AI & Robotics Ontology
This comprehensive knowledge base provides definitions for key terms and concepts in artificial intelligence and robotics. Understanding this vocabulary is essential for navigating the rapidly evolving landscape of intelligent systems.
Core AI Concepts
Artificial Intelligence (AI)
The simulation of human intelligence processes by computer systems. These processes include learning, reasoning, and self-correction. AI encompasses sub-fields such as machine learning, NLP, computer vision, and robotics.
Machine Learning (ML)
A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed. ML algorithms build mathematical models from training data to make predictions or decisions.
Deep Learning
A subset of machine learning based on artificial neural networks with multiple layers between input and output. Deep learning has achieved breakthrough results in computer vision, NLP, and speech recognition.
Neural Network
Computing systems inspired by biological neural networks. Consists of interconnected nodes organized in layers. Each connection can transmit signals, with outputs computed by non-linear functions.
Large Language Model (LLM)
A neural network trained on vast text data to understand and generate human language. Modern LLMs like GPT-4, Claude, and Gemini use Transformer architecture and demonstrate emergent capabilities.
Transformer
A deep learning architecture relying on self-attention mechanisms to process sequential data. Unlike recurrent architectures, Transformers process entire sequences simultaneously, enabling parallel computation.
Machine Learning Approaches
Supervised Learning
Learning from labeled training data where each example is paired with the correct output. The goal is to learn a general rule that maps inputs to outputs.
Unsupervised Learning
Learning from unlabeled data where the algorithm must find structure and patterns. Common tasks include clustering, dimensionality reduction, and anomaly detection.
Reinforcement Learning
A learning paradigm where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward.
Transfer Learning
Applying knowledge gained from solving one problem to a different but related problem. Dramatically reduces data and computation required.
Robotics and Automation
Robot
A machine capable of carrying out complex actions automatically, programmable by computer. May be autonomous or semi-autonomous.
Industrial Robot
A robot system used for manufacturing, typically articulated arms designed for repetitive tasks such as welding, painting, and assembly.
Cobot
Collaborative robot designed to interact with humans in shared workspaces. Incorporates safety features for human-robot collaboration.
SLAM
Simultaneous Localization and Mapping—the computational problem of constructing a map while keeping track of location within it.
ROS
Robot Operating System—a flexible framework providing libraries and tools for building robot applications. Despite its name, ROS is middleware, not an OS.
Sensor Fusion
Combining data from multiple sensors to produce more accurate information than individual sensors alone. Critical for autonomous vehicles.
AI Ethics and Safety
AI Alignment
Ensuring AI systems pursue intended goals and behave in ways consistent with human values. Critical as AI systems become more capable.
Explainable AI (XAI)
Methods that make AI system decisions understandable to humans. Essential for trust, accountability, and regulatory compliance.
Algorithmic Bias
Systematic errors creating unfair outcomes, privileging arbitrary groups. Can emerge from training data, architecture, or deployment contexts.
Turing Test
A test of a machine's ability to exhibit intelligent behavior indistinguishable from a human. Proposed by Alan Turing in 1950.