entities. This allows AI to quickly retrieve and analyze how different pieces of information are related.

Knowledge Model

A Knowledge Model is a framework or structure that defines how knowledge is represented, organized, and used within a system. It includes the rules and relationships that govern how different pieces of information interact.

Knowledge-Based AI

The term Knowledge-based AI refers to artificial intelligence systems that use stored knowledge to make decisions or solve problems. These systems rely on databases of facts, rules, and heuristics to understand and process information like human experts.

L

Large Language Model (LLM)

This term refers to a type of AI that understands and generates text by being trained on massive amounts of data. It helps to predict the next word in a sentence or answer questions.

Latency

The term Latency calculates the delay or wait time between when you give a command and when the system responds. In AI, it refers to how long it takes for an AI to process a task and give you the result.

Linear Regression

Linear regression is a supervised machine learning method used by the Train Using AutoML tool to find the best straight-line equation. It explains the relationship between input variables and the result we want to predict by drawing a line that fits the data as closely as possible using a method called least squares.

Latent Space

It is a hidden space in AI where patterns and relationships are stored in a compressed form. The Latent Space is like a map that helps AI recognize different things, like distinguishing between cats and dogs based on learned patterns.

LangOps (Language Operations)

This terminology refers to the tools and methods used to manage and improve AI’s work with language. It helps language models better understand, process, and generate text.

Language Data

The Language Data consists of all the words, sentences, and text that AI learns from. This data helps AI models understand how people use language and improve their responses.

Loss Function / Cost Function

This term refers to a mathematical tool that tells an AI how far off its guesses are. The AI uses this to learn and get better by minimizing the mistakes it makes.

Limited Memory

It is a type of AI that remembers past actions and uses them for a short time to make decisions, but it doesn’t store long-term memories like humans do.

Lemma

The term Lemma refers to Lemmatization, a basic form of a word. For example, “run” is the lemma, while “running” or “ran” are variations of Lemma.

Lexicon

Lexicon is a term used to describe the artificial intelligence technique that optimizes language models to perform specific tasks. It is a list or collection of words and their meanings, like a dictionary, that an AI might use to understand language.

Logistic Regression

It is a type of math that helps AI decide between two things, like yes/no or true/false. It is often used for classifying information, such as predicting whether an email is spam or not.

M

Machine Learning (ML)

It is a method in which a computer learns to improve its performance using data and experience without being programmed explicitly for every task. In that method, the computer studies sample data and then makes predictions or decisions.

Metacontext and Metaprompt

This term refers to the basic guidelines that tell the AI model how to behave and respond during training.

Metadata

It is the information that explains or gives details about other data to AI, like labels on a file.

Model

The term Mode refers to the results of training a machine learning algorithm with data. The computer uses it to analyze new data and make predictions.

Model Drift

The process when a machine learning model’s accuracy decreases over time because the environment or data it was trained on has changed is called Model Drift.

Model Parameter

The term Mode Parameter is the set of values inside the model that are used during training and help the model make predictions. These parameters shape how well the model fits the data and performs its task.

Morphological Analysis

It is a process of breaking down a complex problem into its simplest parts to understand it better. This technique is often used in problem-solving and language analysis.

Multimodal Models and Modalities

This term refers to the set of AI models trained to understand and process different types of data, like text, images, and audio, so that they can be more effective in various tasks.

Multitask Prompt Tuning (MPT)

It is a method of setting up prompts that allow an AI model to handle similar tasks repeatedly, with minor variations between them.

N

N-Shot Learning

The term N-Shot is a learning method where the AI model is given only a small number of training examples and refers to shots to help it understand and classify new data. The “N” refers to the number of examples provided to improve the accuracy of an AI model.

Natural Language Ambiguity

This term refers to words, phrases, or sentences that have more than one meaning. They can create ambiguity when trying to figure out the correct interpretation.

Natural Language Generation (NLG)

Natural Language Generation is a part of AI that helps create natural-sounding text or speech from data, making it easier for people to read or listen to.

