61 AI Terms: A Glossary for Beginners


With the rapid advancements in technology, buzzwords like ‘machine learning’, ‘neural networks’, ‘augmented reality’, and ‘natural language processing’ are becoming increasingly common in our everyday conversations. But what do these terms really mean?

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Understanding the language of AI can sometimes feel like learning a whole new language. This is especially true if you’re just beginning your journey into this exciting field. It’s easy to get lost in the labyrinth of jargon, acronyms, and technical terms. That’s why I started this list, to help make sense of all of the terminology.

In this guide, I aim to demystify the jargon and break down the key terms and concepts in artificial intelligence. I’ve curated a comprehensive glossary that covers everything from the basics of AI and machine learning to more advanced concepts like genetic algorithms and swarm intelligence.

Whether you’re a student, a professional looking to transition into AI, or just a curious mind wanting to understand the technology that’s transforming our world, this glossary is for you. It’s designed to serve as a reference guide that you can return to time and again as you deepen your understanding of AI.

The landscape of AI is constantly evolving and expanding, making it a dynamic field of study and application. With that in mind, I’ll be updating this glossary from time to time to keep pace with these changes. So, make sure to revisit this page regularly to stay on top of the latest terms and concepts in the world of AI. And be sure to check out my article “What is AI? Unraveling the Intricacies of Artificial Intelligence” for a general discussion of the benefits and potential pitfalls of AI,

Glossary of AI Terms

AI Framework

An AI Framework is a platform or a collection of routines and tools that allow software developers to create and manage AI models. It simplifies the process of building, training, and using AI models. Examples include TensorFlow and PyTorch.

AI Hallucination

AI Hallucination refers to a phenomenon in which an AI system produces its own set of data or images without being given any external input. It is a form of generative AI, in which the AI creates new content based on data it has already seen. This content may not necessarily be meaningful or accurate, but it can provide valuable insights into how an AI system perceives the world.

Artificial Intelligence (AI)

AI refers to machines or software that mimic human intelligence, performing tasks that usually require human intellect, such as understanding natural language or recognizing patterns. AI can be categorized into two types: “Weak AI,” which is designed to perform a specific task (like voice recognition), and “Strong AI,” which performs any intellectual task that a human being can do.

AI Models

AI Models are the algorithms trained on data. They learn patterns from the data they are trained on and use these patterns to make predictions or decisions without being explicitly programmed to do so.


An Algorithm is a set of rules or instructions that a computer follows to solve a problem or achieve a specific outcome. In the context of AI, algorithms are used to create models that learn from data.

Augmented Reality (AR)

AR is a technology that overlays digital information on top of real world views. It enhances users’ perception of and interaction with the real world by combining it with virtual elements.

Automatic Speech Recognition (ASR)

Automatic Speech Recognition is a technology that uses algorithms to convert speech into written text. It’s a crucial component of AI meeting documentation software, serving as the initial step in transcribing the spoken content of a meeting.

Autonomous Decision-Making

Autonomous Decision-Making refers to systems that can make decisions and perform tasks without human intervention. These systems use AI and machine learning to analyze data and make decisions.


Bias in AI refers to errors or prejudices that can occur in the data, the design of the model, or the way the model makes decisions. These biases can lead to unfair or unrepresentative outcomes.

Big Data

Big Data refers to extremely large datasets that can be analyzed to reveal patterns, trends, and associations. These datasets are often so large and complex that traditional data processing applications can’t handle them.


Chatbots are AI programs designed to simulate conversations with human users. In customer service, chatbots are deployed to answer frequently asked questions, guide customers through various processes, and provide immediate responses, thus enhancing customer experience. In the realm of marketing, chatbots play a crucial role in engaging customers, gathering user information, delivering personalized recommendations, and even facilitating purchases, thereby transforming the way businesses interact with their customers.


Classification is a type of supervised learning where an AI model is trained to categorize data into predefined classes or labels based on input features.

Code Generation

Code Generation, in the context of AI, refers to the automatic writing of computer programs by AI systems. This can involve translating high-level programming languages into machine code or generating code based on specific requirements or designs.

Cognitive Computing

Cognitive Computing is a subset of AI that aims to mimic human thought processes and help to solve complex problems without human intervention. It involves self-learning systems that use data mining, pattern recognition, and natural language processing.

