Ultimate Glossary: 100 AI Terms You Need to Know in 2025

f you are trying to understand artificial intelligence technology along with all its topics, you first must learn all the AI terms that are included in this wide category.

We must admit that now the term “artificial intelligence” is used worldwide, and will soon become a significant part of our daily lives. There are already different AI tools that are popular among users such as Google Gemini, ChatGPT, or Copilot from Microsoft, that help users with different daily tasks and questions.

Because this industry is continuously evolving, it’s important to know all the AI terms that are important if you want to dive into this impressive sector.

100 AI Terms You Need To Know In 2025

Central Concepts

‍

Artificial Intelligence - represents a concept that includes to ability of different machines to simulate human cognitive labor.

‍

Machine Learning - a subset of AI that builds algorithms to help machines learn using datasets.

‍

Deep Learning - is a subset of machine learning and functions based on artificial neural networks to make predictions from data.

‍

Neural Network - a machine learning model that is able to mimic human brain activity to understand complex data.

‍

Supervised learning - represents a training method in which the AI system is trained directly on labeled data.

‍

Unsupervised learning - a training method from machine learning, which focuses on training the AI on unlabeled data.

‍

Reinforcement learning - a form of training method that uses rewards or penalties in the AI learning process.

‍

AGI ( Artificial General Intelligence): a conceptual form of AI that is able to execute every intellectual task as humans.

‍

Weak AI - a subgroup of artificial intelligence that is developed with a limit on a certain area.

‍

Black box - a deep learning model that makes decisions without providing any details about the decision-making process.

‍

Automation - the process of using AI to perform different tasks without human intervention.

‍

Artificial superintelligence - a concept of AI that is able to outperform human intelligence in any field.

‍

Chatbot - a conversational interface powered by AI that engages in human conversations with users.

‍

AI agent - the advanced generation of AI bots that are capable of understanding and solving complex issues.

‍

AI copilot - an AI assistant that offers various suggestions, resources, or actions for human users

‍

AI ethics - moral values and principles that control the development along with the responsible use of AI technology.

‍

Augmentation - the process of enhancing human capabilities using technology instead of totally replacing them.

‍

Anthropomorphism - the process of attributing different human traits and emotions to the AI systems.

‍

AI safety - a list of measures and best practices that avoid the potential errors of AI systems.

‍

Bias - a series of prejudices that support different groups of people and that can be inserted into the MLM.

‍

Conditional generation - the capability of an AI system to create outcomes using certain prompts or conditions.

‍

Emergent behavior - an unpredictable pattern that can occur from the interaction of advanced AI system with datasets.

‍

Multimodal AI - a type of AI that is able to process and understand various data types at the same time.

‍

Synthetic Data - a type of data generated artificially generated to train the models when the real data is not available.

‍

Small language model - compressed AI models that are trained on limited hardware data.

‍

Data, training & AI infrastructure

‍

Data set - a structured collection of data that is used to train, test, and validate AI models

‍

Training data - a data set that is used to train the machine learning models.

‍

Context window- the information volume that the AI model is able to keep during an interaction.

‍

Large language model - an AI system that is trained on a large amount of data to understand and generate similar human responses.

‍

Natural language processing - an AI area that focuses on allowing machines to understand, and generate human language.

‍

Prompt - the input provided to an AI system that dictates the generation of new content.

‍

Parameters - internal variables that are modified during the training process to improve the AI performance.

‍

Pre-trained model - represents an AI model that was trained on a large data set to be improved for certain tasks.

‍

Prompt engineering - the process of designing and optimizing prompts to obtain a certain behavior from an AI system.

‍

Token - represents the simple form of text, such as work or even a punctuation mark that is processed and analyzed by the AI model.

‍

Temperature - a parameter that is responsible for controlling the creativity or randomness level in an AI outcome.

‍

Chain of thought - step-by-step reasoning process that is followed by AI to generate an output.

‍

Data preprocessing - converting raw data into a more readable format to be interpreted by an MLM.

‍

Data mining - the process that involves discovering patterns in large amounts of data sets.
Algorithm - a fixed set of rules that is used by the machine learning model in order to learn different patterns.

‍

Genetic Algorithm - an effective method used to solve constrained and unconstrained problems.
Validation Data - data that is used to adapt the parameters of a model in order to avoid overfitting in training.

‍

Data Labeling - the process where the data is being explained in order to guide the model learning process.

‍

Overfitting - an error in modeling that happens when a certain function is too similar to a set of data points.

‍

Underfitting - a modeling error that happens when the AI model fails to learn enough from the training data.

‍

Transfer learning - a method of machine learning that is used by the pre-trained model to solve a different but related task to save time.

Data augmentation - a method that expands the training data artificially

‍

GPU - a hardware component that can accelerate the AI processes, especially the deep learning processes.