Natural Language Processing (NLP)

NLP is a branch of AI that teaches computers to effectively understand and process large amounts of human language data, such as text and speech.

Natural Language Understanding (NLU)

Natural Language Understanding is a part of NLP that focuses on helping computers understand the deeper meaning of language, such as context, intent, and emotions.

No-code

It is an approach to building applications without writing code, so users can create software without needing programming skills.

Neural Network

The term Neural Networks is an AI model inspired by the brain, which is made up of connected “neurons” that process data in layers to make predictions, like recognizing images or speech.

NeRF (Neural Radiance Fields)

The term NeRF refers to a technique in which neural networks are used to create 3D scenes from 2D images, which can be used for realistic visual effects or animations.

Natural Language Query (NLQ)

This term refers to a way of asking a computer question in normal spoken or written language without using special symbols or technical characters.

Natural Language Technology (NLT)

Natural Language Technology (NLT) is a field that combines linguistics, computer science, and AI to develop systems that can process, understand, and generate human language. It includes NLP, NLU, and NLG.

O

OpenAI

Open IA is a research company that created ChatGPT. It focuses on developing safe and helpful AI technologies, like GPT-3, a powerful language model for understanding and generating human language.

Optimization

Optimization is the process of fine-tuning a machine learning model to make its predictions as accurate as possible. In AI models, optimization is achieved by minimizing the difference between what the model predicts and the actual correct answers.

Overfitting

Sometimes, AI machine learning models need help to work with new data and perform poorly outside the training examples. This whole scenario is called overfitting. It happens when a machine learning model becomes too specific to the training data, learning it too well.

Objective Function

The term Objective Function is a mathematical formula that a machine learning model tries to either maximize or minimize during training to improve its performance.

Ontology

An ontology is a system for organizing information that not only classifies items into categories but also adds extra details and connections between them. This creates a more complex and meaningful structure than a simple list or hierarchy.

P

Parameter-Efficient Fine-Tuning (PEFT)

PEFT is a method to improve large AI models without using too much time or energy. This method focuses on tweaking only a few critical parts of the model while keeping most of it the same.

Pre-training

It is the first step in training an AI model, which learns from a large set of general data before being fine-tuned for a specific task, like translating languages.

Prompt Engineering

The process of crafting the right input (or “prompt”) to get the best results from an AI model. It’s like finding the perfect way to ask a question so the AI gives you the most useful answer.

Probabilistic Model

A type of AI model that makes decisions based on the likelihood of different outcomes happening rather than being certain about one specific result.

Parameters

The parameters are the internal settings of a machine learning model used to make predictions. They learn from the training data and are adjusted to improve accuracy.

Pattern Recognition

This term refers to the process of teaching computers to find regular patterns or trends in data, which helps them classify that data into categories.

Predictive Analytics

It is a technique that uses past data to predict the future, such as forecasting sales based on previous years’ trends.

Prescriptive Analytics

Prescriptive Analytics refers to a type of analysis that helps organizations make better decisions by considering different possibilities and resources and recommending the best course of action.

Prompt

A prompt is a set of instructions or queries that users give to an AI system to get a response.

Parsing

This terminology refers to the process of breaking down text into its parts and understanding the role each part plays in the sentence, like identifying nouns, verbs, and grammar.

Part-of-Speech Tagging

Part-of-speech tagging is a function in natural language processing (NLP) that labels each word in a sentence with its grammatical role, such as whether it’s a noun, verb, or adjective.

Post-Edit Machine Translation (PEMT)

PEMT is a process where a translator corrects a document that a machine has already translated. This is usually done sentence by sentence using special software.

Plugins

Plugins are the additional software tools that extend the abilities of AI models. They allow them to perform tasks like booking travel, browsing the web, or doing complex math more effectively.

Post-processing

Post-processing term refers to the steps taken after AI makes predictions to clean up or filter the results. This processing makes the AI models more accurate or useful.

