A

AI – Artificial Intelligence, the branch of computer science focused on creating machines capable of performing tasks that normally require human intelligence.

AGI – Artificial General Intelligence, theoretical AI that matches or exceeds human cognitive abilities across all domains.

Algorithm – A set of rules or instructions given to an AI system to solve problems or complete tasks.

API – Application Programming Interface, protocols that allow different AI systems and applications to communicate.

Attention Mechanism – A neural network technique that helps models focus on relevant parts of input data.

Autonomous System – AI-powered systems that can operate independently without human intervention.

Autoencoder – A neural network that learns to compress and reconstruct data, often used for feature learning.

B

Backpropagation – The method neural networks use to learn by adjusting weights based on errors in predictions.

BERT – Bidirectional Encoder Representations from Transformers, a language model developed by Google.

Bias – Systematic errors in AI models that can lead to unfair or inaccurate results for certain groups.

Big Data – Extremely large datasets that require specialized tools and techniques to process and analyze.

Binary Classification – Machine learning task that categorizes data into one of two classes.

Black Box – AI systems whose internal workings are not easily understood or interpretable by humans.

C

ChatGPT – OpenAI’s conversational AI model based on the GPT (Generative Pre-trained Transformer) architecture.

Computer Vision – AI field focused on enabling machines to interpret and understand visual information.

CNN – Convolutional Neural Network, a type of neural network particularly effective for image processing.

Clustering – Unsupervised learning technique that groups similar data points together.

Cognitive Computing – AI systems that simulate human thought processes to solve complex problems.

Cross-validation – Technique to assess how well a machine learning model will generalize to new data.

D

Deep Learning – Subset of machine learning using neural networks with multiple layers to learn complex patterns.

Data Mining – Process of discovering patterns and knowledge from large amounts of data.

Decision Tree – Machine learning model that makes decisions by splitting data based on feature values.

Diffusion Models – Generative AI models that create images by gradually removing noise from random data.

DALL-E – OpenAI’s AI system that generates images from text descriptions.

Data Augmentation – Techniques to artificially increase training data by creating modified versions of existing data.

E

Ensemble Learning – Combining multiple machine learning models to improve overall performance.

Explainable AI – AI systems designed to provide clear explanations for their decisions and processes.

Epoch – One complete pass through the entire training dataset during machine learning model training.

Edge AI – Running AI algorithms locally on devices rather than in the cloud for faster processing.

Embedding – Mathematical representations of words, images, or other data in a continuous vector space.

Expert System – AI program that mimics the decision-making ability of human experts in specific domains.

F

Feature Engineering – Process of selecting and transforming variables for machine learning models.

Fine-tuning – Adjusting a pre-trained AI model for specific tasks or domains.

Foundation Model – Large-scale AI models trained on broad data that can be adapted for various tasks.

Federated Learning – Training AI models across decentralized data sources without sharing raw data.

Forward Propagation – Process of passing input data through a neural network to generate predictions.

Fuzzy Logic – Form of logic that deals with reasoning that is approximate rather than fixed and exact.

G

GPT – Generative Pre-trained Transformer, a type of large language model developed by OpenAI.

GAN – Generative Adversarial Network, two neural networks competing to generate realistic synthetic data.

Gradient Descent – Optimization algorithm used to minimize errors in machine learning models.

Generative AI – AI systems that can create new content like text, images, music, or code.

GPU – Graphics Processing Unit, specialized hardware that accelerates AI and machine learning computations.

Ground Truth – The actual correct answer or label used to train and evaluate AI models.

H

Hallucination – When AI models generate false or nonsensical information that seems plausible.

Hyperparameter – Configuration settings that control how machine learning algorithms learn.

Human-in-the-loop – AI systems that incorporate human judgment and feedback in their decision-making process.

Heuristic – Problem-solving approach that uses practical methods to find good enough solutions quickly.

Hidden Layer – Intermediate layers in neural networks between input and output layers.

HuggingFace – Popular platform and library for sharing and using pre-trained AI models.

I

Inference – Process of using a trained AI model to make predictions on new, unseen data.

IoT – Internet of Things, network of connected devices that can collect and share data for AI processing.

Image Recognition – AI capability to identify and classify objects, people, or scenes in images.

Interpretability – The degree to which humans can understand and explain AI model decisions.

Imitation Learning – Training AI systems by having them mimic human behavior or expert demonstrations.

Instance Segmentation – Computer vision task that identifies and separates individual objects in images.

J

JAX – Google’s machine learning framework designed for high-performance numerical computing.

JSON – JavaScript Object Notation, a data format commonly used for AI model inputs and outputs.

Jailbreaking – Attempts to bypass AI safety measures or restrictions through clever prompting.

Joint Distribution – Probability distribution that describes the likelihood of multiple variables occurring together.

K

K-means – Popular clustering algorithm that groups data into k clusters based on similarity.

Knowledge Graph – Structured representation of information showing relationships between entities.

Keras – High-level neural network API that makes deep learning more accessible to developers.

KNN – K-Nearest Neighbors, simple machine learning algorithm for classification and regression.

Knowledge Distillation – Technique to transfer knowledge from large AI models to smaller, more efficient ones.

L

Large Language Model (LLM) – AI models trained on massive text datasets to understand and generate human language.

Learning Rate – Parameter that controls how quickly a machine learning model adapts during training.

