AI Glossary
Acoustic ModelsActivation FunctionsAdaGradAI AlignmentAI Emotion RecognitionAI GuardrailsAI Speech EnhancementArticulatory SynthesisAttention MechanismsAuto ClassificationAutoregressive ModelBatch Gradient DescentBeam Search AlgorithmBenchmarkingCandidate SamplingCapsule Neural NetworkCausal InferenceClassificationClustering AlgorithmsCognitive ComputingCognitive MapComputational CreativityComputational LinguisticsComputational PhenotypingComputational SemanticsConditional Variational AutoencodersConcatenative SynthesisContext-Aware ComputingContrastive LearningCURE AlgorithmData AugmentationData DriftDeepfake DetectionDiffusionDomain AdaptationDouble DescentEnd-to-end LearningEvolutionary AlgorithmsExpectation MaximizationFeature Store for Machine LearningFederated LearningFew Shot LearningFlajolet-Martin AlgorithmForward PropagationGaussian ProcessesGenerative Adversarial Networks (GANs)Gradient Boosting Machines (GBMs)Gradient ClippingGradient ScalingGrapheme-to-Phoneme Conversion (G2P)GroundingHuman-in-the-Loop AIHyperparametersHomograph DisambiguationHooke-Jeeves AlgorithmIncremental LearningInstruction TuningKeyphrase ExtractionKnowledge DistillationKnowledge Representation and Reasoningk-ShinglesLatent Dirichlet Allocation (LDA)Markov Decision ProcessMetaheuristic AlgorithmsMixture of ExpertsModel InterpretabilityMultimodal AIMultitask Prompt TuningNamed Entity RecognitionNeural Radiance FieldsNeural Style TransferNeural Text-to-Speech (NTTS)One-Shot LearningOnline Gradient DescentOut-of-Distribution DetectionOverfitting and UnderfittingParametric Neural Networks Part-of-Speech TaggingPrompt ChainingPrompt EngineeringPrompt TuningQuantum Machine Learning AlgorithmsRandom ForestRegularizationRepresentation LearningRetrieval-Augmented Generation (RAG)RLHFSemantic Search AlgorithmsSemi-structured dataSentiment AnalysisSequence ModelingSemantic KernelSemantic NetworksSpike Neural NetworksStatistical Relational LearningSymbolic AITokenizationTransfer LearningVoice CloningWinnow AlgorithmWord 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.

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. 

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