Pre-processing

Pre-processing refers to the step taken before analyzing data, where raw information is cleaned and organized into a format that computers can easily understand and work with.

Pretraining

Pretraining is the first phase of training a model. It involves learning basic skills from general data before it receives specialized training for specific tasks.

Precision

Precision measures the accuracy of an AI system’s results. For example, if an AI system identifies five dog breeds correctly out of five attempts, its precision is 100%.

Pretrained Model

The term pre-trained model refers to a model that has already been trained on a general task and can be used as a starting point to adapt to more specific tasks through fine-tuning.

Prompt Chaining

Prompt Chaining term refers to the use of a series of prompts to gradually refine an AI model’s response and improve the accuracy or depth of the answers.

Q

Quantum Computing

Quantum computing uses special properties of tiny particles, like entanglement and superposition, to do calculations differently than regular computers. Quantum machine learning takes advantage of these quantum computers to make machine learning tasks much faster than traditional computers.

R

Random Forest

Random Forest is a machine learning method that combines multiple decision trees to make better predictions. It can be used to classify data or predict numbers, often in programming languages like Python and R.

Recall

Recall measures how many correct results a system finds out of all the possible correct results. For example, if a system is supposed to find ten cat breeds in a document but only identifies 5, its recall is 50%.

Recurrent Neural Networks (RNN)

RNNs refer to a type of neural network that remembers previous information and uses it to help understand new inputs. These neural networks are often used for tasks like language processing and speech recognition.

Reinforcement Learning

Reinforcement learning refers to a method where a computer learns to make decisions by trying different actions and being rewarded or penalized based on whether the action was good or bad.

Reinforcement Learning with Human Feedback (RLHF)

It is a training method where an AI model improves by learning from human feedback, which helps it to understand the right actions by observing human responses to its behavior.

Relations

In Natural Language Processing, relations refers to how different parts of a sentence are connected. For example, in the sentence “James is Lilly’s father,” the relation is the family connection between John and Mary.

Retrieval Augmented Generation (RAG)

RAG is a technique that combines AI-generated responses with information from external, trusted sources to improve accuracy and provide reliable facts. Retrieval-augmented generation increases the efficiency of large language models and makes them more reliable.

Return on Artificial Intelligence (ROAI)

ROAI, aka return on artificial intelligence, measures the success or value of investing in AI. It is similar to how ROI (Return on Investment) evaluates financial returns.

Rules-based Machine Translation (RBMT)

RBMT is an older method of machine translation that uses predefined language rules to translate text. It understands how words change meaning based on context.

Robotics

The term robotics refers to integrating AI programs with robots that increase their capabilities and achieve a higher level of efficiency and productivity.

Regularization

Regularization is a machine learning technique that prevents overfitting by simplifying the model. This technique adds a penalty for making the model too complex, which helps an AI model work better on new data.

Reasoning

Reasoning in AI occurs when AI systems solve problems, think critically, and make decisions by analyzing the information they have.

Recursive Prompting

This terminology refers to a method in which AI is guided by a series of prompts or questions that build on previous responses, refining its understanding and improving the quality of the final output.

S

Supervised Learning

A supervised learning is a type of machine learning where the model is trained on labeled data, meaning the input data is paired with the correct output. With this approach the AI model learns to predict the output from new inputs.

Sequence Modeling

A supervised learning is a type of machine learning where the model is trained on labeled data, meaning the input data is paired with the correct output. With this approach the AI model learns to predict the output from new inputs.

Speech-to-text

Speech-to-text is a technology that converts spoken language into written text by analyzing and interpreting audio recordings.

Symbolic Artificial Intelligence

Symbolic artificial intelligence is a branch of AI that uses precise, human-readable symbols and rules to represent knowledge and solve problems, often through logic and reasoning.

Stable Diffusion

Stable Diffusion is an advanced AI method used to generate images or other media by continuously refining noisy data in prompts into a clearer form. This AI technology is commonly used in generative AI models.

