130 AI Glossary(2024)

Artificial intelligence (AI) and natural language (NL) technologies play a vital role in enterprise businesses. However, due to their intricate nature, assessing these technologies can be challenging for many individuals. Nonetheless, it is crucial that everyone is included in this important conversation. To facilitate a better understanding, we have curated a comprehensive glossary of AI and NL terms, aiming to simplify the discourse.

The following glossary comprises essential words and phrases that will enhance your understanding of natural language and artificial intelligence technologies. Armed with this knowledge, you will be able to confidently navigate your journey towards adopting and implementing natural language processing and natural language understanding solutions within your enterprise organization.

1.Application Programming Interface(API)

An API, or application programming interface, is a set of rules and protocols that allows different software programs to communicate and exchange information with each other. It acts as a kind of intermediary, enabling different programs to interact and work together, even if they are not built using the same programming languages or technologies. API’s provide a way for different software programs to talk to each other and share data, helping to create a more interconnected and seamless user experience.

2.Artificial Intelligence(AI)

the intelligence displayed by machines in performing tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and language understanding. AI is achieved by developing algorithms and systems that can process, analyze, and understand large amounts of data and make decisions based on that data.

3.Compute Unified Device Architecture(CUDA)

CUDA is a way that computers can work on really hard and big problems by breaking them down into smaller pieces and solving them all at the same time. It helps the computer work faster and better by using special parts inside it called GPUs. It’s like when you have lots of friends help you do a puzzle – it goes much faster than if you try to do it all by yourself.

The term “CUDA” is a trademark of NVIDIA Corporation, which developed and popularized the technology.

4.Data Processing

The process of preparing raw data for use in a machine learning model, including tasks such as cleaning, transforming, and normalizing the data.

5.Deep Learning(DL)

A subfield of machine learning that uses deep neural networks with many layers to learn complex patterns from data.

6.Embedding

When we want a computer to understand language, we need to represent the words as numbers because computers can only understand numbers. An embedding is a way of doing that. Here’s how it works: we take a word, like “cat”, and convert it into a numerical representation that captures its meaning. We do this by using a special algorithm that looks at the word in the context of other words around it. The resulting number represents the word’s meaning and can be used by the computer to understand what the word means and how it relates to other words. For example, the word “kitten” might have a similar embedding to “cat” because they are related in meaning. Similarly, the word “dog” might have a different embedding than “cat” because they have different meanings. This allows the computer to understand relationships between words and make sense of language.

7.Machine Learning (ML)

A subset of AI that focuses on enabling computers to learn and make predictions or decisions without being explicitly programmed.

8.Deep Learning

A branch of ML that utilizes artificial neural networks with multiple layers to learn hierarchical representations of data, enabling complex pattern recognition and decision-making.

9.Supervised Learning

ML technique in which a model learns from labeled examples, where each example is paired with a corresponding target or label.

10.Unsupervised Learning

ML technique in which a model learns from unlabeled data, finding patterns, and relationships without specific target labels.

11.Reinforcement Learning

ML technique in which an agent learns through interactions with an environment, receiving rewards or penalties based on its actions.

12.Neural Network

A network of interconnected artificial neurons or nodes that process and transmit information. It mimics the structure and function of the human brain.

13.Deep Neural Network

A neural network with multiple hidden layers, allowing for deep learning and complex feature representation.

14.Overfitting

A phenomenon where a ML model performs well on the training data but fails to generalize to unseen data due to excessive complexity or noise fitting.

15.Underfitting

A phenomenon where a ML model is too simple to capture the underlying patterns in the data, leading to poor performance on both training and test data.

16.Feature Extraction

The process of transforming raw input data into a set of meaningful and representative features that can be used for ML tasks.

17.Training Set

The “training set” in AI and machine learning is a subset of data used to train a model. It plays a vital role in model development by providing examples and patterns for learning and prediction. During training, the model adjusts its internal parameters based on the input and expected output from the training set.

