Ultimate AI Marketing Glossary (2024)

Tuck Ross
13 min readJan 20, 2024

The 115 AI Keywords and Terms You Need to Know

A glossary from another era. Credit: Midjourney

If you’re a marketer looking to jump into AI, hold up a sec. It’s a bit like anything else new; you gotta get the lingo down first. Prep before you prompt!

Think of it like learning a new language, where everything’s connected. You can’t really get what LLMs are all about without a grip on neural networks, right? And hey, don’t mix up ML with LLM or even a LLaMA or LaMDA — that’d be a bit awkward. Don’t worry, though, I’ve put together a no-nonsense glossary of all the AI terms you need to know. I needed this and started putting it together, so figured you might too. It’s like your personal AI cheat sheet, and I’ll keep it updated as new buzzwords pop up.

Here’s the top 10 AI terms everyone should know:

1. Artificial Intelligence (AI)

Imagine a sophisticated computer system mimicking human-like abilities such as understanding speech, making decisions, translating languages, gauging sentiments, and learning from experiences. That’s AI. Unlike a physical robot, AI refers to software algorithms that process vast data to automate complex tasks, usually needing human intellect. AI is often subtly integrated into our lives, aiding in tasks like text prediction, music recommendations, and tailored content based on preferences.

2. Machine Learning

Machine Learning (ML) is the engine driving AI. It’s a subset of AI where computers learn to recognize patterns and make predictions, akin to endless piano practice for flawless sight-reading. This learning involves repetitively processing data through algorithms, enhancing the system’s accuracy. The recent boom in digitized data and advanced computing power has fueled the emergence of powerful ML-driven tools like ChatGPT.

3. Large Language Models (LLMs)

LLMs are advanced AI systems trained on massive text datasets, enabling them to mimic human communication. They’re built on neural networks, inspired by the human brain’s structure, to recognize and reproduce language patterns. LLMs are used for tasks like translation, chatbot responses, summarizing, and creative writing. They’re continually refined to sound more natural and conversational.

4. Generative AI

Generative AI goes beyond analyzing data; it creates new, original content. It can produce images, music, text, and more by learning and replicating existing patterns. This technology has applications in art, storytelling, and even healthcare. However, its potential for generating misleading content is a concern, prompting efforts to distinguish AI-created materials.

5. Hallucinations (in AI)

AI systems, particularly generative ones, sometimes produce results based on inaccuracies, known as ‘hallucinations’ or ‘fabrications’. This occurs when AI misinterprets or creates false information, similar to mistaking shapes in the clouds for real objects. Addressing these inaccuracies involves ‘grounding’ the AI with reliable data sources for better accuracy.

6. Responsible AI

Responsible AI emphasizes the ethical creation and use of AI technologies. It covers safety, fairness, and bias mitigation at all levels, from the underlying model to the user interface. Recognizing that AI is shaped by imperfect human data, Responsible AI aims to reflect diverse societal values and reduce inherent biases.

7. Multimodal Models

Multimodal models are the ultimate multitaskers in AI, capable of processing different data types — images, sounds, and text — simultaneously. They blend this information to perform complex tasks, such as understanding queries about visuals.

8. Prompts

In AI, a prompt is a specific instruction in language, images, or code that directs the AI to execute a task. Just like ordering a customized sandwich, crafting effective prompts is essential to guide AI systems towards desired outcomes.

9. Copilots

AI copilots are digital assistants that enhance productivity in various applications. They assist with writing, coding, summarizing, and data analysis. Inspired by large language models, these copilots understand natural language and offer support while ensuring safety and ethical use. Like an airplane’s copilot, they supplement human decision-making but don’t take control.

10. Plugins

Plugins in AI are akin to smartphone apps, enhancing AI applications with additional capabilities without altering the core model. They enable AI systems to connect with other software, access new information, perform complex calculations, and more, effectively integrating AI into the broader digital ecosystem.