Sentiment Analysis

The term Sentiment Analytics refers to the process of using AI to analyze and determine the emotional tone (positive, negative, neutral) behind a piece of text. This mechanism gives insights of a textual content and distinguishes whether the content is a product review or social media post.

Steerability

It is the capability to adjust or control the behavior or output of an AI model, often in a guided or targeted manner.

Syntax

The set of rules that govern the structure of sentences or expressions in a language determines how words and symbols are organized.

Self-attention Mechanism

This term refers to a component in neural networks, especially in transformers, that allows the AI model to consider the importance of different words in a sentence when making predictions. While making predictions, it focuses on relevant parts of the input.

Strong AI

The term Strong AI refers to a hypothetical form of artificial intelligence that possesses general intelligence. It means that the Strong AI can understand, learn, and apply knowledge across a wide range of tasks, much like a human.

Semantic Search

It is a search technique that understands the meaning behind words and phrases to provide more accurate and relevant search results beyond simple keyword matching.

Structured Data

The term Structured Data refers to the data that is organized in a predefined format, such as rows and columns in a spreadsheet or database, making it easy to store, search, and analyze.

Summarization

It is the process of automatically condensing a large body of text into a shorter version while retaining the most important information.

Symbolic AI

Symbolic AI is a type of artificial intelligence that uses symbols and logical rules to represent knowledge and perform reasoning, typically involving human-understandable concepts.

Symbolic Methodology

The term Symbolic Methodology refers to an approach in AI where problems are solved using symbolic representations (like logic, rules, and symbols) rather than through statistical methods.

Speech Analytics

It is the use of AI to analyze speech recordings, extracting insights such as emotional tone, key themes, or the speaker’s intent from the audio.

Semantics

Semantics is the study of meaning in language, focusing on how words, phrases, and sentences convey meaning.

Semi-structured Data

A form of data that does not follow a strict structure but has some organizational properties, such as tags or markers, which makes it easier to analyze than unstructured data.

Speech Recognition

This terminology refers to the technology that converts spoken language into text by identifying and transcribing the words spoken in an audio recording.

Structured Data

Structured data is data organized into a clear, predefined format, often in databases or tables, making it easy to search, analyze, and manage.

Sentiment Analysis

Sentiment analysis is a process of using AI to identify and classify the emotional tone of a piece of text. Such kind of analysis is used to determine whether a review is positive, negative, or neutral.

Supervised Learning

Supervised Learning is a type of machine learning where the AI model is trained on labeled data, meaning the input data comes with the correct output, and the model learns to predict the output from new inputs.

Stochastic Parrot

A term used to describe large language models that generate text by mimicking patterns in their training data without true understanding, often producing fluent but contextually shallow responses.

Similarity

Similarity is the measure of how alike two objects are, often used to compare features or data points.

Semantic Network (SN)

Semantic Networks, or SN, refer to a knowledge representation structure that shows concepts and their relationships. These networks are often used to represent meaning and associations in AI systems.

Specialized Corpora

This term refers to collections of texts or data sets that are specifically designed or compiled for a particular subject area, domain, or language use. These are often used in training AI models for specialized tasks.

Singularity

Singularity is a hypothetical future point when artificial intelligence surpasses human intelligence, leading to rapid technological advancements that could be unpredictable or uncontrollable.

Simple Knowledge Organization System (SKOS)

This term refers to a standardized framework for organizing and sharing knowledge using concepts, labels, and relationships. It is often used to structure information in digital libraries and the Semantic Web.

T

Transformer

Transformer is a type of neural network architecture used in AI that processes and understands sequences of data. They focus on the most relevant parts of the text input or prompt.

TensorFlow

This term refers to an open-source machine learning framework created by Google that helps developers build and train AI models, particularly for deep learning tasks.

Token

The token is a piece of a larger text, such as a word, punctuation mark, or symbol, used in natural language processing to break down and analyze text data.

Turing test

The Turning Test, also known as the Turing Test, was proposed by Alan Turing to assess a machine’s capability to exhibit intelligent behavior distinguishable from that of a human during a conversation.