The training set should represent real-world data, covering diverse examples and variations. Its size depends on the problem complexity, with larger and more diverse sets generally leading to better performance.

The training set is distinct from other datasets. The validation set evaluates the model during training for adjustments and hyperparameter tuning. The test set assesses the final model’s performance on unseen data.

By using an appropriate training set and following best practices, models learn from the data and make accurate predictions on new data. The quality and representativeness of the training set greatly impact model performance and generalization. Thus, it is a critical component in AI and machine learning workflows.

18.Test Set

In the field of AI and machine learning, the “test set” refers to a separate subset of data that is used to evaluate the performance of a trained model. It serves as a benchmark to assess how well the model generalizes to unseen examples.

The test set is crucial because it provides an unbiased evaluation of the model’s performance on new data. It should be carefully constructed to represent the same distribution as the real-world data that the model will encounter in practice.

Unlike the training set, the test set should not be used for training or adjusting the model. It should remain untouched until the model is fully trained. By evaluating the model on the test set, researchers and practitioners can estimate its effectiveness in making accurate predictions and measure its overall performance metrics, such as accuracy, precision, recall, or F1 score.

Maintaining a separate test set helps prevent overfitting, where the model becomes too specialized in the training data and performs poorly on new examples. Thus, the test set plays a critical role in assessing the model’s generalization capability and determining its readiness for deployment.

19.Validation Set

The “validation set” is a subset of data that is used to fine-tune and evaluate a model during the training process. It helps assess the model’s performance and aids in selecting the best hyperparameters or adjusting the model’s architecture. The validation set is distinct from the training and test sets. It allows researchers to iteratively adjust the model based on its performance on unseen data without overfitting to the test set. By using the validation set, they can make informed decisions regarding the model’s configuration, such as choosing the optimal number of layers, adjusting learning rates, or selecting regularization techniques.

20.Loss Function

A measure that quantifies the difference between the predicted output of a ML model and the actual target values, used to guide the learning process.

21.Gradient Descent

An optimization algorithm used to update the parameters of a ML model iteratively by descending the gradient of the loss function.

22.Bias-Variance Tradeoff

The delicate balance between a ML model’s ability to capture complex patterns (low bias) and its susceptibility to noise and variability (high variance).

23.Ensemble Learning

A technique that combines multiple ML models to improve performance and generalization by leveraging the diversity of predictions.

24Convolutional Neural Network (CNN)

A specialized type of neural network designed for processing grid-like data, such as images or sequential data.

25.Natural Language Processing (NLP)

The field of AI that focuses on the interaction between computers and human language, enabling tasks such as language translation and sentiment analysis.

26.Data Science

An interdisciplinary field that combines scientific methods, statistics, and ML techniques to extract insights and knowledge from data.

27.Big Data

Extremely large and complex datasets that require specialized tools and techniques for storage, processing, and analysis.

28.Data Preprocessing

The initial step in data preparation, involving cleaning, transformation, and normalization of raw data to make it suitable for ML algorithms.

29.Feature Engineering

The process of creating new features or selecting and transforming existing features to enhance the performance of ML models.

30.Hyperparameters

Parameters that are set before the learning process and affect the behavior and performance of ML models, such as learning rate and regularization strength.

31.Cross-Validation

A technique used to assess the performance of ML models by partitioning the data into multiple subsets for training and evaluation, allowing for more robust model assessment.

32.Bias

The systematic error introduced by a ML model when it consistently predicts values that are different from the true values.

33.Variance

The amount of fluctuation or inconsistency in the predictions of a ML model when trained on different datasets.

34.Precision

A metric that measures the proportion of true positive predictions out of all positive predictions made by a ML model.

35.Recall

A metric that measures the proportion of true positive predictions out of all actual positive instances in the data.

36.F1 Score

A metric that combines precision and recall to provide a single measure of a model’s accuracy, particularly useful when dealing with imbalanced datasets.

37.Accuracy

A metric that measures the proportion of correct predictions made by a ML model out of all predictions.