Want it all? Here’s the full list of 115 AI terms defined to get you current quick:

  1. Algorithm: A set of rules or instructions given to a computer to solve problems or perform calculations.
  2. Anthropomorphize: Attributing human characteristics or behavior to a god, animal, or object.
  3. Artificial Intelligence (AI): A field of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence. This includes tasks like decision making, object detection, speech recognition, and language translation.
  4. AI Art: Artwork created with the assistance of artificial intelligence.
  5. Artificial Neural Network (ANN): Computing systems inspired by biological neural networks, used to estimate or approximate functions.
  6. Augmented Reality (AR): An enhanced version of reality where live direct or indirect views of physical real-world environments are augmented with superimposed computer-generated images.
  7. Automatic Speech Recognition (ASR): Technology that can recognize spoken words, converting them into text.
  8. BERT (Bidirectional Encoder Representations from Transformers): A transformer-based machine learning technique for natural language processing pre-training.
  9. Bias (in AI): Prejudice in favor or against something, typically in a way considered to be unfair, reflected in AI based on its programming or the data it learns from.
  10. Big Data: Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.
  11. Chatbot: A software application used to conduct an online chat conversation via text or text-to-speech.
  12. ChatGPT: An AI model developed by OpenAI, specializing in generating human-like text based on the prompts it receives.
  13. Computer Vision: A field of AI that trains computers to interpret and understand the visual world.
  14. Convolutional Neural Network (CNN): A deep learning algorithm that can take an input image, assign importance to various aspects/objects in the image, and differentiate one from the other.
  15. Conversion Rate Optimization (CRO): The process of increasing the percentage of website visitors who perform a desired action.
  16. Copilots: In AI, copilots are assistant systems designed to help users with various tasks. They can provide guidance, support, and automation, often leveraging AI to facilitate interactions and decision-making processes.
  17. Customer Relationship Management (CRM): A technology for managing all your company’s relationships and interactions with customers and potential customers.
  18. Customer Segmentation: The practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing.
  19. DALL-E: An AI program created by OpenAI that generates digital images from natural language descriptions.
  20. Data Augmentation: A technique to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data.
  21. Data Mining: The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
  22. Data Privacy: The handling of data in a way that ensures confidentiality and privacy of the data subjects.
  23. Decision Tree: A decision support tool that uses a tree-like model of decisions and their possible consequences.
  24. Deep Learning: A class of machine learning algorithms that use several layers to progressively extract higher-level features from raw input.
  25. Deepfake: Synthetic media in which a person in an existing image or video is replaced with someone else’s likeness.
  26. DeepMind: An AI company and research lab.
  27. Diffusion (in AI): A process used in machine learning that involves spreading out or scattering nodes in a graph.
  28. Digital Twinning: The process of creating a digital replica of a physical entity.
  29. Emergent Behavior: Complex behavior that arises from simple rules in a system, not directed by an outside source.
  30. Entities and Entity Annotation: In NLP, entities are real-world objects such as names and places, and entity annotation is the process of tagging them in text.
  31. Ethical AI: AI that adheres to ethical guidelines and values.
  32. Explainable AI (XAI): AI in which the results of the solution can be understood by humans.
  33. Feature Engineering: The process of using domain knowledge to extract features from raw data.
  34. Generative Adversarial Networks (GANs): A class of machine learning frameworks designed by two neural networks contesting with each other.
  35. Generative AI: Refers to AI algorithms capable of generating new content. This can include text, images, music, and other forms of media. These AI systems don’t just analyze data but can also create new, original outputs.
  36. Generator (in AI): In GANs, the part of the network that generates new data instances.
  37. Google Bard: A conversational AI model developed by Google.
  38. GPT (Generative Pre-trained Transformer): An autoregressive language model that uses deep learning to produce human-like text.
  39. Grounding or Dynamic Grounding: The process of connecting words to real-world situations and contexts.
  40. Guardrails (in AI): Pre-defined rules or limits set in AI systems to ensure they operate safely and ethically.
  41. Hallucination (in AI context): In AI, particularly in the context of language models, hallucinations refer to instances where the model generates incorrect, nonsensical, or unrelated outputs. It’s a kind of error where the AI ‘imagines’ information not present in the input or training data.
  42. Human-in-the-Loop (HITL): A model of interaction where human judgment is integrated with the operation of AI systems. In this setup, humans are involved in training, tuning, or overseeing AI algorithms, ensuring that the system’s outputs are accurate, fair, and reliable.
