AI Glossary

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A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

A

Artificial Intelligence (AI)
The simulation of human intelligence by machines, particularly computer systems.
Algorithm
A set of rules or instructions given to an AI program to help it learn on its own.
Autonomous Systems
Systems that can operate and make decisions without human intervention.

B

Backpropagation
Algorithm for training neural networks by adjusting weights based on error gradient.
Bias
Systematic error introduced into sampling or testing by selecting or encouraging one outcome over others.
Big Data
Extremely large datasets that may be analyzed to reveal patterns and trends.

C

Computer Vision
Field of AI that trains computers to interpret and understand visual world.
Clustering
Grouping similar data points together based on common characteristics.
CNN (Convolutional Neural Network)
Deep learning algorithm particularly effective for analyzing visual imagery.

D

Deep Learning
Subset of machine learning based on artificial neural networks with multiple layers.
Data Mining
Process of discovering patterns in large datasets.
Decision Tree
Tree-like model of decisions and their possible consequences.

E

Ensemble Learning
Technique combining multiple machine learning models to create a more accurate model.
Epoch
One complete pass through the entire training dataset during model training.
Expert System
AI system that emulates decision-making of human experts using predefined rules.

F

Feature Engineering
Process of selecting, manipulating and transforming raw data into features for machine learning.
Fine-tuning
Process of adjusting pre-trained model parameters for specific task or domain.
Federated Learning
Technique for training algorithms across decentralized devices without exchanging data.

G

Gradient Descent
Optimization algorithm for finding local minimum of a differentiable function.
GPU (Graphics Processing Unit)
Specialized processor designed to accelerate machine learning computations.
GAN (Generative Adversarial Network)
AI architecture where two networks compete to generate authentic-looking data.

H

Hyperparameter
Configuration variables set before training that control the learning process.
Hidden Layer
Layer in neural network between input and output layers.
Heuristic
Problem-solving approach using practical methods not guaranteed to be optimal.

I

Image Recognition
Technology that identifies places, people, objects, and actions in images.
Inference
Process of using trained model to make predictions on new data.
Instance-based Learning
Learning method that compares new problem instances with training instances.

J

JSON (JavaScript Object Notation)
Data format commonly used for transmitting AI model configurations and API responses.
Joint Probability
Probability of two or more events occurring simultaneously, important in probabilistic AI models.
Jupyter Notebook
Interactive computing environment popular for AI/ML development and data analysis.

K

K-means Clustering
Algorithm that partitions data into k clusters based on similarity.
Kernel
Function used to transform data in SVM algorithms.
K-Nearest Neighbors (KNN)
Classification algorithm based on closest training examples in feature space.

L

Large Language Model (LLM)
AI model trained on vast text data to understand and generate human-like text.
Loss Function
Function that measures how well model's predictions match training data.
Learning Rate
Hyperparameter controlling how much model adjusts weights during training.

M

Machine Learning
Field of AI focused on systems that can learn from experience.
Model Training
Process of teaching a machine learning model to make predictions.
Multi-layer Perceptron
Class of feedforward neural network with multiple layers.

N

Neural Network
Computing system inspired by biological neural networks.
NLP (Natural Language Processing)
Branch of AI dealing with understanding and generating human language.
Normalization
Process of scaling data to a specific range for better model performance.

O

Overfitting
When model learns training data too well, performing poorly on new data.
Optimization
Process of minimizing or maximizing a function by systematically choosing input values.
One-Hot Encoding
Representation of categorical variables as binary vectors.

P

Perceptron
Simple type of artificial neural network unit.
Preprocessing
Transforming raw data before feeding it into a machine learning algorithm.
Parameter
Variable in the model learned during training.

Q

Q-Learning
Form of reinforcement learning that learns the value of actions in states without requiring a model.
Quantization
Process of reducing model precision to improve performance and reduce memory usage.
Query
Input or request made to an AI model or system to retrieve information or generate a response.

R

Reinforcement Learning
Training models to make sequences of decisions through reward-based feedback.
Regression
Predicting continuous numerical values based on input features.
RNN (Recurrent Neural Network)
Neural network designed to work with sequence data.

S

Supervised Learning
Training with labeled data to learn input-output mappings.
SVM (Support Vector Machine)
Algorithm for classification and regression analysis.
Sentiment Analysis
Determining emotional tone behind words using NLP.

T

Transfer Learning
Applying knowledge learned in one task to a different but related task.
Tensor
Multi-dimensional array used in deep learning computations.
Training Set
Dataset used to train a machine learning model.

U

Underfitting
When model is too simple to learn the underlying data patterns.
Unsupervised Learning
Learning patterns in data without pre-existing labels.
Utility Function
Function defining agent's preferences in reinforcement learning.

V

Validation Set
Dataset used to tune hyperparameters and assess model performance.
Vector
One-dimensional array of numbers used in machine learning.
Variance
Measure of model's sensitivity to variations in training data.

W

Weights
Parameters in neural network that determine strength of connections.
Word Embedding
Representation of words as vectors of real numbers.
Workflow
Sequence of machine learning pipeline steps from data to deployment.

X

XGBoost
Popular implementation of gradient boosted decision trees.
XAI (Explainable AI)
AI systems whose actions can be easily understood by humans.

Y

Yield Function
Function that determines the output of a neural network node.

Z

Zero-shot Learning
Ability to solve tasks without any specific training examples.
Z-score Normalization
Standardizing data by transforming it to have zero mean and unit variance.

Additional Resources

This glossary provides a curated list of essential AI and Machine Learning terms. For more comprehensive definitions, you may also want to explore:

Google Machine Learning Glossary

A comprehensive collection of ML terms maintained by Google's AI/ML teams.

Visit Google's ML Glossary

Attributions:

  • Definitions in this glossary are synthesized from various academic and industry sources
  • Some definitions are adapted from publicly available resources including Google's Machine Learning Glossary
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