38.ROC Curve

A graphical representation of the performance of a ML model by plotting the true positive rate against the false positive rate at various classification thresholds.

39.AUC-ROC

The Area Under the ROC Curve, which quantifies the overall performance of a ML model across all possible classification thresholds.

40.Regression

A ML task that involves predicting continuous numerical values, such as predicting house prices based on features like area and location.

41.Classification

A ML task that involves assigning predefined categories or labels to input data, such as classifying emails as spam or non-spam.

42.Clustering

A ML task that involves grouping similar data points together based on their inherent patterns or similarities, without predefined categories.

43.Dimensionality Reduction

The process of reducing the number of input features or variables while retaining the most important information, often used to alleviate the curse of dimensionality.

44.Transfer Learning

A technique in which a pre-trained ML model is used as a starting point for a new task, leveraging the learned representations and knowledge from the previous task.

45.Neural Architecture Search (NAS)

The process of automating the design and optimization of neural network architectures to achieve better performance on specific tasks.

46.Style Generative Adversarial Networks (StyleGAN)

StyleGAN is an extension of GANs that focuses on controlling the style and attributes of generated images. It allows for fine-grained manipulation of image features, such as facial expressions, age, and hair color, by modifying latent variables or style vectors.

47.Explainable AI

The field of AI that aims to develop methods and techniques to make the decision-making process of ML models transparent and understandable to humans.

48.Bias in AI

The unfair and discriminatory behavior exhibited by AI systems when they reflect the biases present in the data or the algorithms used to train them.

49.Ethics in AI

The study and practice of ensuring that AI systems are developed and deployed in a responsible and ethical manner, considering potential social impacts and biases.

50.Artificial General Intelligence (AGI)

AI systems that possess the ability to understand, learn, and apply knowledge across a wide range of tasks and domains, similar to human intelligence.

51.Explainability-Interpretability Tradeoff

The tradeoff between the complexity and interpretability of ML models, where more complex models may provide better performance but are harder to interpret.

52.One-shot Learning

A learning paradigm where a ML model is trained to recognize or classify new instances based on just a single example or a few examples.

53.Data Augmentation

The technique of generating additional training data by applying transformations or perturbations to the existing data, increasing the diversity of the training set.

54.Bias-Variance Decomposition

The decomposition of the total error of a ML model into bias and variance components, providing insights into the model’s performance.

55.Dropout

A regularization technique in neural networks that randomly deactivates a fraction of neurons during training, reducing overfitting and improving generalization.

56.Activation Function

A function applied to the output of a neuron or a layer in a neural network, introducing non-linearity and enabling the network to learn complex relationships in the data.

57.Backpropagation

An algorithm used to train neural networks by propagating the errors backward from the output layer to the input layer, adjusting the model’s weights accordingly.

58.Gradient Vanishing/Exploding

The phenomenon where the gradients in deep neural networks become extremely small (vanishing) or excessively large (exploding) during training, hindering learning.

59.Regularization

Techniques used to prevent overfitting by introducing additional constraints or penalties on the model’s parameters, such as L1 or L2 regularization.

60.L1 Regularization (Lasso)

A regularization technique that adds the sum of the absolute values of the model’s coefficients as a penalty term to the loss function.

61.L2 Regularization (Ridge)

A regularization technique that adds the sum of the squared values of the model’s coefficients as a penalty term to the loss function.

62.DeepFake

DeepFake technology gained significant attention for its ability to generate realistic and deceptive video content by swapping faces or manipulating facial expressions. It uses deep learning techniques, typically based on GANs or autoencoders, to create convincing visual illusions.

63.Dropout Regularization

A regularization technique that randomly drops out a fraction of neurons during training, reducing interdependencies and forcing the network to learn more robust representations.

64.Batch Normalization

A technique that normalizes the inputs of each layer in a neural network by standardizing them to have zero mean and unit variance, improving the training process.

65.Learning Rate

A hyperparameter that determines the step size or rate at which a ML model learns during training, influencing the convergence and optimization process.

