AI Glossary

AI and Big Data Common Crawl Datasets Corpus in NLP Data Drift Data Poisoning Data Scarcity Ego 4D ImageNet SQuAD The Pile Imbalanced Data Machine Learning Bias Synthetic Data for AI Training Test Data Set Validation Data Set

Ablation Accuracy in Machine Learning Active Learning (Machine Learning)Adversarial Machine Learning Affective AI AI Agents AI and Education AI and Finance AI and Medicine AI Assistants AI Detection AI Ethics AI Generated Music AI Hallucinations AI Hardware AI in Customer Service AI Interpretability AI Lifecycle Management AI Literacy AI Monitoring AI Oversight AI Privacy AI Prototyping AI Recommendation Algorithms AI Regulation AI Resilience AI Robustness AI Safety AI Scalability AI Simulation AI Standards AI Steering AI Transparency AI Video Generation AI Voice Transfer Approximate Dynamic Programming Artificial Super Intelligence Backpropagation Bayesian Machine Learning Bias-Variance Tradeoff Binary Classification AI Chatbots Clustering in Machine Learning Composite AI Confirmation Bias in Machine Learning Conversational AI Convolutional Neural Networks Counterfactual Explanations in AI Curse of Dimensionality Data Labeling Deep Learning Deep Reinforcement Learning Differential Privacy Dimensionality Reduction Embedding Layer Emergent Behavior Entropy in Machine Learning Ethical AI Explainable AI F1 Score in Machine Learning F2 Score Feedforward Neural Network Fine Tuning in Deep Learning Gated Recurrent Unit Generative AI Graph Neural Networks Ground Truth in Machine Learning Hidden Layer Human Augmentation with AI Hyperparameter Tuning Intelligent Document Processing Large Language Model (LLM)Loss Function Machine Learning Machine Learning in Algorithmic Trading Model Drift Multimodal Learning Natural Language Generation (NLG)Natural Language Processing (NLP)Natural Language Querying (NLQ)Natural Language Understanding (NLU)Neural Text-to-Speech (NTTS)Neuroevolution Objective Function Precision and Recall Pretraining Recurrent Neural Networks Transformers Unsupervised Learning Voice Cloning Zero-shot Classification Models Machine Learning Neuron Reproducibility in Machine Learning Semi-Supervised Learning Supervised Learning Uncertainty in Machine Learning

Cognitive Architectures Keras Matplotlib MLOps Natural Language Toolkit (NLTK)NumPy Pandas PyTorch SciPy Scikit-learn Seaborn Python Package TensorFlow

Acoustic Models Activation Functions AdaGrad AI Alignment AI Emotion Recognition AI Guardrails AI Speech Enhancement Articulatory Synthesis Association Rule Learning Attention Mechanisms Augmented Intelligence Auto Classification Autoencoder Autoregressive Model Batch Gradient Descent Beam Search Algorithm Benchmarking Boosting in Machine Learning Candidate Sampling Capsule Neural Network Causal Inference Classification Clustering Algorithms Cognitive Computing Cognitive Map Collaborative Filtering Computational Creativity Computational Linguistics Computational Phenotyping Computational Semantics Conditional Variational Autoencoders Concatenative Synthesis Confidence Intervals in Machine Learning Context-Aware Computing Contrastive Learning Cross Validation in Machine Learning CURE Algorithm Data Augmentation Data Drift Decision Intelligence Decision Tree Deepfake Detection Diffusion Domain Adaptation Double Descent End-to-end Learning Ensemble Learning Epoch in Machine Learning Evolutionary Algorithms Expectation Maximization Feature Learning Feature Selection Feature Store for Machine Learning Federated Learning Few Shot Learning Flajolet-Martin Algorithm Forward Propagation Gaussian Processes Generative Adversarial Networks (GANs)Genetic Algorithms in AI Gradient Boosting Machines (GBMs)Gradient Clipping Gradient Scaling Grapheme-to-Phoneme Conversion (G2P)Grounding Human-in-the-Loop AI Hyperparameters Homograph Disambiguation Hooke-Jeeves Algorithm Hybrid AI Image Recognition Incremental Learning Inductive Bias Information Retrieval Instruction Tuning Keyphrase Extraction Knowledge Distillation Knowledge Representation and Reasoning k-Shingles Latent Dirichlet Allocation (LDA)Learning To Rank Learning Rate Logits Machine Learning Life Cycle Management Machine Learning Preprocessing Machine Translation Markov Decision Process Metaheuristic Algorithms Mixture of Experts Model Interpretability Monte Carlo Learning Multimodal AI Multi-task Learning Multitask Prompt Tuning Naive Bayes Classifier Named Entity Recognition Neural Radiance Fields Neural Style Transfer Neural Text-to-Speech (NTTS)One-Shot Learning Online Gradient Descent Out-of-Distribution Detection Overfitting and Underfitting Parametric Neural Networks Part-of-Speech Tagging Pooling (Machine Learning)Principal Component Analysis Prompt Chaining Prompt Engineering Prompt Tuning Quantum Machine Learning Algorithms Random Forest Rectified Linear Unit (ReLU)Regularization Representation Learning Restricted Boltzmann Machines Retrieval-Augmented Generation (RAG)RLHF Semantic Search Algorithms Semi-structured data Sentiment Analysis Sequence Modeling Semantic Kernel Semantic Networks Spike Neural Networks Statistical Relational Learning Symbolic AI Topic Modeling Tokenization Transfer Learning Vanishing and Exploding Gradients Voice Cloning Winnow Algorithm Word Embeddings

Last updated on February 29, 20242 min read

AI Glossary

Welcome to your definitive resource on the world of machine learning, applied deep learning, and the rapidly-evolving field of Language AI.

Expertise has to start somewhere. Michelin-starred chefs started by making grilled cheese sandwiches (or their equivalent) as an after-school snack. World-class architects’ first real-world building experiences came in the form of stacking blocks in the corner of the nursery. Today’s top-tier aerospace engineers got their start with paper airplanes and hobby kits, back in the day.

One can imagine that most experts have an origin story. And if you’re looking to spin yourself up on all things AI, this resource could be yours. Just as master chefs learned the basics of cooking simple dishes, and architects began by stacking blocks, this AI glossary provides the building blocks of knowledge needed to understand artificial intelligence.

Covering everything from the history of AI to current state-of-the-art techniques, our glossary serves as a launchpad for your AI education. Understanding the foundational concepts such as machine learning, neural networks, and data science will provide the basis for delving deeper into how AI systems work. From the simplest terms to more complex methodologies, our glossary allows you to start from square one and progressively build your knowledge.

Think of this as your first model rocket, baking soda volcano, or birdhouse. Much like the early projects of experts got the wheels turning for greater things to come, our beginner's guide to AI terms and concepts could start you down the path of becoming an AI expert yourself. So whether you're just AI-curious or aiming for a career in the field, consider this glossary your origin story. Let's start learning.

Navigating the AI Glossary

This glossary is divided into sections, each of which has a general theme. In no particular order:

Fundamentals. If you’re starting from scratch, here’s where you begin by learning about foundational concepts like machine learning, natural language processing (NLP), and the AI computing hardware whirring away behind the scenes.
Techniques. Once you get a feel for the basics, you’ll probably want to get acquainted with the myriad processes and methods within the AI model life cycle, from data cleaning to deployment. This section also has information about different model architectures, like diffusion models and transformers (the T in GPT).
Models. What’s the result of all the work done to build AI? Like a gossip column during fashion week, it’s all about the models. Here we profile some notable AI models, with a slight bias toward the world of Language AI.