‍

TPU - a chip designed by Google to optimize the AI model training on a large scale.

‍

Model drift - the degradation of an MLM’s performance in time due to certain changes in the data patterns.

‍

Algorithms & Techniques

‍

Decision Tree - a support tool especially for decision making which uses a tree-like model to analyze all the potential consequences.

‍

Gradient Descent - algorithm created for optimization and used for minimizing different functionalities by moving in the direction of the highest decent.

‍

Recurrent Neural Network - a type of artificial neural network created to identify various patterns in a series of data.

‍

Convolutional Neural Network - a certain type of neural network designed to use convolutional layers to select inputs.

‍

Random Forest - a learning method that is used for tasks, by forming various decision trees.

‍

Backpropagation - an artificial neural network method created to estimate the gradient required for the calculation of weights.

‍

Transformer - a neural network type that has the role of processing sequences of data for various tasks.

‍

Long short-term memory - neural network type that can learn the order dependence in sequential data.

‍

Attention Mechanism - a method created especially for helping the AI models have a better focus on important parts of the input.

‍

Principal Component Analysis - a process that uses an orthogonal transformation to transform a set of possibly correlated observations into uncorrelated variables.
‍

Autoencoder - neural network type created especially to learn to compress and reconstruct input data, in anomaly detection.

‍

Ensemble learning - a machine learning process, where models are trained to figure out the same problem to be combined for improved results.

‍

Logistic Regression - an algorithm for classification that predicts a binary outcome according to a set of variables.

‍

Q-Learning - a technique of reinforcement learning created to detect the perfect option based on a q function.

‍

Naive Bayes - a technique for classifying the content using Baye’s Theorem, used usually in text analysis.

‍

Principal component analysis - a technique used to reduce the data dimensions while maintaining the variation.

‍

Generative Adversarial Network - algorithmic architecture applied in unsupervised learning, to generate realistic synthetic data.

‍

Feedback Loops - the process that involves the usage of AI outputs to improve future performances.

‍

Explainable AI - AI model that has transparent systems to make its decision-making process easier to understand.

‍

Inference - the process of using a trained model to make predictions based on new sets of data.

‍

Hallucination - the event when the AI system generates an output that seems reasonable but is incorrect.

‍

Model - the AI structure that is specially trained for certain tasks like prediction or generation.

‍

Retrieval augmented generation - the process that combines LLMs with certain retrieval systems to generate more precise outcomes.

‍

Validation - the process of analyzing the performance of a trained model.

‍

Zero data retention - a technique used especially in AI systems to make sure that personal information is not stored.

‍

Zero-shot learning - the capability of AI systems to execute certain tasks without needing previous training

Practical AI: Everyday use cases

‍

Deepfake - a media content such as video or audio that is able to imitate humans with generative AI models.

‍

Computer Vision - a process that allows machines to understand certain visual information directly from images or videos.

‍

Autonomous Vehicles - represents the concept of self-driving cars that use AI technology for different purposes such as navigation.

‍

Predictive analysis - the process of using AI to predict certain trends based on previous events.

‍

Fraud detection - an AI model trained to find fraudulent transactions.

‍

AI in healthcare - technology that can offer diagnosis, along with personalized treatment plans according to symptoms.

‍

AI in education - the process of integrating AI technology into educational tools and learning platforms.

‍

AI in finance - advanced functionalities such as risk management, and algorithmic trading using AI technology.

‍

Emotion recognition - the process of analyzing and detecting human emotions directly from facial expressions or text inputs.

‍

Smart assistants - AI integrated into different tools such as Google Assistant, Alexa, or Siri that can perform tasks based on voice inputs.

‍

AI in Cybersecurity - using artificial intelligence to detect potential threats based on pattern recognition.

‍

Robotic Process Automation - software robots that use AI to automate repetitive tasks.

‍

Sentiment Analysis - a social media monitoring technique that uses NLP to discover certain emotions or opinions.

‍

Facial recognition - the process of using AI to detect certain persons from photos or videos.

‍

Personalized learning - the process of creating personalized learning plans for students based on AI insights.

‍

Voice recognition - an AI system that is able to transform voice inputs into text in order to solve certain tasks.

‍

Behavioral Biometrics - the process of analyzing the typing speeds, mouse movement, or other actions for fraud detection.

‍

Cognitive robotics - robots that use AI technology to understand and make decisions based on certain real-world environments.

‍

Narrow AI - AI systems that are trained to perform just a small range of tasks.

‍

Model Compression - the process of reducing the size of AI models in order to speed up the reasoning process.

‍

Self-Healing AI - AI system that is capable of detecting automatic issues and fixing them without requiring humans.

