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AI Glossary

This glossary provides clear and concise definitions for common terms related to artificial intelligence (AI). It's important to note that definitions in the field of AI are not universally agreed upon, can change quickly, and are often interconnected. AI combines three disciplines—math, computer science, and cognitive science—to mimic human behavior through various technologies. This glossary categorizes terms by subject area, labels them by their level of complexity (beginner, intermediate, advanced), and offers examples to illustrate the meaning of more complex terms. Cross-references are also provided between related terms.

Types of AI


Before diving into specific terms, it’s helpful to understand the different types of AI based on their capabilities:


  • Artificial Narrow Intelligence (ANI): This is the only type of AI that exists today. ANI systems are designed to perform specific tasks and cannot operate outside of their defined functions. Examples include image recognition software, spam filters, and virtual assistants like Siri.

  • Artificial General Intelligence (AGI): AGI is a theoretical concept where AI systems possess human-level intelligence and can learn and perform any intellectual task that a human being can.

  • Artificial Superintelligence (ASI): ASI is a hypothetical AI that surpasses human intelligence in all aspects. This type of AI could potentially solve complex global challenges or even pose an existential threat to humanity.

General AI Terms


















































































































































































































































































Term Complexity Definition Example Cross-references
Artificial intelligence (AI) Beginner The ability of a computer or a robot controlled by a computer to perform tasks that are usually done by humans because they require human intelligence and discernment. Self-driving cars, medical diagnosis, and online customer service. Machine learning, Deep learning
Algorithm Beginner A set of rules or instructions given to an AI, computer, or other device to help it solve a problem or perform a task. A recipe for baking a cake or a set of directions for navigating to a destination.
Agent Intermediate An entity that can perceive its environment and take actions to achieve goals. A self-driving car that perceives its surroundings through sensors and takes actions to navigate safely.
Artificial general intelligence (AGI) Intermediate A theoretical type of AI that has the ability to understand, learn, and perform any intellectual task that a human being can. AGI could potentially perform tasks such as writing a novel, composing a symphony, or designing a new scientific experiment. Artificial Superintelligence (ASI)
Artificial Superintelligence (ASI) Advanced A hypothetical AI that surpasses human intelligence in all aspects. ASI could potentially solve complex global challenges, develop new technologies beyond human comprehension, or even pose an existential threat to humanity. Artificial general intelligence (AGI)
AI ethics Beginner The moral principles and considerations surrounding the development and use of AI in a responsible and ethical manner. Ensuring AI systems are fair, unbiased, and respect human values.
AI safety Beginner A field focused on ensuring that AI systems operate safely and reliably, minimizing potential risks and unintended consequences. Developing safeguards against AI systems malfunctioning or causing harm.
AI washing Beginner The practice of misleadingly portraying a product or service as AI-powered when it is not, or exaggerating its AI capabilities. A company claiming its basic software uses AI when it only uses simple rule-based automation.
Explainable AI (XAI) Intermediate AI systems that are designed to be transparent and understandable to humans, allowing users to comprehend how the AI arrives at its decisions. An XAI system for medical diagnosis that provides clear explanations for its diagnoses, increasing trust and allowing doctors to validate the AI’s reasoning.
Bias Beginner Systematic errors or prejudices in AI systems that can lead to unfair or discriminatory outcomes. An AI system used for loan applications might unfairly deny loans to people from certain demographic groups due to biases in the training data. Algorithmic bias
Algorithmic bias Intermediate Unfairness that can arise from problems with an algorithm’s process or the way the algorithm is implemented, resulting in the algorithm inappropriately privileging or disadvantaging one group of users over another group. Algorithmic biases often result from biases in the data that has been used to train the algorithm, which can lead to the reinforcement of systemic prejudices around race, gender, sexuality, disability, or ethnicity. Bias
Alignment Intermediate Ensuring that the goals and actions of an AI system are in line with human values and intentions. Designing AI systems that prioritize human well-being and avoid unintended harmful consequences.
Anthropomorphism Intermediate The attribution of human-like qualities to AI systems, even though they do not possess consciousness or emotions. Describing an AI chatbot as “feeling happy” or “thinking creatively.”
Emergent Behavior Advanced Unexpected or unintended capabilities that arise from the complex interactions within an AI system. An AI language model developing the ability to translate languages without being explicitly trained to do so.
Hallucination Intermediate Outputs generated by AI systems that deviate significantly from reality or are not supported by the input data. An AI image generator creating an image with impossible features or an AI chatbot providing factually incorrect information.
Prompt Beginner An instruction or input provided to an AI system to guide its response or generate specific outputs. Instructing an AI image generator to “create an image of a cat wearing a hat” or asking an AI chatbot “what is the capital of France?”
Prompt Engineering Beginner The process of designing and refining prompts to elicit desired responses from AI systems. Experimenting with different phrasing, keywords, and formats to improve the quality and relevance of AI-generated outputs.
Token Intermediate The smallest unit of text that an AI model processes and understands. In the sentence “The cat sat on the mat,” the tokens would be “The,” “cat,” “sat,” “on,” “the,” and “mat.”
Context Window Intermediate The maximum number of tokens that an AI model can process and consider simultaneously when generating a response. If an AI model has a context window of 1024 tokens, it can only “remember” and utilize information from the previous 1024 tokens in the conversation.
Parameters Intermediate Numerical values that determine the structure and behavior of an AI model. The weights and biases in a neural network are examples of parameters.
Hyperparameter Tuning Advanced The process of selecting the appropriate values for the hyperparameters of a machine learning model.
Inference Intermediate The process of using a trained AI model to make predictions or generate outputs based on new input data. Using a trained image classification model to identify objects in a new image.
AI Copilot Beginner An AI-powered virtual assistant that works alongside users in various applications, providing assistance with tasks such as writing, coding, and decision-making. Microsoft Copilot in Microsoft Office applications.
Plugin Beginner A software component that adds specific functionalities to an AI system, allowing it to interact with other software or services. A plugin that enables an AI chatbot to access real-time weather information or retrieve data from a database.
Agentic AI Intermediate AI systems that can make decisions and take actions independently to achieve objectives set by humans. An AI system that manages a smart home by automatically adjusting the temperature, lighting, and security based on the homeowner’s preferences.
Agentic Process Automation (APA) Advanced The use of AI agents to automate complex business processes, enabling greater efficiency and flexibility. An AI agent that handles customer service inquiries, processes orders, and manages inventory.
AI Workforce Intermediate A team of AI agents working together to perform tasks or automate processes. A group of AI agents that collaborate to analyze financial data, generate reports, and make investment recommendations.
Workplace 5.0 Beginner A vision of the future workplace where humans and AI collaborate seamlessly to achieve greater productivity and innovation. A workplace where AI assistants help employees with routine tasks, freeing them to focus on more creative and strategic work.
AI Orchestration Intermediate The process of coordinating and managing multiple AI agents or systems to work together effectively. Using a central platform to control and monitor the actions of various AI agents involved in a complex business process.
AI Assist Beginner An AI-powered system that supports users by understanding queries, providing information, and performing tasks. AI-powered chatbots that assist customers with their inquiries.
AI Discovery Beginner The use of AI to analyze data and identify patterns, trends, and insights that would be difficult or impossible for humans to discover on their own. Using AI to analyze large datasets of scientific research to identify potential new drug targets.
AI Observability Intermediate The ability to monitor and understand the internal workings of AI systems, enabling developers to identify and address issues, improve performance, and ensure responsible use. Tools and techniques that provide insights into the decision-making processes of AI models.
AI Service Desk Beginner An AI-powered help desk that automates customer support tasks, such as answering questions, resolving issues, and routing inquiries to the appropriate personnel. An AI chatbot that handles common customer service requests, freeing up human agents to focus on more complex issues.
AI Search Beginner The use of AI to improve search results, making it easier for users to find the information they need. Search engines that use AI to understand the intent behind user queries and provide more relevant results.
Omnichannel AI Support Beginner Providing AI-powered support across multiple channels, such as chat, email, and social media, to ensure a consistent and seamless customer experience. An AI system that can answer customer questions and resolve issues regardless of the channel through which the customer contacts the company.
Universal Bot Beginner An advanced AI-powered chatbot designed to interact across multiple channels and platforms. A chatbot that can be used on a company’s website, mobile app, and social media platforms.
AI Privacy Beginner The protection of personal and sensitive data used by AI systems. Ensuring that data used to train and operate AI systems is collected, processed, and stored in a way that respects user privacy.