Tagging

Tagging refers to the process of labeling or annotating data, such as words in a text, with categories or identifiers (e.g., parts of speech or named entities) to make it easier for AI models to analyze.

Tunable

This term refers to parameters or settings in a machine learning model that can be adjusted or fine-tuned to improve the model’s performance.

Triple or Triplet Relations (Subject Action Object (SAO))

It is a way of representing knowledge where information is broken down into three parts: a subject (who/what is doing something), an action (what they are doing), and an object (what is being acted upon).

Treemap

This term refers to a visualization tool that displays hierarchical data as a set of nested rectangles, where the size and color of each rectangle represent the data’s importance or value.

Tensor Processing Unit (TPU)

TPU is a specialized hardware chip designed by Google to accelerate machine learning tasks, particularly for deep learning models, by efficiently processing tensors.

Text-to-speech

It refers to a technology that converts written text into spoken words by synthesizing audio that sounds like human speech.

Training Data

The labeled data used to train a machine learning model, helping the model learn patterns and relationships to make predictions on new, refers to Training Data.

Tokenization

Tokenization is a process of breaking down a text into smaller pieces, such as words, phrases, or symbols, to make it easier for a machine to understand and analyze.

Transfer Learning

It is a technique in machine learning in which an AI model trained on one task is reused or adapted to perform a different but related task. This type of learning saves time and improves the overall performance of an AI model.

Training Set

The term Training Set refers to a subset of labeled data used specifically to train a machine learning model. It helps the AI models to learn patterns and relationships before being tested on new data.

U

Unsupervised Learning

It is a type of machine learning in which an AI model is trained on data without labeled answers. The model tries to find hidden patterns or groupings within the data on its own.

Underfitting

This term refers to an AI model’s poor performance. It happens when a machine learning model is too simple and fails to learn the patterns in the data, leading to poor performance on both the training data and new data.

Unstructured Data

It is a type of data that doesn’t have a predefined format or organization, like text, images, or videos, making it harder to analyze compared to structured data, like tables.

V

Virtual Assistant

This term refers to a software program that uses AI to perform tasks or services for a user, such as answering questions, setting reminders, or controlling smart devices, often through voice or text interactions (e.g., Siri, Alexa).

Voice Processing

Voice Processing refers to the technology used to analyze, interpret, and manipulate voice signals, such as converting spoken language into text or recognizing speaker identity.

Validation Data

It is a separate set of labeled data used during training to evaluate and adjust a machine learning model’s performance. This data ensures that the model generalizes well to new, unseen data.

Voice Recognition

It is a separate set of labeled data used during training to evaluate and adjust a machine learning model’s performance. This data ensures that the model generalizes well to new, unseen data.

Vision Processing Unit (VPU)

A VPU is a specialized processor designed to accelerate computer vision tasks, such as recognizing objects in images or videos, by efficiently handling the data required for these tasks.

W

Weak AI

Weak AI, also known as narrow AI, refers to artificial intelligence that is designed and trained to perform a specific task or a limited set of tasks. Unlike strong AI, weak AI does not possess general intelligence or understanding beyond its designated function.

X

Explainable AI (XAI)

It is a branch of artificial intelligence that focuses on creating models whose decisions and behaviors can be understood and interpreted by humans. XAI models aim to make AI systems more transparent by providing explanations for how they reach their conclusions.
This helps users to trust and understand the reasoning behind AI outputs.

Y

Yield Management in AI

In AI, yield management systems use algorithms and machine learning models to analyze large amounts of data (such as demand, booking patterns, and competition) to adjust prices dynamically in real-time. AI-powered yield management systems help businesses make smarter pricing decisions, predict customer behavior, and allocate resources more effectively to maximize profitability.

Z

Zero-Shot Learning

This term refers to a machine learning technique where a model is able to recognize and classify objects or categories that it has never been explicitly trained on by using knowledge transferred from related tasks or features.

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