A
- Accuracy: The proportion of correct predictions made by an AI model relative to all predictions.
- Agent: An entity that perceives its environment and takes actions to achieve specific goals.
- Algorithm: A set of rules or steps for solving a problem or performing a computation.
- Artificial General Intelligence (AGI): AI with the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence.
- Artificial Intelligence (AI): The simulation of human intelligence in machines, enabling them to perform tasks like learning, reasoning, and problem-solving.
- Artificial Neural Network (ANN): A computing system inspired by the human brain’s network of neurons, used for pattern recognition and learning.
- Autonomy: The ability of an AI system to operate independently without human intervention.
- AutoML: Automated Machine Learning processes that simplify the development of machine learning models.
B
- Backpropagation: An algorithm used to train neural networks by adjusting weights to minimize error.
- Bias (in AI): Systematic errors in AI systems due to flawed data or algorithms, potentially leading to unfair outcomes.
- Big Data: Extremely large datasets that require specialized methods and technologies to analyze.
- Bot: A software application that performs automated tasks, often used in web scraping or online customer service.
- Business Intelligence (BI): Technologies and practices for analyzing business data to support decision-making.
C
- Chatbot: A program that simulates human conversation to assist users, often used in customer service.
- Classification: A machine learning task that assigns categories to data points.
- Clustering: Grouping data points into clusters based on similarity without predefined labels.
- Cognitive Computing: AI systems designed to mimic human thought processes.
- Computer Vision: A field of AI that enables machines to interpret and process visual information.
- Context Awareness: The ability of AI systems to recognize and respond to the context of their environment.
- Convolutional Neural Network (CNN): A type of neural network specialized for image and video recognition.
- Corpus: A large collection of text used for training language models.
- Crowdsourcing: Using large groups of people to gather data or perform tasks.
- Customer Relationship Management (CRM): Software systems for managing customer interactions and data.
D
- Data Augmentation: Techniques for increasing the diversity of training data without collecting new data.
- Data Mining: The process of discovering patterns in large datasets.
- Data Preprocessing: Steps taken to clean and prepare raw data for analysis.
- Dataset: A structured collection of data used for training or testing AI models.
- Decision Tree: A flowchart-like model used for decision-making and classification.
- Deep Learning: A subset of machine learning using neural networks with many layers.
- Domain Adaptation: Techniques for applying an AI model to a different but related dataset.
- Drone AI: AI systems used to autonomously control drones.
E
- Edge Computing: Processing data near the source rather than in a centralized data center.
- Epoch: A single pass through the entire training dataset during model training.
- Ethics in AI: The study of moral principles governing AI development and use.
- Expert System: AI that uses a knowledge base and inference rules to solve specific problems.
- Exploratory Data Analysis (EDA): Techniques for summarizing and visualizing data.
F
- Feature: An individual measurable property of data used in machine learning.
- Feature Engineering: Creating new features to improve model performance.
- Federated Learning: A decentralized approach to training AI models across multiple devices while keeping data localized.
- Fine-Tuning: Adjusting a pre-trained model to a new, related task.
- Forecasting: Using AI to predict future trends based on historical data.
G
- Generative AI: AI systems capable of creating new content, such as text, images, or music.
- Generative Adversarial Network (GAN): A neural network architecture used to generate realistic data.
- Gradient Descent: An optimization algorithm used to minimize error in machine learning models.
- Graph Neural Network (GNN): A neural network designed to process graph-structured data.
- Ground Truth: Accurate data used to validate the performance of AI models.
H
- Hyperparameter: Configuration settings used to control the behavior of machine learning algorithms.
- Hybrid AI: Combining different AI techniques to improve performance.
- Human-in-the-Loop (HITL): AI systems that involve human feedback to improve decision-making.
I
- Image Recognition: AI’s ability to identify objects or patterns in images.
- Imbalanced Dataset: A dataset where some classes are underrepresented.
- Inference: The process of making predictions with a trained model.
- Intelligent Agent: An AI system capable of perceiving, reasoning, and acting in its environment.
- Internet of Things (IoT): Network of connected devices that collect and share data.
J
- Job Scheduling: AI-driven optimization of task execution in computing environments.
- Joint Training: Simultaneously training multiple AI models on shared data.
K
- K-Means Clustering: A common clustering algorithm for grouping data points.
- Knowledge Base: A collection of information used by AI to make decisions.
