AI Terms 101: An A to Z AI Terminology Guide for Beginners

Reading Time: 10 minutes

AI promises to transform a variety of industries including healthcare, manufacturing, and finance among others. But the technical vocabulary around artificial intelligence can be confusing to most business leaders. To help demystify AI terminology, we created this A to Z guide packed with must-know AI terms and simple examples.

AI Terminology Guide

Understanding the basics of AI terminology is critical for all business professionals Here are some core AI terms that you should familiarize yourself with.

A

• AI Algorithm – Step-by-step instructions for solving problems or accomplishing tasks using AI. Example: Recommendation algorithm suggests products based on user purchase history.
• Analytics – The analysis of data to draw insights and optimize decisions. Example: Web analytics to understand visitor behavior.
• Anthropic – Values alignment research to ensure beneficial AI systems. Example: Research for AI assistant safety.
• Artificial Intelligence – Simulation of human intelligence and learning capabilities within machines. Example: Game-playing through pattern recognition.
• Auto-Classification: Applying AI/ML to automatically categorize text faster and more accurately. Example: Sorting customer inquiries by topic.
• Autonomous: A machine that can perform tasks without needing human intervention. Example: Self-driving cars navigating roads without driver input.

B

• Backward Chaining: An AI reasoning method starting from a goal to determine if data supports achieving it. Example: Diagnosing diseases from symptoms.
• BERT: Bidirectional Encoder Representations from Transformers (BERT); Large scale pre-trained NLP model fine-tuned for downstream tasks. Example: Question answering application.
• Bias: Assumptions simplifying learning that may negatively impact model accuracy. Example: Facial recognition working better for some demographics.
• Big Data – Extremely large structured and unstructured datasets analyzed computationally to reveal patterns. Example: Massive datasets of healthcare records.
• Bots – An AI system designed to automate tasks previously requiring human labor. Example: Scheduling assistant bot.
• Bounding Box: An imaginary box drawn on images to delineate objects to recognize. Example: Identifying cars in drone footage.

C

• Custom Language Model: AI model specialized for an organization/industry’s language use. Example: Banking chatbot understanding financial terminology.
• Chatbot – An AI system designed to converse with humans using natural language. Example: Virtual concierge answers hotel guest questions.
• Classification – Categorizing input data into specific groups or classes. Example: Identifying spam emails.
• Cloud Computing – Delivering computing services including storage and processing power over the internet. Example: On-demand access to AI-powered tools.
• Cognitive Map: Mental representation encoding environment information to aid memory and reasoning. Example: Spatial awareness navigating rooms mapped mentally.
• Composite AI: Combined AI techniques improving efficiency and applicability. Example: Computer vision and NLP for automated video tagging.
• Computational Learning Theory: Field focused on analyzing machine learning algorithms. Example: Studying neural network model optimization.
• Conversational AI: Building chatbots and assistants using natural language interactions. Example: Virtual customer service agent.
• Convolutional Neural Networks: Deep learning for processing grid-like topology data like images. Example: Facial recognition.
• Corpus: Large collections of textual or spoken language data to train AI models. Example: Massive database of customer support transcripts.
• Cognitive Computing – Human-like pattern recognition and analysis powers smart decisions. Example: Understanding speech commands.
• Computer Graphics – Enables visual scene and object rendering digitally. Example: 3D animation in modern movies.
• Computer Vision (CV): Automated processing and analysis of visual inputs like images/video. Example: Identifying faces in photos.

D

• Data Discovery: Uncovering and delivering data insights to users. Example: Analytics revealing sales opportunities.
• Data Drift: Changes in input data distribution over time. Example: Demographic shift among customer base.
• Data Ingestion: Importing data from diverse sources into a unified format. Example: Central data lake.
• Data Labeling: Adding metadata tags to data to train AI models. Example: Image tagging for computer vision.
• Data Mining – Techniques to identify hidden predictive patterns in large datasets. Example: Discovering new correlation in cancer data.
• Data Scarcity: Insufficient data to power accurate analytics. Example: Rare disease detection limits.
• Data Science – Extraction of insights from structured and unstructured data. Example: Personalized medicine discoveries.
• Dataset: Structured collection of related data points. Example: Image recognition model training on labeled images.
• Deep Learning – Advanced machine learning using neural networks, inspired by human brains to learn complex tasks. Example: Natural language translation apps.
• Did You Mean (DYM): Search function suggesting alternatives for inaccurate queries. Example: Fixing typos automatically.
• Disambiguation: Clarifying terms with multiple meanings. Example: Distinguishing bank for finance vs. river edge.