Linear Regression – Statistical method for modeling relationships between variables using straight lines.

LSTM – Long Short-Term Memory, type of neural network good at processing sequential data.

Logistic Regression – Statistical method used for binary classification problems.

Loss Function – Mathematical function that measures how wrong an AI model’s predictions are.

M

Machine Learning – Subset of AI that enables computers to learn and improve from experience without explicit programming.

Model – Mathematical representation that AI systems use to make predictions or decisions.

Multimodal AI – AI systems that can process and understand multiple types of input like text, images, and audio.

MLOps – Machine Learning Operations, practices for deploying and maintaining ML models in production.

Metadata – Data that provides information about other data, often used to train AI systems.

Monte Carlo Method – Statistical technique that uses random sampling to solve complex problems.

N

Neural Network – Computing system inspired by biological neural networks that learns to perform tasks.

NLP – Natural Language Processing, AI field focused on helping computers understand human language.

NLG – Natural Language Generation, AI capability to produce human-like text from data.

Node – Individual processing unit within a neural network that receives and processes information.

Normalization – Technique to scale data to a standard range for better AI model performance.

NVIDIA – Leading company in AI hardware, particularly GPUs used for machine learning training.

O

OpenAI – AI research company known for creating GPT models, DALL-E, and ChatGPT.

Overfitting – When an AI model learns training data too specifically and fails on new data.

Optimization – Process of adjusting AI model parameters to achieve the best possible performance.

One-shot Learning – AI technique that enables models to learn from just one or very few examples.

ONNX – Open Neural Network Exchange, standard format for representing machine learning models.

Outlier Detection – Identifying data points that differ significantly from the majority of data.

P

Prompt Engineering – Art of crafting effective inputs to get desired outputs from AI language models.

PyTorch – Popular open-source machine learning framework developed by Meta (Facebook).

Preprocessing – Cleaning and preparing raw data before feeding it to machine learning models.

Precision – Metric measuring what percentage of positive predictions were actually correct.

Perceptron – Simplest type of artificial neural network with just one layer.

Parameter – Numerical values that machine learning models learn during training to make predictions.

Q

Q-Learning – Reinforcement learning algorithm that learns optimal actions in different situations.

Quantization – Technique to reduce AI model size by using fewer bits to represent numbers.

Query – Input or question submitted to an AI system to get information or perform a task.

Quantum Machine Learning – Emerging field combining quantum computing with machine learning algorithms.

R

Reinforcement Learning – ML approach where agents learn through trial and error using rewards and penalties.

RNN – Recurrent Neural Network, designed to process sequential data like text or time series.

Regression – Machine learning task that predicts continuous numerical values rather than categories.

Random Forest – Ensemble method that combines multiple decision trees for better predictions.

Regularization – Techniques to prevent overfitting by adding constraints to model complexity.

Retrieval-Augmented Generation – AI technique that combines information retrieval with text generation.

S

Supervised Learning – Machine learning using labeled examples to train models for predictions.

Speech Recognition – AI technology that converts spoken language into written text.

Sentiment Analysis – NLP technique to determine emotional tone or opinion in text.

SVM – Support Vector Machine, algorithm that finds optimal boundaries to separate different classes.

Stable Diffusion – Open-source AI model for generating images from text descriptions.

Synthetic Data – Artificially generated data used to train AI models when real data is limited.

T

Transformer – Revolutionary neural network architecture that powers modern language models like GPT.

TensorFlow – Google’s open-source machine learning framework for building AI applications.

Training Data – Dataset used to teach machine learning models to make accurate predictions.

Transfer Learning – Using knowledge from pre-trained models to solve new, related problems.

Turing Test – Test of machine intelligence based on ability to exhibit human-like conversation.

Tokenization – Process of breaking text into smaller units (tokens) for AI processing.

U

Unsupervised Learning – Machine learning that finds patterns in data without labeled examples.

Underfitting – When AI models are too simple to capture underlying patterns in data.

User Interface – How humans interact with AI systems, from chatbots to voice assistants.

Uncertainty Quantification – Measuring and expressing how confident AI models are in their predictions.

V

Validation – Process of testing AI model performance on data not used during training.

Vector Database – Specialized database for storing and searching high-dimensional vector embeddings.

Vision Transformer – Application of transformer architecture to computer vision tasks.

Variational Autoencoder – Generative model that learns to create new data similar to training examples.

Vanishing Gradient – Problem in deep networks where learning becomes ineffective in early layers.

W

Weight – Numerical parameters in neural networks that determine the strength of connections.

Weak AI – AI systems designed for specific tasks, as opposed to general artificial intelligence.

Word Embedding – Mathematical representation of words as vectors in multi-dimensional space.

Workflow Automation – Using AI to automatically perform routine business processes.

X

XAI – Explainable Artificial Intelligence, making AI decisions transparent and understandable.

XGBoost – Popular gradient boosting framework for machine learning competitions and applications.

XML – Extensible Markup Language, format sometimes used for structuring AI training data.

Y

YOLO – You Only Look Once, real-time object detection algorithm in computer vision.

Yield – In AI context, the output or result produced by an AI system or algorithm.

Z

Zero-shot Learning – AI capability to perform tasks without specific training examples for those tasks.

Z-score – Statistical measure used in data preprocessing to standardize values.