AI Fundamentals
59 terms in the AI Fundamentals domain — each bilingual TR/EN with related-term graph.
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AI Winter
Periods when interest and investment in AI dropped sharply due to the gap between expectations and technical reality.
Action
The decision output chosen by an agent according to the current state, which produces an effect on the environment.
Active Learning
A data-efficiency approach in which the model selects the most informative examples and requests labels from a human or expert source.
Annotation
The process of adding meaningful labels, notes, or markings to data so that it becomes usable for model training.
Artificial General Intelligence (AGI)
A hypothetical level of AI with human-like flexibility, capable of transferring knowledge across different tasks and contexts.
Artificial Intelligence (AI)
A discipline focused on enabling machines to perform capabilities associated with human intelligence, such as perception, learning, reasoning, and decision-making.
Autonomous System
A system capable of handling perception, evaluation, and action generation with little or no human intervention.
Dartmouth Conference
The 1956 meeting widely regarded as the historical turning point where AI began to take shape as a distinct research field.
Data Leakage
A situation where model performance appears misleadingly strong because it has learned information during training that would not be available in real use.
Dataset
An organized collection of data that enables a model to be trained, evaluated, or tested.
Deep Learning
A modern machine learning approach that learns hierarchical representations from data using multi-layer neural networks.
Dimensionality Reduction
An approach that reduces the number of variables representing data while preserving as much useful information as possible.
Distance Metric
A mathematical function that numerically measures the difference or separation between two data points.
Embedding
A learned dense vector representation that carries the meaning of a word, document, image, or another entity.
Expert System
A classical AI approach that models domain expertise through rules and a knowledge base to perform inference.
Exploration-Exploitation Trade-off
The balance problem between trying new options to gain information and using already known good options.
Latent Space
The internal representational space in which a model encodes data in a more abstract, compressed, and meaningful way.
Learning Rate
A core hyperparameter that determines how much model parameters change at each update step.
Limited Memory AI
A type of AI that uses not only current input but also some recent observations and state information when making decisions.
Loss Function
A mathematical function that quantifies the difference between model predictions and true values and guides the training process.
Online Learning
A learning approach in which the model is updated incrementally with new examples arriving over time rather than all at once.
Optimization
The process of systematically improving model parameters according to an objective function in order to increase performance.
Overfitting
A situation where a model learns the training data too closely and performs poorly on new data.
Parameters and Hyperparameters
The core difference between internal values learned from data and external settings that shape the training process.
Perception
The core capability of transforming raw inputs into meaningful structures so the system can interpret its environment.
Policy
A decision rule or behavioral strategy that defines which action an agent should choose in a given state.
Reactive Machine
A basic type of AI system that responds only to current input without persistently using past experience.
Reasoning
The process of deriving new conclusions from available knowledge, rules, or observations and grounding decisions.
Regularization
A set of techniques used to reduce overfitting and improve a model’s ability to generalize.
Reinforcement Learning
A paradigm in which an agent learns a long-term behavior policy through rewards and penalties by interacting with its environment.
Representation Learning
An approach in which informative, discriminative, and task-relevant internal representations are learned automatically from raw data.
Reward Function
A feedback mechanism that numerically defines which outcomes the system should consider more valuable.
Rule-Based System
A classical system architecture whose behavior is determined by predefined if-then rules.
Search Space
The conceptual space that contains all possible states, solution paths, and action combinations of a problem.
Self-Supervised Learning
An approach that enables strong representation learning by generating supervision signals from the internal structure of the data itself.
Semi-Supervised Learning
An approach that improves learning performance by combining a small amount of labeled data with a large amount of unlabeled data.
Similarity
A core concept used to measure how close, related, or semantically similar two examples are.
State
A meaningful description of a system or environment at a given moment for decision-making purposes.
Supervised Learning
A core learning paradigm in which the relationship between inputs and target outputs is learned from labeled examples.
Symbolic AI
A classical AI approach that performs inference by representing knowledge with rules, symbols, and logical expressions.