Computational Learning Theory

Computational Learning Theory is a subfield of AI that focuses on the design and analysis of machine learning algorithms. It involves mathematical and computational analysis to understand the principles of learning in machines and humans.

Computer Vision

Computer Vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. In AI video editing and production software, computer vision can be used to automatically identify and categorize elements in a video, such as people, objects, scenes, and actions.

Data Analytics

Data Analytics is the process of examining, cleaning, and transforming data to discover useful information, draw conclusions, and support decision-making.

Data Labeling

Data Labeling is the process of tagging or annotating data, such as images or text, to make it usable for machine learning. Labels are the “answers” or “outcomes” that the AI model should aim to predict or classify.

Data Mining

Data Mining is the process of discovering patterns in large datasets using methods like machine learning, statistics, and database systems. It’s used to extract valuable information and insights from data.

Data Preprocessing

Data Preprocessing is the process of cleaning and transforming raw data into a format that AI models can understand. The process might involve dealing with missing values, normalizing data, or encoding categorical variables.

Datasets and Data Mining

Datasets are collections of data that AI models learn from. They consist of many examples, each with features (the input) and usually a label (the desired output). Data mining, as described above, is the process of extracting valuable insights from these datasets.

Decision Trees

Decision Trees are a type of machine-learning algorithm that make decisions based on certain conditions. They can be used in project management software to guide decision-making processes, such as resource allocation or risk management, based on predefined rules and criteria.

Deep Learning

Deep Learning is a subset of machine learning that involves neural networks with many layers (hence “deep”). These models are particularly good at learning from unstructured data like images or text. Deep learning is used in AI video editing to enable advanced features like automatic color grading, smart cut, and scene recognition. 


Deepfake is a technique that uses deep learning to create convincing fake images or videos. This is often used to superimpose one person’s face onto another’s body in a video or to generate synthetic voices.

Expert System

An Expert System is a computer system that mimics the decision-making ability of a human expert. They are designed to solve complex problems by reasoning about knowledge, represented mainly as if–then rules.

Face Recognition

Face Recognition is a technology capable of identifying or verifying a person’s identity by comparing and analyzing patterns based on the person’s facial contours.

Feature Extraction

Feature Extraction is a process of dimensionality reduction by which an initial set of raw data is reduced to more manageable groups for processing. It transforms the data in the high-dimensional space to a space of fewer dimensions.

Generative Adversarial Networks (GANs)

GANs are a class of artificial intelligence algorithms used in unsupervised machine learning. They were invented by Ian Goodfellow and his colleagues in 2014. GANs can generate new content that is nearly identical to or completely different from the original data it was trained on.

A notable use of GANs is in AI image creation, where they can generate highly realistic images from scratch. These AI-created images can range from altered real-world images to completely new and original artworks. In video editing, GANs can be used to create new video content or modify existing content, such as changing the lighting or weather in a scene or even creating deepfake videos. It’s important to note that while GANs can produce stunning results, they also raise ethical considerations, especially when used to create deepfakes.

Generative Models

Generative Models are a type of statistical model often used in machine learning that is capable of generating new data that is similar to the training data. In the context of AI for computer programming, generative models can be used to generate new code based on the patterns and structures learned from existing code.

Generative Pre-trained Transformer (GPT)

GPT is a state-of-the-art language model developed by OpenAI. This advanced AI model uses machine learning techniques to generate text that closely mirrors human writing. It’s trained on a diverse range of internet text but can also be fine-tuned with specific datasets for various tasks. The model predicts the probability of a word given the previous words used in the text, allowing it to generate coherent and contextually relevant sentences. From drafting emails to writing code or creating written content, GPT has wide-ranging applications.

Genetic Algorithms

Genetic Algorithms are a type of optimization algorithm inspired by the process of natural selection. They generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.


Hyperparameters are the variables that determine the network structure (e.g., the number of hidden units in the neural network) and the variables which determine how the network is trained (e.g., the learning rate).

Image Recognition

Image Recognition is the ability of an AI system to identify objects, places, people, writing, and actions in images.

Intelligent Process Automation (IPA)

Intelligent Process Automation combines methods from artificial intelligence and process automation to automate and optimize complex business processes. In project management, IPA can automate routine tasks, like scheduling meetings or updating task statuses, freeing up project managers to focus on more strategic aspects of the project.