  43. Hyperparameter: In machine learning, hyperparameters are the settings or configurations that govern the overall behavior of a machine learning algorithm. Unlike parameters, which are learned from the training data, hyperparameters are set prior to the learning process and can significantly influence the performance and effectiveness of the model.Image Recognition or Image Classification: The process by which an AI system identifies objects, places, people, writing, and actions in images.
  44. Image-Text Pairs: Used in machine learning, these are combinations of images and their corresponding textual descriptions which help train models to understand and generate descriptions for visual content.
  45. Internet of Things (IoT): A network of interconnected devices that collect and exchange data using embedded sensors.
  46. Large Language Model (LLM): A type of advanced machine learning model that processes and generates human-like text. These models are ‘large’ due to their extensive training on vast datasets of text and their ability to understand and generate nuanced language.
  47. Large Language Model Meta AI (LLaMA): A specific type of large language model known for its efficiency and ability to perform a variety of natural language tasks.
  48. Language Model for Dialogue Applications (LaMDA): Google’s AI model designed specifically for creating more natural and open-ended conversations.
  49. Machine Learning (ML): A subset of AI that involves the development of algorithms and statistical models that enable computers to improve their performance on a specific task with experience or data, without being explicitly programmed for that task.
  50. Machine Learning Bias: Systematic and unfair biases in machine learning algorithms, often arising from biases in training data or the way algorithms are written.
  51. Marketing Automation: The use of software and technology to automate marketing processes and tasks, enhancing efficiency and personalization.
  52. Multimodal Models: AI models that can process and understand multiple types of data input, such as text, images, and sound. These models integrate different modes of data to improve their understanding and interaction capabilities.
  53. Natural Language Generation (NLG): The use of AI to generate text or speech from structured data.
  54. Natural Language Processing (NLP): The ability of computers to understand, interpret, and respond to human language in a useful way.
  55. Natural Language Query (NLQ): A type of query in natural language, allowing users to interact with databases and AI systems using everyday language.
  56. Neural Networks: Computational models inspired by the human brain, used in AI to help machines learn from data.
  57. OpenAI: An AI research and deployment company, known for developing advanced AI models like GPT.
  58. Parameters (in AI): The internal elements of a model that the AI system learns through training, which dictate the output given for a particular input.
  59. Personalization (in marketing): Tailoring marketing messages and offers to individual customers based on their preferences and behavior.
  60. Plugins: Plugins in AI systems are additional modules or software components that can be added to an AI system to enhance its functionality or enable new features. They are often used to customize or extend the capabilities of AI applications.
  61. Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.
  62. Prompts: In the context of AI, a prompt is the input given to an AI system to elicit a response or output. For language models, prompts usually come in the form of text queries or statements.
  63. Rank (in AI context): The order or position of items in a list, determined by an AI algorithm based on relevance or importance.
  64. Reactive Machines (aka Reactive AI): AI systems that only react to current situations and do not retain memory of past experiences.
  65. Recall (in information retrieval): A measure of a system’s ability to retrieve all relevant instances from a data set.
  66. Reinforcement Learning (RL): A type of machine learning where an agent learns to make decisions by performing actions and receiving feedback on the results.
  67. Relevancy (in AI context): The degree to which the information returned by an AI system matches the user’s query or needs.
  68. Responsible AI: This term refers to the practice of designing, developing, and deploying AI with ethical considerations in mind. It involves ensuring fairness, transparency, accountability, and safety in AI systems.
  69. Robotics: The branch of technology that deals with the design, construction, operation, and application of robots.
  70. Safety (in AI context): Ensuring that AI systems operate without causing unintended harm.
  71. Seeds (in AI model training): Initial values set for the algorithm to start learning, affecting the randomness in training results.
  72. Self-aware AI: Hypothetical AI that has the ability to be conscious or aware of itself.
  73. Self-supervised learning: A type of machine learning where the system learns to predict part of the input data from other parts of the data.
  74. Semantic Analysis: The process of understanding the meaning and interpretation of words and sentences in context.
  75. Semi-supervised learning: Machine learning involving a small amount of labeled data and a large amount of unlabeled data.