66.Convolution

A mathematical operation in which a function is applied to a subset of the input data, allowing convolutional neural networks to extract local features and patterns.

67.Pooling

A downsampling operation in which the input data is divided into non-overlapping regions, reducing the spatial dimensions and extracting the most salient features.

68.Recurrent Neural Network (RNN)

A type of neural network designed to process sequential data by incorporating feedback connections, enabling the network to retain memory of past information.

69.Long Short-Term Memory (LSTM)

A variant of RNNs that addresses the vanishing gradient problem by introducing memory cells and gates to selectively remember or forget information.

70.Attention Mechanism

A technique used in neural networks to focus on specific parts or features of the input sequence, enabling better performance in tasks such as machine translation and image captioning.

71.Word Embedding

A technique that represents words or phrases as dense vector representations in a continuous space, capturing semantic relationships and improving NLP tasks.

72.Autoencoder

A neural network architecture designed to learn efficient representations of data by training an encoder and decoder network to reconstruct the input data.

73.Generative Models

ML models that learn the underlying distribution of the input data and can generate new samples that resemble the original data distribution.

74.Supervised Fine-tuning

The process of taking a pre-trained model and further training it on a smaller labeled dataset to adapt it to a specific task or domain.

75.Semi-Supervised Learning

A learning paradigm that combines both labeled and unlabeled data to train ML models, leveraging the additional unlabeled data for improved performance.

76.Active Learning

A learning paradigm in which the ML model actively selects the most informative or uncertain instances from a pool of unlabeled data for annotation, reducing the annotation cost.

77.Outlier Detection

The task of identifying rare or anomalous instances in a dataset that deviate significantly from the normal patterns, often indicating errors or interesting phenomena.

78.Neural Style Transfer

A technique that combines the content of one image with the style of another image, creating artistic and visually appealing results.

79.Hyperparameter Optimization

The process of searching for the best combination of hyperparameters for a ML model, often using techniques like grid search, random search, or more advanced methods such as Bayesian optimization or genetic algorithms.

80.Ensemble Methods

Techniques that combine the predictions of multiple ML models to improve overall performance and robustness, such as bagging, boosting, and stacking.

81.Bagging

A technique in which multiple ML models are trained on different subsets of the training data, and their predictions are combined through averaging or voting.

82.Boosting

A technique in which multiple ML models are trained sequentially, with each model giving more weight to the instances that were misclassified by the previous models.

83.Stacking

A technique that combines predictions from multiple base models by training a meta-model on their outputs, allowing for higher-level learning and improved performance.

84.Bias in Data

The presence of systematic errors or prejudices in the training data, which can lead to biased predictions and unfair outcomes in ML models.

85.Fairness in AI

The principle of ensuring that AI systems treat individuals or groups fairly and without discrimination, mitigating biases and promoting equitable outcomes.

86.Privacy-Preserving ML

Techniques that allow ML models to be trained and utilized without revealing sensitive or personally identifiable information in the data.

87.Semi-Structured Data

Data that has a predefined structure but contains some unstructured or variable components, such as JSON or XML files.

88.Reinforcement Learning Agent

The entity that interacts with an environment in reinforcement learning, learning to take actions that maximize cumulative rewards.

89.Policy

In reinforcement learning, a policy defines the strategy or set of rules that an agent follows to select actions based on the observed state.

90.Q-Learning

A model-free reinforcement learning algorithm that learns an action-value function to estimate the expected return from taking a specific action in a given state.

91.Markov Decision Process (MDP)

A mathematical framework used to model sequential decision-making problems in reinforcement learning, based on the principles of Markov chains.

92.Exploration-Exploitation Tradeoff

The balance between exploring the environment to discover new actions with uncertain outcomes and exploiting known actions with higher expected rewards.

93.Inference

The process of making predictions or drawing conclusions based on the learned parameters and structure of a ML model.

94.Deployment

The stage in ML where a trained model is put into practical use in real-world applications, often involving integration into existing systems or platforms.

