Here’s a glossary of common AI terms organized alphabetically:
A
• Algorithm: A set of rules or processes for solving a problem in a finite number of steps.
• Artificial Intelligence (AI): The simulation of human intelligence processes by machines, particularly computer systems.
• Artificial Neural Network (ANN): A machine learning model inspired by the structure and function of biological neural networks.
• Augmented Reality (AR): Technology that overlays digital content onto the real world, often used in AI-driven applications.
B
• Backpropagation: A method used in neural networks to calculate and update the weights to minimize error.
• Bias: A systematic error in a machine learning model that leads to incorrect predictions or analysis.
C
• Chatbot: An AI program designed to simulate conversation with users.
• Classifier: An algorithm that categorizes data into specific classes.
• Clustering: A type of unsupervised learning used to group similar data points together.
D
• Deep Learning: A subset of machine learning using multi-layered neural networks to process data.
• Decision Tree: A model that makes decisions based on a series of questions.
• Domain Adaptation: Techniques that allow AI models to work across different domains or datasets.
E
• Edge Computing: Processing data at or near the source rather than relying on a centralized data center.
• Embeddings: Vector representations of words or data points in machine learning.
F
• Feature Engineering: The process of selecting, modifying, or creating input variables for machine learning.
• Fine-Tuning: Adjusting a pre-trained model to perform a specific task better.
G
• Generative AI: A class of AI that creates new content, such as text, images, or music, based on learned patterns.
• Gradient Descent: An optimization algorithm used to minimize errors in machine learning models.
H
• Hyperparameters: Configurations set before training a machine learning model that affect performance.
• Heuristic: A practical approach to problem-solving that isn’t guaranteed to be optimal.
I
• Inference: The process of making predictions using a trained AI model.
• Instance Segmentation: Identifying and labeling individual objects in an image.
J
• Joint Probability: The likelihood of two events happening simultaneously.
K
• Knowledge Graph: A network of real-world entities and their relationships used to provide contextual information.
L
• Latent Space: A lower-dimensional representation of data learned by an AI model.
• Learning Rate: A hyperparameter that controls how much a model adjusts its weights in response to errors.
• Local Language Model (LLM): A very large transformer-based neural network trained on vast text corpora to predict and generate human-like language for many tasks.
M
• Machine Learning (ML): Algorithms that automatically learn patterns from data to make predictions or decisions without explicit programming.
• Model: A mathematical representation of a real-world process learned from data.
N
• Natural Language Processing (NLP): A field of AI that enables machines to understand and process human language.
• Neural Network: A series of algorithms that mimic the human brain to recognize relationships in data.
O
• Overfitting: When a model learns too much detail from the training data, resulting in poor performance on new data.
P
• Prompt Engineering: The process of designing and refining prompts to improve interactions with AI models.
• Pre-training: Training a model on a general task before fine-tuning it for a specific one.
Q
• Quantization: Reducing the precision of numbers in AI models to make them faster and more efficient.
R
• Reinforcement Learning (RL): An area of AI where agents learn by interacting with their environment.
• Regularization: Techniques used to prevent overfitting in machine learning models.
S
• Supervised Learning: A machine learning approach where models are trained on labeled data.
• Swarm Intelligence: Collective behavior of decentralized, self-organized systems.
T
• Tokenization: Breaking text into smaller pieces, like words or subwords, for processing in NLP.
• Transfer Learning: Applying a pre-trained model to a new but related problem.
U
• Unsupervised Learning: A type of machine learning that finds patterns in data without labeled responses.
• Underfitting: When a model is too simple to capture the underlying patterns in the data.
V
• Validation Set: A subset of data used to evaluate a model’s performance during training.
• Variational Autoencoder (VAE): A type of deep learning model used for generating new data points.
W
• Weight: Parameters within a neural network that are adjusted during training to minimize error.
X
• XAI (Explainable AI): AI systems designed to provide insights into their decision-making process.
Y
• YOLO (You Only Look Once): A real-time object detection algorithm.
Z
• Zero-Shot Learning: Training models to recognize new categories without labeled data for those categories.