  76. Sentient AI: Theoretical AI that possesses the capacity to experience feelings and consciousness.
  77. Sentiment Analysis: The process of computationally determining whether a piece of writing is positive, negative, or neutral.
  78. Sequence-to-sequence models (Seq2Seq): AI models that convert sequences from one domain (like text in one language) to another (like text in another language).
  79. Singularity (in AI context): A hypothetical future point where AI systems become smarter than human beings.
  80. Soft weights (in AI context): Weights in a neural network that are continuously updated during training.
  81. Stable Diffusion: A type of diffusion process used in machine learning to ensure stable training of models.
  82. Stochastic (in AI context): Involving a random probability distribution or pattern that can be statistically analyzed.
  83. Stochastic Parrot: A term for AI models that mimic human language by randomly generating text based on learned patterns.
  84. Stochasticity (in AI context): The quality of being randomly determined in AI processes.
  85. Structured Data: Data that is organized in a fixed field within a record or file, like in databases.
  86. Style Transfer (in AI context): The process of applying the style of one image to the content of another using AI.
  87. Super AI (Strong AI, Super-intelligence, Artificial Super-intelligence [ASI]): AI that surpasses human intelligence and capability.
  88. Supervised Learning: A type of machine learning where the model is trained on a labeled dataset.
  89. Temperature (in AI context): A parameter in AI models that determines the randomness in the generation of predictions.
  90. TensorFlow: An open-source software library for machine learning and artificial intelligence.
  91. Text Generation: The process of automatically creating text using machine learning, typically using models like GPT-3.
  92. Text Prompt: An input given to an AI model, usually in the form of text, to generate a specific output or response.
  93. Text-to-Image Generation: The process of creating images from textual descriptions using AI algorithms.
  94. Theory of Mind AI: AI that can understand and interpret human emotions, beliefs, and intentions.
  95. Tokenization: The process of converting text into smaller units (tokens), such as words or phrases, for processing in NLP.
  96. Tokens (in AI context): The smallest units of text, like words or phrases, used in NLP and machine learning.
  97. Toxicity (in AI context): Harmful or inappropriate content generated or propagated by AI systems.
  98. Training Data: Data used to train machine learning models to recognize patterns and make decisions.
  99. Transfer Learning: Using a pre-trained model on one task and applying it to a different but related task.
  100. Transferability (in AI context): The ability of features learned in one task to be useful in another task in machine learning.
  101. Transformer Model: A type of neural network architecture used primarily in the field of natural language processing (NLP).
  102. Transformers (in AI context): AI models that transform input data into a more useful output, commonly used in NLP.
  103. Turing Test: A test of a machine’s ability to exhibit intelligent behavior equivalent to or indistinguishable from that of a human.
  104. Uncanny Valley: A hypothesis in robotics and 3D computer animation stating that humanoid objects which imperfectly resemble actual human beings provoke uncanny or eerily familiar feelings.
  105. Underfitting (in AI context): A scenario where a machine learning model is too simple and fails to capture the complexity of the data.
  106. Unstructured Data: Data that does not have a predefined format or organization, making it difficult to process and analyze using conventional methods.
  107. Unsupervised Learning: A type of machine learning where the algorithm learns patterns from untagged data.
  108. Validation (in machine learning): The process of evaluating the performance of a model using a separate dataset not used in training.
  109. Value Alignment Problem: The challenge of ensuring that an AI system’s goals and behaviors are aligned with human values.
  110. Value Vector: In AI, a representation of values or weights in a vector form, often used in decision-making processes.
  111. Variants (in AI models): Different versions or adaptations of a basic AI model, adjusted for specific tasks or datasets.
  112. Variational Autoencoders (VAEs): A type of generative model in machine learning that focuses on unsupervised learning of complex data distributions.
  113. Voice Clones: AI-generated synthetic voices that closely mimic the sound and characteristics of a human voice.
  114. Weak AI (aka Narrow AI): AI systems designed and trained for a particular task.
  115. Weight (in neural networks): The strength or amplitude of a connection between two nodes in a neural network, influencing the network’s output.

Don’t see a word you know or were expecting? Let me know in comments.

Like this? Subscribe to the Marketing AI Playbook newsletter. Boost your marketing, accelerate revenue, and gain a competitive edge with curated news, insights, app reviews, and practical tips. Join the fastest growing marketing AI newsletter and maximize AI’s potential for your business. Subscribe now and harness the full power of AI in your marketing strategy.

Sign up here



Tuck Ross

Digital Marketing & Social Media Expert | Consumer Strategist | Public Speaker | Course Builder | Creator | Corporate Trainer ❤️