95.Edge Computing

The practice of processing and analyzing data locally on edge devices, such as sensors or smartphones, reducing the need for cloud-based computations.

96.Cloud Computing

The practice of using remote servers over the internet to store, manage, and process data, providing on-demand access to computing resources.

97.Internet of Things (IoT)

The network of physical devices embedded with sensors, software, and connectivity, enabling them to collect and exchange data.

98.Robotic Process Automation (RPA)

The use of software robots or “bots” to automate repetitive and rule-based tasks, mimicking human interactions with digital systems.

99.Natural Language Generation (NLG)

The process of generating human-like language or text based on given data or instructions, often used in chatbots or automated report generation.

100.Computer Vision

The field of AI that focuses on enabling computers to understand and interpret visual information from images or videos.

101.Object Detection

The task of identifying and localizing objects of interest within an image or video, often achieved using techniques like bounding box regression and image classification.

102.Image Segmentation

The process of partitioning an image into multiple segments or regions, assigning a label to each pixel to enable detailed analysis and understanding.

103.Neural Machine Translation (NMT)

The use of neural networks to translate text or speech from one language to another, achieving improved translation quality compared to traditional statistical machine translation methods.

104.Gated Recurrent Unit (GRU)

A type of recurrent neural network architecture that uses gating mechanisms to selectively update and propagate information, balancing memory and computation.

105.Transformer

A neural network architecture based on self-attention mechanisms, widely used in natural language processing tasks such as machine translation and text generation.

106.Synthetic Data

Artificially generated data that mimics the characteristics and patterns of real data, often used when real data is limited, sensitive, or expensive to acquire.

107.Feature Engineering

The process of selecting and creating new features from the raw data that can be used to improve the performance of a machine learning model.

108.Freemium

You might often see the term “Freemium” used on more site.Freemium, a portmanteau of the words “free” and “premium”, is a pricing strategy by which a basic product or service is provided free of charge, but money (a premium) is charged for additional features, services, or virtual (online) or physical (offline) goods that expand the functionality of the free version of the software.

109.Generative Adversarial Network(GAN)

A type of computer program that creates new things, such as images or music, by training two neural networks against each other. One network, called the generator, creates new data, while the other network, called the discriminator, checks the authenticity of the data. The generator learns to improve its data generation through feedback from the discriminator, which becomes better at identifying fake data. This back and forth process continues until the generator is able to create data that is almost impossible for the discriminator to tell apart from real data. GANs can be used for a variety of applications, including creating realistic images, videos, and music, removing noise from pictures and videos, and creating new styles of art.

110.Generative Art

Generative art is a form of art that is created using a computer program or algorithm to generate visual or audio output. It often involves the use of randomness or mathematical rules to create unique, unpredictable, and sometimes chaotic results.

111.Generative Pre-trained Transformer(GPT)

GPT stands for Generative Pretrained Transformer. It is a type of large language model developed by OpenAI.

112.Giant Language model Test Room(GLTR)

GLTR is a tool that helps people tell if a piece of text was written by a computer or a person. It does this by looking at how each word in the text is used and how likely it is that a computer would have chosen that word. GLTR is like a helper that shows you clues by coloring different parts of the sentence different colors. Green means the word is very likely to have been written by a person, yellow means it’s not sure, red means it’s more likely to have been written by a computer and violet means it’s very likely to have been written by a computer.

113.GitHub

GitHub is a platform for hosting and collaborating on software projects

114.Google’s Federated Learning of Cohorts (FLoC)

Google has been testing a new approach to online advertising that uses federated learning to group users into cohorts based on their browsing behavior, rather than tracking individuals’ behavior across the web. This approach aims to preserve users’ privacy while still allowing advertisers to reach their intended audience.

115.Google Colab

Google Colab is an online platform that allows users to share and run Python scripts in the cloud

116.Graphics Processing Unit(GPU)

A GPU, or graphics processing unit, is a special type of computer chip that is designed to handle the complex calculations needed to display images and video on a computer or other device. It’s like the brain of your computer’s graphics system, and it’s really good at doing lots of math really fast. GPUs are used in many different types of devices, including computers, phones, and gaming consoles. They are especially useful for tasks that require a lot of processing power, like playing video games, rendering 3D graphics, or running machine learning algorithms.