Large Language Models (LLM)

Large Language Models, such as GPT, are AI models that have been trained on a large amount of text data. They can generate human-like text and are used for tasks such as translation, question answering, and summarization.

Machine Learning

Machine Learning is a subset of AI that gives computers the ability to learn from and improve upon experience without being explicitly programmed. It uses statistical techniques to learn patterns in data and make predictions or decisions. In project management software, Machine Learning can be used to predict project outcomes based on historical data, identify risks early, or automate task assignments based on team members’ skills and workload.

Machine Learning Platform

A Machine Learning Platform is a comprehensive solution that provides tools for AI professionals to develop, deploy, and monitor machine learning models. It might include data preprocessing tools, model training tools, and analytics dashboards. 

Some examples include:

  • TensorFlow: An open-source platform developed by Google Brain Team, widely used for creating deep learning models.
  • PyTorch: An open-source machine learning library developed by Facebook’s AI Research lab, popular for research and prototyping.
  • Amazon SageMaker: Amazon’s fully managed service for building, training, and deploying machine learning models.
  • Microsoft Azure Machine Learning: Microsoft’s cloud-based platform that provides a wide range of tools for machine learning.
  • IBM Watson Studio: IBM’s machine learning platform for data scientists, application developers, and subject matter experts.
  • Google Cloud AutoML: Google’s product suite that enables developers with limited machine learning expertise to train high-quality models.
  • DataRobot: A platform that automates the end-to-end process for building, deploying, and maintaining artificial intelligence and machine learning at scale.

The best platform depends on the specific needs and constraints of the specific project.

Natural Language Generation (NLG)

Natural Language Generation (NLG) is a branch of artificial intelligence (AI) focused on creating natural, human-like text or speech from data or structured information. It powers many familiar applications like Siri, Alexa, and Google Assistant. A key advancement in NLG is its use in AI writing applications. These tools can rapidly generate content such as articles or reports based on provided inputs or prompts and are valuable in fields like journalism, marketing, and customer service. However, while they augment content creation, they currently serve to complement, not replace, human creativity and editorial oversight.

Natural Language Processing (NLP)

Natural Language Processing is a field of AI that focuses on the interaction between computers and humans through natural language. The goal is to read, decipher, understand, and make sense of the human language in a valuable way.

Neural Language Models

Neural Language Models are a type of AI model that is used to predict the likelihood of a sentence or to suggest the next word in a sentence. They are often used in machine translation, speech recognition, and other natural language processing tasks.

Neural Networks

Neural Networks are a set of algorithms modeled loosely after the human brain, designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling, or clustering of raw input.


Overfitting happens when a machine learning model is too complex and learns not only the underlying patterns but also the noise in the training data. This leads to poor performance when the model is exposed to new, unseen data.


In the context of machine learning, parameters are the internal variables that the algorithm adjusts during learning to make accurate predictions. For example, the weights in a neural network are parameters.

Predictive Analytics

Predictive Analytics refers to the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. It’s all about providing the best assessment of what will happen in the future. For instance, in travel applications, predictive analytics could be used to forecast flight delays, predict crowd levels at popular tourist destinations, or suggest optimal travel times. In project management, predictive analytics can be used to forecast project timelines, estimate costs, or identify potential bottlenecks.

Recommendation Engine

A Recommendation Engine (also known as a recommender system) is a class of machine learning that predicts and suggests items or actions that users might like based on their past behavior or similar user profiles. For instance, recommending a movie on a streaming platform or a product on an e-commerce site. In the travel industry, a recommendation engine could suggest hotels, destinations, or activities that align with a traveler’s preferences and behaviors.

Reinforcement Learning

Reinforcement Learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a goal. The agent is rewarded or punished (reinforced) based on the consequences of its actions, hence the name.

Route Optimization Algorithm

Route Optimization Algorithm is a method used to determine the most cost-effective or quickest route from one place to another, considering various factors like distance, traffic, and time. In travel applications, this algorithm can be used to optimize travel routes and schedules.

Real-Time Analytics

Real-Time Analytics is the use of data and related resources for analysis as soon as it enters the system. In travel applications, real-time analytics can provide up-to-the-minute information on factors like flight status, traffic conditions, or weather updates.

Sentiment Analysis

Sentiment Analysis (also known as opinion mining) is the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information from source materials. Sentiment Analysis is used by a number of AI-driven meeting documentation programs to gauge the tone of the meeting participants.