E

• Edge Model: Analytics model leveraging data from user devices/sensors. Example: Real-time shopper behavior analysis.
• Emotion AI: Detecting user moods via interactions to personalize responses. Example: Empathetic chatbot conversations.
• Entity Annotation: Labeling unstructured text with tags identifying key info categories like people and locations. Example: Extracting personal data from privacy policies.
• Entity Extraction: Adding structural labels or tags to unstructured data to enable machine reading. Example: Identifying relevant data like dates and places within documents.
• ESG (Environmental, social, and governance): Societal impact/sustainability metrics for organizations. Example: Environmental friendliness ranking.
• ETL (Extract, transform and load): Extracting data entities, like people, places from unstructured text, and then loading into a data warehouse. Example: Identifying experts/locations in documents to tag for search.
• Expert System – AI system to match or exceed specialized human-level domain expertise through reasoning over knowledge. Example: Medical diagnosis.
• Extraction: Identifying key topics in text. Example: Determining document themes.

F

• F-score: Also called a F1-score. This is the measure of a model’s accuracy on a dataset. Example: Spam detection filter assessment.
• Facial Recognition – Identifying human faces accurately in images or videos via biometrics. Example: Unlocking phones with face authentication.
• Fine-tuned Model: Specialized model for specific context/data categories. Example: Industry-specific procurement analysis.
• Forward Chaining: Reasoning from existing data towards conclusions. Example: Inferring credit risk from financial transaction history data.
• Foundational Model: Broadly trained baseline model for general tasks. Example: BERT language model.

G

• Generative AI: Algorithms creating new artifacts modeled after training data. Example: AI art generating images.
• General AI: AI possessing general intelligence comparable to humans across intellectual tasks. Example: A computer that can write novels, prove mathematical theorems and diagnose diseases as well as a talented human.
• Generalized Model: Model aimed at general rather than specialized tasks. Example: Multilingual translator.
• Generative AI – AI algorithms that create new data matching patterns in training data. Example: Deepfakes.
• Generative Summarization: Condensing long text into concise overviews using LLMs. Example: Email thread summaries.
• Grounding: Rooting generative applications’ output in factual sources to ensure accuracy. Example: Citing references.

H

• Hallucination: Inaccurate fabricated details in generative text. Example: Fake historical figure “quotes”.
• Hyperparameters: Adjustable model settings optimized for performance. Example: Neural network learning rate.
• Human-AI Collaboration – Combining strengths of humans and AI drives enhanced outcomes. Example: Doctors using AI for improved diagnosis.
• Hybrid AI: Combined symbolic and machine learning techniques. Example: Expert rules refine stock trading algorithms.
• Hyperparameter: Variables controlling model learning processes set manually outside the model. Example: Learning rate affecting speed and quality of training neural networks.

I

• Inference Engine: Logic system component applying rules to deduce insights. Example: Recommending products based on purchase history.
• Intelligent Agent – An autonomous AI system acting on behalf of users across networked systems. Example: AI avatar assistant interface.
• Intelligent Document Processing: Automated extraction and processing of usable info from documents. Example: Populating databases from scanned invoices.
• Intent: Goal or purpose label in natural language data. Example: Understanding “decrease volume” intent from voice command.
• Insight Engine: Search, analytics and knowledge discovery combined. Example: Enterprise data analysis/exploration.

J

• Job Displacement – Loss of human jobs/tasks due to increasing AI automation. Example: Truck driver jobs threatened by autonomous big rigs.

K

• Knowledge Graph: A knowledge base that uses a graph structure to connect entities and their relationships. Example: A knowledge graph could connect Tom Cruise, Top Gun: Maverick, Joseph Kosinski (Director), and Paramount Pictures or relate medical symptoms to diseases.
• Knowledge Engineering: Capturing human knowledge to model AI systems. Example: Encoding medical logic for diagnosis assistant.

L

• Label: Desired output identifying correct classification for input data when training supervised models. Example: Spam/not spam tags on emails.
• Language Models – Mapping statistical relationships between words and phrases in human languages. Example: Predictive text suggestions.
• Large Language Models (LLM): Massive neural networks trained on diverse text data to generate language. Example: GPT-3 autocompleting sentences.
• Linguistic Annotation: Adding lexical or syntactic tags to text data for training NLP algorithms. Example: Part-of-speech and named entity tagging.
• Logic Programming – Solving problems declaratively by specifying rules and facts allowing finding solutions automatically through logical inference. Example: Automated airline flight booking considering restrictions.