117.Langchain

LangChain is a library that helps users connect artificial intelligence models to external sources of information. The tool allows users to chain together commands or queries across different sources, enabling the creation of agents or chatbots that can perform actions on a user’s behalf. It aims to simplify the process of connecting AI models to external sources of information, enabling more complex and powerful applications of artificial intelligence.

118.Large Language Model(LLM)

A type of machine learning model that is trained on a very large amount of text data and is able to generate natural-sounding text.

119.Machine Learning(ML)

A method of teaching computers to learn from data, without being explicitly programmed.

120. WaveNet

WaveNet is a deep generative model for audio synthesis developed by DeepMind. It uses autoregressive neural networks to generate high-quality and realistic audio waveforms. WaveNet has been widely used for text-to-speech synthesis and music generation.

121.Neural Networks

A type of machine learning algorithm modeled on the structure and function of the brain.

122.Neural Radiance Fields(NeRF)

Neural Radiance Fields are a type of deep learning model that can be used for a variety of tasks, including image generation, object detection, and segmentation. NeRFs are inspired by the idea of using a neural network to model the radiance of an image, which is a measure of the amount of light that is emitted or reflected by an object.

123.OpenAI

OpenAI is a research institute focused on developing and promoting artificial intelligence technologies that are safe, transparent, and beneficial to society

124.Overfitting

A common problem in machine learning, in which the model performs well on the training data but poorly on new, unseen data. It occurs when the model is too complex and has learned too many details from the training data, so it doesn’t generalize well.

125.Prompt

A prompt is a piece of text that is used to prime a large language model and guide its generation

126.Python

Python is a popular, high-level programming language known for its simplicity, readability, and flexibility (many AI tools use it)

127.Reinforcement Learning

A type of machine learning in which the model learns by trial and error, receiving rewards or punishments for its actions and adjusting its behavior accordingly.

128.Spatial Computing

Spatial computing is the use of technology to add digital information and experiences to the physical world. This can include things like augmented reality, where digital information is added to what you see in the real world, or virtual reality, where you can fully immerse yourself in a digital environment. It has many different uses, such as in education, entertainment, and design, and can change how we interact with the world and with each other.

129.Stable Diffusion

Stable Diffusion generates complex artistic images based on text prompts. It’s an open source image synthesis AI model available to everyone. Stable Diffusion can be installed locally using code found on GitHub or there are several online user interfaces that also leverage Stable Diffusion models.

130.Supervised Learning

A type of machine learning in which the training data is labeled and the model is trained to make predictions based on the relationships between the input data and the corresponding labels.

131.Temporal Coherence

Temporal Coherence refers to the consistency and continuity of information or patterns across time. This concept is particularly important in areas such as computer vision, natural language processing, and time-series analysis, where AI models need to process and understand data that evolves over time.

Temporal coherence can be viewed from different perspectives, depending on the specific application:

  1. In computer vision, temporal coherence might refer to the smoothness and consistency of visual content in videos, where objects and scenes should maintain their properties and relationships across frames.
  2. In natural language processing, it could refer to the consistency and flow of information in a text or conversation, ensuring that the AI model generates responses or summaries that logically follow previous statements or events.
  3. In time-series analysis, temporal coherence could relate to the consistency of patterns and trends in the data, such that the AI model can predict future values based on past observations.

132.Unsupervised Learning

A type of machine learning in which the training data is not labeled, and the model is trained to find patterns and relationships in the data on its own.

133.Webhook

A webhook is a way for one computer program to send a message or data to another program over the internet in real-time. It works by sending the message or data to a specific URL, which belongs to the other program. Webhooks are often used to automate processes and make it easier for different programs to communicate and work together. They are a useful tool for developers who want to build custom applications or create integrations between different software systems.