The AI Glossary
This glossary has been curated by data scientists and machine learning experts like you.
The Appen Artificial Intelligence Glossary
To help those who are just learning about the nuances of AI, we have developed the below Artificial Intelligence Glossary, a list of words and terms which can help prepare you for when AI starts to become a part your everyday conversations.
More than just robots seeking to terminate or games looking to self-engage in a challenge versus humans, artificial intelligence (AI) is the application of complex programmatic math in which the outcome, combined with high quality training data, becomes the technological advances we see occurring in our everyday lives. From self-driving cars to finding cures for cancer, artificial intelligence applied in the real world is becoming a way of life.
Active Learning (Active Learning Strategy)
Area Under the Curve (AUC)
Artificial Neural Networks
Association Rule Learning
Automated Speech Recognition
Backpropagation (Backpropagation Through Time)
Bias (Inductive Bias, Confirmation Bias)
Inductive Bias: the set of assumptions that the learner uses when predicting outputs given inputs that have not been encountered yet.
Confirmation Bias: the tendency to search for, interpret, favor, and recall information in a way that confirms one’s own beliefs or hypotheses while giving disproportionately less attention to information that contradicts it.
A human worker providing annotations on the Appen data annotation platform.
Convolutional Neural Network (CNN)
Central Processing Unit (CPU)
Cross-Validation (k-fold Cross-Validation, Leave-p-out Cross-Validation)
Data (Structured Data, Unstructured Data, Data augmentation)
Structured Data: data processed in a way that it becomes ingestible by a Machine Learning algorithm and, if in the case of Supervised Machine Learning, labeled data; data after it has been processed on the Appen data annotation platform.
Deep Learning (Deep Reinforcement Learning)
Embedding (Word Embedding)
Feature (Feature Selection, Feature Learning)
A variable that is used as an input to a model.
Feed-Forward (Neural) Networks
Garbage In, Garbage Out
General Data Protection Regulation (GDPR)
Generative Adversarial Networks (GANs)
Graphic Processing Unit (GPU)
Hyperparameter (Hyperparameter Tuning)
Layer (Hidden Layer)
Long Short-Term Memory Networks
Machine Learning Lifecycle Management
Named Entity Recognition
Natural Language Processing (NLP)
Optical Character Recognition
Pooling (Max Pooling)
Personally Identifiable Information
Principal Component Analysis
Rectified Linear Unit
Recurrent Neural Networks
Regression (Linear Regression, Logistic Regression)
The subfield of Machine Learning inspired by human behavior studying how an agent should take action in a given environment to maximize some notion of cumulative reward.