‍

Chatbot - virtual assistants that are able to imitate human conversation.

‍

AI as a service - cloud-based platforms that offer AI functionalities without requiring internal knowledge.

‍

Continual learning - AI systems that continue the learning process over time, constantly adding new data.

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100 AI Terms You Need To Know In 2025

Central Concepts

‍

Artificial Intelligence - represents a concept that includes to ability of different machines to simulate human cognitive labor.

‍

Machine Learning - a subset of AI that builds algorithms to help machines learn using datasets.

‍

Deep Learning - is a subset of machine learning and functions based on artificial neural networks to make predictions from data.

‍

Neural Network - a machine learning model that is able to mimic human brain activity to understand complex data.

‍

Supervised learning - represents a training method in which the AI system is trained directly on labeled data.

‍

Unsupervised learning - a training method from machine learning, which focuses on training the AI on unlabeled data.

‍

Reinforcement learning - a form of training method that uses rewards or penalties in the AI learning process.

‍

AGI ( Artificial General Intelligence): a conceptual form of AI that is able to execute every intellectual task as humans.

‍

Weak AI - a subgroup of artificial intelligence that is developed with a limit on a certain area.

‍

Black box - a deep learning model that makes decisions without providing any details about the decision-making process.

‍

Automation - the process of using AI to perform different tasks without human intervention.

‍

Artificial superintelligence - a concept of AI that is able to outperform human intelligence in any field.

‍

Chatbot - a conversational interface powered by AI that engages in human conversations with users.

‍

AI agent - the advanced generation of AI bots that are capable of understanding and solving complex issues.

‍

AI copilot - an AI assistant that offers various suggestions, resources, or actions for human users

‍

AI ethics - moral values and principles that control the development along with the responsible use of AI technology.

‍

Augmentation - the process of enhancing human capabilities using technology instead of totally replacing them.

‍

Anthropomorphism - the process of attributing different human traits and emotions to the AI systems.

‍

AI safety - a list of measures and best practices that avoid the potential errors of AI systems.

‍

Bias - a series of prejudices that support different groups of people and that can be inserted into the MLM.

‍

Conditional generation - the capability of an AI system to create outcomes using certain prompts or conditions.

‍

Emergent behavior - an unpredictable pattern that can occur from the interaction of advanced AI system with datasets.

‍

Multimodal AI - a type of AI that is able to process and understand various data types at the same time.

‍

Synthetic Data - a type of data generated artificially generated to train the models when the real data is not available.

‍

Small language model - compressed AI models that are trained on limited hardware data.

‍

Data, training & AI infrastructure

‍

Data set - a structured collection of data that is used to train, test, and validate AI models

‍

Training data - a data set that is used to train the machine learning models.

‍

Context window- the information volume that the AI model is able to keep during an interaction.

‍

Large language model - an AI system that is trained on a large amount of data to understand and generate similar human responses.

‍

Natural language processing - an AI area that focuses on allowing machines to understand, and generate human language.

‍

Prompt - the input provided to an AI system that dictates the generation of new content.

‍

Parameters - internal variables that are modified during the training process to improve the AI performance.

‍

Pre-trained model - represents an AI model that was trained on a large data set to be improved for certain tasks.

‍

Prompt engineering - the process of designing and optimizing prompts to obtain a certain behavior from an AI system.

‍

Token - represents the simple form of text, such as work or even a punctuation mark that is processed and analyzed by the AI model.

‍

Temperature - a parameter that is responsible for controlling the creativity or randomness level in an AI outcome.

‍

Chain of thought - step-by-step reasoning process that is followed by AI to generate an output.

‍

Data preprocessing - converting raw data into a more readable format to be interpreted by an MLM.

‍

Data mining - the process that involves discovering patterns in large amounts of data sets.
Algorithm - a fixed set of rules that is used by the machine learning model in order to learn different patterns.

‍

Genetic Algorithm - an effective method used to solve constrained and unconstrained problems.
Validation Data - data that is used to adapt the parameters of a model in order to avoid overfitting in training.

‍

Data Labeling - the process where the data is being explained in order to guide the model learning process.

‍

Overfitting - an error in modeling that happens when a certain function is too similar to a set of data points.

‍

Underfitting - a modeling error that happens when the AI model fails to learn enough from the training data.

‍

Transfer learning - a method of machine learning that is used by the pre-trained model to solve a different but related task to save time.

Data augmentation - a method that expands the training data artificially

‍

GPU - a hardware component that can accelerate the AI processes, especially the deep learning processes.

‍

TPU - a chip designed by Google to optimize the AI model training on a large scale.

‍

Model drift - the degradation of an MLM’s performance in time due to certain changes in the data patterns.

‍

Algorithms & Techniques

‍

Decision Tree - a support tool especially for decision making which uses a tree-like model to analyze all the potential consequences.