- Knowledge Graph: A network of interconnected data points representing relationships.
L
- Labeling: Assigning categories to data points for supervised learning.
- Language Model: AI designed to understand and generate human language.
- Latent Variable: A hidden variable inferred from observable data.
- Learning Rate: A parameter that controls how much an AI model updates during training.
- Logistic Regression: A statistical method used for binary classification.
M
- Machine Learning (ML): Algorithms enabling systems to learn from data without explicit programming.
- Meta-Learning: Learning how to learn, enabling AI to adapt to new tasks quickly.
- Model Drift: The degradation of model accuracy over time.
- Model Training: The process of teaching an AI model using data.
- Multi-Agent System: A collection of AI agents interacting to achieve complex goals.
- Multimodal AI: AI capable of processing multiple data types (e.g., text and images).
N
- Natural Language Processing (NLP): AI’s ability to understand and generate human language.
- Neural Network: A machine learning model inspired by the human brain’s structure.
- Noise: Unwanted or irrelevant data that can interfere with learning.
- Normalization: Scaling data to a standard range for improved model performance.
O
- Overfitting: When a model performs well on training data but poorly on new data.
- Optimization: Improving a model’s accuracy and efficiency.
- Outlier: A data point significantly different from others in the dataset.
- Output Layer: The final layer in a neural network providing the prediction.
P
- Parameter: A variable used by models to make predictions.
- Perceptron: The simplest type of neural network model.
- Personalization: Customizing AI outputs based on individual user preferences.
- Preprocessing: Preparing data before it’s used to train a model.
- Predictive Analytics: Using data to forecast future outcomes.
- Precision: The ratio of true positive results to all positive predictions.
- Privacy-Preserving AI: Techniques ensuring user data remains confidential during AI processing.
Q
- Quantization: Reducing model size and complexity without significant loss of accuracy.
- Query: A request for information from a database or AI system.
R
- Recall: The proportion of actual positives correctly identified by the model.
- Recurrent Neural Network (RNN): A neural network model designed for sequential data.
- Reinforcement Learning: AI learning by trial and error to maximize rewards.
- Regularization: Techniques used to prevent overfitting.
- Regression: Predicting continuous values rather than categories.
- Representation Learning: Automatically discovering useful features from raw data.
S
- Scalability: The ability of a system to handle growing workloads efficiently.
- Semi-Supervised Learning: Combining labeled and unlabeled data for training.
- Supervised Learning: Training AI with labeled data.
- Synthetic Data: Artificially generated data used for model training.
T
- Transfer Learning: Using a pre-trained model on a new but related task.
- Training Data: Data used to teach an AI model.
- Transformer: An advanced neural network architecture used for processing sequences, particularly in NLP.
U
- Underfitting: A model that is too simple to capture the underlying pattern in the data.
- Unlabeled Data: Data without predefined categories, used in unsupervised learning.
- Unsupervised Learning: Machine learning without labeled data, focused on finding hidden patterns.
- User Intent: The goal or purpose behind a user’s input to an AI system.
V
- Validation Set: A dataset used to fine-tune model parameters during training.
- Variance: A measure of how much model predictions fluctuate with different datasets.
- Vectorization: The process of converting data into numerical vectors for machine learning.
- Voice Recognition: AI technology that identifies and processes spoken language.
W
- Weak AI: AI systems designed for specific tasks without true understanding or consciousness.
- Weight: A parameter in neural networks that determines the importance of input features.
- Word Embedding: A technique for representing words as numerical vectors to capture semantic relationships.
- Workflow Automation: Using AI to streamline repetitive business processes.
X
- XAI (Explainable AI): AI systems designed to make their decision-making processes transparent and understandable.
- XML (Extensible Markup Language): A data format often used for exchanging structured data in AI applications.
Y
- Yield Prediction: Using AI to forecast production outputs, common in agriculture and manufacturing.
- YOLO (You Only Look Once): A real-time object detection algorithm in computer vision.
Z
- Zero-Shot Learning: AI models that can perform tasks without specific training examples.
- Z-Score: A statistical measure indicating how many standard deviations a data point is from the mean.
- Zone of Proximal Development (ZPD): An educational concept sometimes applied to AI learning environments.
This glossary provides a foundational understanding of key AI and AI Agent terms. Bookmark it as a quick reference guide as you explore the world of artificial intelligence.