Speech Recognition

Speech recognition is a technology under the umbrella of artificial intelligence that converts spoken language into written text. It’s the driving force behind many voice-activated systems, such as personal voice assistants and automated customer service systems. In the context of AI meeting documentation software, speech recognition is the technology that transcribes spoken words during a meeting.

Supervised Learning

Supervised Learning is a type of machine learning where the model is trained on a labeled dataset, i.e., a dataset where the target outcomes are already known. The model makes predictions based on this learned information.

Support Vector Machines (SVM)

Support Vector Machines are a type of supervised learning model used for classification and regression analysis. They are effective in high dimensional spaces and are versatile as different Kernel functions can be specified for the decision function.

Swarm Intelligence

Swarm Intelligence is a field of artificial intelligence based on the collective behavior of decentralized, self-organized systems. The concept is employed in work on artificial intelligence, and applications are typically found in swarm robotics.

Text Summarization

Text Summarization is an application of Natural Language Processing that involves condensing the source text into a shorter version, reducing the size of the original text while preserving key information and overall meaning. For AI meeting documentation software, this technology can create summaries of the transcribed meeting, highlighting key points and decisions.


Tokenization is a fundamental process in Natural Language Processing (NLP), a field of artificial intelligence. It involves breaking up text into smaller pieces called ‘tokens’, which can be as small as words or as large as sentences. These tokens help machines understand and analyze the text more effectively.

A key application of tokenization is AI grammar-checking tools. These tools use tokenization to separate text into individual words or sentences, which allows the software to analyze the grammar, context, and usage of each token, thereby identifying and correcting grammatical errors.

Transfer Learning

Transfer Learning is a machine learning technique where a pre-trained model is used on a new problem. It is an optimization that allows rapid progress or improved performance when modeling a new problem. Transfer Learning is useful in AI-produced computer programming because it allows a model trained on a large amount of code to be applied to a specific coding task, saving time and computational resources.

Unsupervised Learning

Unsupervised Learning is a type of machine learning where the model is trained on a dataset without labels. The model learns patterns in the data without any prior information about the outcomes.

Virtual Reality (VR)

Virtual Reality is a technology that creates a simulated experience that can be similar to or completely different from the real world. It is primarily used in gaming and immersive environments.

Voice Assistant

A Voice Assistant is a digital assistant that uses voice recognition, natural language processing, and speech synthesis to provide services and tasks for users. Examples include Amazon’s Alexa, Apple’s Siri, and Google Assistant.

Weak AI

Weak AI, also known as Narrow AI, is an AI system that is designed and trained for a particular task. It simulates human intelligence but doesn’t possess true intelligence. Examples include most of the AI applications we see today like recommendation systems or image recognition systems.

Workflow Automation

Workflow Automation is a designed sequence of automated actions for the steps in a business process. It is used to improve everyday business processes to optimize and simplify work.

Zero-shot Learning

Zero Shot Learning refers to the ability of a machine learning model to correctly infer or classify new, unseen instances based on knowledge learned from its training data without having seen any example of those during training.


We’ve now reached the end of our journey through this AI glossary, but remember, this is merely the start of your exploration into the fascinating world of artificial intelligence. I hope that this guide has offered you a solid foundation, enabling you to understand and engage with AI more deeply and confidently. From here, the possibilities are limitless.

Knowing this, I encourage you to use this newfound understanding to navigate the AI-infused world more easily and confidently. Whether it’s asking your voice assistant to play your favorite song, using a recommendation engine to find a new book, or simply understanding the privacy settings on social media platforms that employ AI, this knowledge can empower you to make the most of the technology surrounding you.

Artificial intelligence is already interwoven into our daily lives, and with this glossary, you can peel back the layers and better understand what’s happening behind the scenes. This way, you can feel more comfortable and secure using these AI-based tools, knowing you’re well-equipped to understand and control your interaction with them.

Remember, the world of AI is dynamic and constantly evolving, and keeping yourself familiar with the key terms and concepts can help you stay informed and make the best use of the technology. It’s not about becoming an expert but an informed user who can confidently navigate an increasingly AI-driven world.

I wish you all the best as you continue to explore and make sense of the AI landscape. Remember, the future of AI is in our hands, and every bit of knowledge counts. Keep learning, keep growing, and most importantly, keep questioning. Here’s to shaping the future of AI, one term at a time!


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