AI Glossary: Key Terms & Concepts Explained
Artificial Intelligence (AI)
The simulation of human intelligence processes by machines, especially computer systems.
Machine Learning (ML)
A subset of AI that enables computers to learn from data and improve their performance over time without being explicitly programmed.
Deep Learning
A type of machine learning that uses neural networks with many layers (deep neural networks) to model complex patterns in data.
Neural Network
A series of algorithms modeled after the human brain that are designed to recognize patterns and interpret data.
Supervised Learning
A machine learning task where models are trained on labeled data, meaning each training example is paired with an output label.
Unsupervised Learning
A type of machine learning where models find patterns or structures in unlabeled data.
Reinforcement Learning
A learning paradigm where an agent learns to make decisions by performing actions and receiving feedback in the form of rewards or penalties.
Natural Language Processing (NLP)
The branch of AI focused on the interaction between computers and human language, enabling tasks like translation, sentiment analysis, and chatbots.
Computer Vision
AI technology that enables machines to interpret and analyze visual information from the world.
Training Data
The dataset used to train machine learning models, containing input-output pairs or unlabeled examples.
Model
A mathematical representation trained on data to make predictions or decisions.
Overfitting
A modeling error where a machine learning model performs well on training data but poorly on unseen data.
Underfitting
A situation where a model is too simple to capture the underlying pattern of the data, resulting in poor performance.
Bias
Systematic error introduced by incorrect assumptions in the learning algorithm.
Variance
The sensitivity of a model to fluctuations in the training data, affecting its ability to generalize.
AI Ethics
A field concerned with the moral implications and responsible development of AI technologies.
Automation
The use of AI and machines to perform tasks that were previously done by humans.
Artificial Intelligence (AI)
The simulation of human intelligence processes by machines, especially computer systems.
Machine Learning (ML)
A subset of AI that enables computers to learn from data and improve their performance over time without being explicitly programmed.
Deep Learning
A type of machine learning that uses neural networks with many layers (deep neural networks) to model complex patterns in data.
Neural Network
A series of algorithms modeled after the human brain that are designed to recognize patterns and interpret data.
Supervised Learning
A machine learning task where models are trained on labeled data, meaning each training example is paired with an output label.
Unsupervised Learning
A type of machine learning where models find patterns or structures in unlabeled data.
Reinforcement Learning
A learning paradigm where an agent learns to make decisions by performing actions and receiving feedback in the form of rewards or penalties.
Natural Language Processing (NLP)
The branch of AI focused on the interaction between computers and human language, enabling tasks like translation, sentiment analysis, and chatbots.
Computer Vision
AI technology that enables machines to interpret and analyze visual information from the world.
Training Data
The dataset used to train machine learning models, containing input-output pairs or unlabeled examples.
Model
A mathematical representation trained on data to make predictions or decisions.
Overfitting
A modeling error where a machine learning model performs well on training data but poorly on unseen data.
Underfitting
A situation where a model is too simple to capture the underlying pattern of the data, resulting in poor performance.
Bias
Systematic error introduced by incorrect assumptions in the learning algorithm.
Variance
The sensitivity of a model to fluctuations in the training data, affecting its ability to generalize.
AI Ethics
A field concerned with the moral implications and responsible development of AI technologies.
Automation
The use of AI and machines to perform tasks that were previously done by humans.