M

• Machine Learning (ML): Field of AI focused on algorithms that can learn from data to improve at tasks without explicit programming. Example: Product recommendations.
• Machine Learning Model (MLM): Mathematical model enabling machines to learn behaviors from data to make predictions or decisions. Example: Predictive analytics forecasting sales numbers.
• Machine Translation: Automated translation of languages using AI. Example: Real-time video subtitles.
• Machine Intelligence: Collective term for machine learning and AI training algorithms. Example: Using neural networks for facial recognition demonstrates machine intelligence.
• Model: Output of running a machine learning algorithm on data, used to make predictions. Example: Predictive sales forecasting model.
• Model Parameter: Variables learned from data tuning model performance. Example: Neural network connection weights.
• Morphological Analysis: Breaking problems into basic elements to understand solutions. Example: Linguistics examining word roots.
• Multimodal Models: Models processing multiple data types like text, images and audio. Example: Video analysis extracting embedded text and visuals.
• Multitask Prompt Tuning: Adapting prompts to allow variables for repetitive queries. Example: Changing only name/age in chatbot customer data questions.

N

• Natural Language Generation (NLG) – Automating written or spoken language production. Early executions with weak NLG technology was criticized for its poor output. Example: Generating real estate listing descriptions from structured data or converting data into coherent sentences in finance reports.
• Natural Language Processing (NLP) – Processing and analyzing human languages using AI algorithms. Example: Sentiment analysis classifying movie reviews or chatbots answering customer questions.
• Natural Language Query: Spoken question input without non-language characters. Example: “What is the weather forecast today?” voice search query.
• Natural Language Understanding (NLU): Enabling machines to derive meaning from natural language, understanding nuance. Example: Determining subtle sentiment from product reviews.
• Natural Language Technology: Combined disciplines enabling computers to process natural language. Example: Chatbots use NLP, NLU and NLG.
• Neural Network – Computing system modeled on the neural structure of animal brains, enabling learning skills from data patterns without task-specific programming. Example: Computer vision identifying objects in images or analyzing medical scans..

O

• Ontology – Formally representing knowledge around concepts, definitions, properties and interrelationships for a domain of interest. Example: Finding relationships between medical symptoms and diseases.
• Overfitting: Model over-optimized to training data fails to generalize to new data. Example: Fraud detection trigger false positives.

P

• Parameter: Variable learned from data enabling model tuning to improve performance. Example: Weights in neural networks.
• Parsing: Assigning linguistic labels and logical roles to text elements. Example: Recognizing subjects and objects in sentences to determine meaning.
• Part-of-Speech Tagging: Labeling words by grammatical function (noun, verb, adjective etc.). Example: Distinguishing nouns from verbs and tagging words in the sentence, “David Hasselhoff posed shirtless for a photo with puppies.”
• Pattern Recognition: Identifying trends and patterns in data sets. Example: Bioinformatics analysis detecting gene sequencing patterns related to disease risk.
• Plugins: Extending functionality via add-ons. Example: Adding ability to book flights in conversational interface.
• Post Edit Machine Translation: Human correction of machine authored translations. Example: Reviewing and revising software-generated Spanish version of an article originally written in English.
• Post-processing: Refining crude software outputs. Example: Correcting grammar of AI-written paragraphs.
• Pre-processing: Cleaning and formatting data before analysis. Example: Converting unstructured text documents into structured data records.
• Pretrained Model – A model already trained on large datasets for reuse and further training. Example: Using BERT as a pretrained model for question-answering.
• Pretraining – Initial unsupervised training of a foundation model to give general capabilities. Example: Pretraining GPT-3 on Common Crawl.
• Precision: Fraction of results that are correct out of all returned. Example: Percentage of identified topics actually present in documents.
• Predictive Analytics: Using data and AI to forecast future outcomes. Example: Predicting demand for retail products.
• Prompt – Phrase used to query a foundation model. Example: “Once upon a time, there were” is a prompt for GPT-3 to begin a fairy tale.
• Prompt Chaining – Using multiple prompts to iteratively refine an LLM’s output. Example: Adding “The story takes place in space” to further constrain a sci-fi prompt.
• Prompt Engineering – Crafting effective prompts for LLMs through experimentation. Example: Trying out prompts with different styles, genres or points-of-view.

R

• Random Forest – An ensemble ML algorithm using multiple decision trees. Example: Classifying images of animals in a random forest model.
• Recall – Percentage of correct results returned out of total expected. Example: Retrieving 5/10 expected dog breeds gives 50% recall.
• Recommendation System – Uses AI to predict items or actions a user may want. Example: Product suggestions based on purchase history.
• Recurrent Neural Network – A neural network allowing previous outputs as inputs, useful for sequence tasks. Example: Process text one token at a time using memory in a RNN.
• Reinforcement Learning – Agents learn optimal strategy through trial and error guided by a reward-based system. Example: Google DeepMind’s AI system, AlphaGo, mastering the ancient game of Go.
• Responsible AI – Ethical development and use of AI models. Example: Ensuring transparency, explainability and lack of bias.
• Retrieval Augmented Generation – Improving LLM output by retrieving knowledge from additional corpora. Example: Providing accurate factual information to QA models.
• Robotics – Field of engineering focused on building machines capable of autonomous movement and tasks. Example: Warehouse inventory robot.
• Robotic Process Automation (RPA) – Automating repetitive digital tasks using scripted software bots instead of employees. Example: Processing insurance claims.