‍

Gradient Descent - algorithm created for optimization and used for minimizing different functionalities by moving in the direction of the highest decent.

‍

Recurrent Neural Network - a type of artificial neural network created to identify various patterns in a series of data.

‍

Convolutional Neural Network - a certain type of neural network designed to use convolutional layers to select inputs.

‍

Random Forest - a learning method that is used for tasks, by forming various decision trees.

‍

Backpropagation - an artificial neural network method created to estimate the gradient required for the calculation of weights.

‍

Transformer - a neural network type that has the role of processing sequences of data for various tasks.

‍

Long short-term memory - neural network type that can learn the order dependence in sequential data.

‍

Attention Mechanism - a method created especially for helping the AI models have a better focus on important parts of the input.

‍

Principal Component Analysis - a process that uses an orthogonal transformation to transform a set of possibly correlated observations into uncorrelated variables.
‍

Autoencoder - neural network type created especially to learn to compress and reconstruct input data, in anomaly detection.

‍

Ensemble learning - a machine learning process, where models are trained to figure out the same problem to be combined for improved results.

‍

Logistic Regression - an algorithm for classification that predicts a binary outcome according to a set of variables.

‍

Q-Learning - a technique of reinforcement learning created to detect the perfect option based on a q function.

‍

Naive Bayes - a technique for classifying the content using Baye’s Theorem, used usually in text analysis.

‍

Principal component analysis - a technique used to reduce the data dimensions while maintaining the variation.

‍

Generative Adversarial Network - algorithmic architecture applied in unsupervised learning, to generate realistic synthetic data.

‍

Feedback Loops - the process that involves the usage of AI outputs to improve future performances.

‍

Explainable AI - AI model that has transparent systems to make its decision-making process easier to understand.

‍

Inference - the process of using a trained model to make predictions based on new sets of data.

‍

Hallucination - the event when the AI system generates an output that seems reasonable but is incorrect.

‍

Model - the AI structure that is specially trained for certain tasks like prediction or generation.

‍

Retrieval augmented generation - the process that combines LLMs with certain retrieval systems to generate more precise outcomes.

‍

Validation - the process of analyzing the performance of a trained model.

‍

Zero data retention - a technique used especially in AI systems to make sure that personal information is not stored.

‍

Zero-shot learning - the capability of AI systems to execute certain tasks without needing previous training

Practical AI: Everyday use cases

‍

Deepfake - a media content such as video or audio that is able to imitate humans with generative AI models.

‍

Computer Vision - a process that allows machines to understand certain visual information directly from images or videos.

‍

Autonomous Vehicles - represents the concept of self-driving cars that use AI technology for different purposes such as navigation.

‍

Predictive analysis - the process of using AI to predict certain trends based on previous events.

‍

Fraud detection - an AI model trained to find fraudulent transactions.

‍

AI in healthcare - technology that can offer diagnosis, along with personalized treatment plans according to symptoms.

‍

AI in education - the process of integrating AI technology into educational tools and learning platforms.

‍

AI in finance - advanced functionalities such as risk management, and algorithmic trading using AI technology.

‍

Emotion recognition - the process of analyzing and detecting human emotions directly from facial expressions or text inputs.

‍

Smart assistants - AI integrated into different tools such as Google Assistant, Alexa, or Siri that can perform tasks based on voice inputs.

‍

AI in Cybersecurity - using artificial intelligence to detect potential threats based on pattern recognition.

‍

Robotic Process Automation - software robots that use AI to automate repetitive tasks.

‍

Sentiment Analysis - a social media monitoring technique that uses NLP to discover certain emotions or opinions.

‍

Facial recognition - the process of using AI to detect certain persons from photos or videos.

‍

Personalized learning - the process of creating personalized learning plans for students based on AI insights.

‍

Voice recognition - an AI system that is able to transform voice inputs into text in order to solve certain tasks.

‍

Behavioral Biometrics - the process of analyzing the typing speeds, mouse movement, or other actions for fraud detection.

‍

Cognitive robotics - robots that use AI technology to understand and make decisions based on certain real-world environments.

‍

Narrow AI - AI systems that are trained to perform just a small range of tasks.

‍

Model Compression - the process of reducing the size of AI models in order to speed up the reasoning process.

‍

Self-Healing AI - AI system that is capable of detecting automatic issues and fixing them without requiring humans.

‍

Chatbot - virtual assistants that are able to imitate human conversation.

‍

AI as a service - cloud-based platforms that offer AI functionalities without requiring internal knowledge.

‍

Continual learning - AI systems that continue the learning process over time, constantly adding new data.

Ultimate Glossary: 100 AI Terms You Need to Know in 2025

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and everything we write.

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