S

• Semantics – Study of meaning of words and how sentences are understood.
• Semantic Annotation: Adding contextual tags to content to improve relevance for searches. Example: Linking products in e-commerce store to buyer persona groups.
• Semantic Network – Concepts connected by semantic relationships. Example: Identifying John is the father of Mary.
• Semantic Search – Understanding meaning and relationships behind search keywords. Example: Matching intent rather than just keywords.
• Sentiment Analysis – Understanding emotional tone and attitudes in text. Example: Classifying social media brand mentions as positive, negative or neutral.
• Speech Recognition – Converting spoken language into text. Example: Automatically transcribing a phone call into text or powering voice assistants like Siri and Alexa.
• Strong AI: Futuristic AI possessing general human-level intellectual abilities. Example: A computer that can wholly pass itself off as human, known as “artificial general intelligence” (AGI).
• Supervised Learning: Training models on input data matched with desired output labels. Example: Image classifiers trained with cat and dog pictures or email spam detection filters.
• Symbolic AI – Rule-based linguistic pattern matching approach for NLP. High precision but more brittle.
• Syntax – Rules for arranging words and phrases to create meaning.
• Swarm Intelligence – Coordinating large numbers of simple agents exhibiting communal behaviors to solve problems. Example: Modeling flow of pedestrians during building evacuations.

T

• Taxonomy – A hierarchical organization of concepts into subclasses. Example: Animal taxonomy with kingdom, phylum etc.
• Test Data: Unlabeled input data for evaluating trained model performance. Example: New spam emails to assess spam detection filter accuracy.
• Text Analytics – Automatically extract insights from textual data using NLP. Example: Analyze survey responses to identify key themes.
• Text Generation – Automating coherent natural language text production. Example: Corporate earnings report summaries.
• Text Summarization – Automatically generating shorter summaries. Example: Summarizing a long news article into key points.
• Thesauri: A resource “dictionary” of word and phrase meanings describing relationships aiding in semantic processing.
• Tokens – Individual words used to compose a sentence.
• Training Data: The examples used to train a machine learning model. Example: Historic emails labeled as spam/not spam to train spam classifier.
• Transfer Learning: Applying knowledge gained solving one problem to improve learning on related problems. Example: Use image recognition trained on animals to better identify plant species.
• Tree Map – Visualization showing hierarchical data as nested rectangles.
• Turing Test: Benchmark for AI to exhibit behavior indistinguishable from a human in conversation. Example: An AI fooling people it is human via text chat.

U

• Unsupervised Learning – Algorithms find hidden patterns and intrinsic structure in unlabeled input data without human supervision. Example: Customer segmentation.
• Unstructured Data – Data without a predefined structure, like text or images. Requires NLP and computer vision to interpret.

V

• Validation Data: Labeled data for validating model performance during/after training. Example: Ensure model classifies previously unseen spam emails accurately.
• Variance: Susceptibility of model functioning to change during training. High variance risks overfitting. Example: Stock price predictor overly swayed by recent volatility.
• Variation: Ways people phrase utterances to achieve the same intent. Example: “Please turn down volume”, “Make it quieter”.
• Virtual Agent – An AI system designed to communicate with humans to provide customer service support or sales assistance. Example: Bank chatbot handles common questions to serve customers anytime.
• Virtual Reality – Computer-generated 3D simulated environments allowing immersive user experiences through VR headsets. Example: Architectural walkthroughs of building designs.
• Voice Recognition – Identifying individuals by unique voiceprints. Example: Voice authentication access controls.

W

• Weak AI – AI systems focused narrowly on specific tasks using data without general human-level intelligence. Example: Chess programs.
• Web Scraping – Automatically collecting structured web data. Example: Comparing product prices across retailers.

Helpful AI Terms From LLM to AGI

Artificial intelligence is already powering innovations across industries through techniques like machine learning, neural networks, computer vision and natural language processing.

AI terminology can be overwhelming, but by starting with a solid grasp of AI terms, anyone can better understand the technology unlocking our intelligent future.

What AI acronyms and terms did we forget to include on this list? Please let us know in the comments.

Glossary of AI Terms: AI terms that are essential to building and expanding your knowledge... #aiterms #ai101 #aiglossary #ai Share on X