Machine Learning







































































Term Complexity Definition Example Cross-references
Machine learning (ML) Beginner A type of AI that allows software applications to become more accurate in predicting outcomes without being explicitly programmed to do so. Email spam filtering, product recommendations, and fraud detection. Supervised learning, Unsupervised learning, Reinforcement learning
Reactive Machine AI Intermediate AI systems with no memory, designed to perform a specific task based only on the current input. A chess-playing AI that analyzes the current board state to make a move without considering previous moves. Machine learning
Limited Memory AI Intermediate AI systems that can recall past events and outcomes to make decisions, but do not store these experiences permanently. A self-driving car that uses recent sensor data and a short history of its surroundings to navigate. Machine learning
Supervised learning Intermediate A type of machine learning where an AI system is provided with labeled data, meaning the data is tagged with the correct answers. Training an AI system to recognize handwritten digits by providing it with images of digits labeled with the correct number. Machine learning
Unsupervised learning Intermediate A type of machine learning where the AI system is given unlabeled data and must find patterns and relationships on its own. Clustering customers into different groups based on their purchasing behavior. Machine learning
Reinforcement learning Intermediate A type of machine learning where an AI system learns by trial and error, receiving rewards for correct actions and penalties for incorrect ones. Training an AI system to play a game by rewarding it for winning and penalizing it for losing. Machine learning
Data Mining Beginner The process of extracting useful information and patterns from large datasets. Using data mining techniques to identify customer segments with a high likelihood of purchasing a particular product. Machine learning
Double Descent Advanced A phenomenon in machine learning where model performance first improves, then worsens, and then improves again as model complexity increases. This phenomenon has implications for model selection and training, suggesting that sometimes increasing model complexity beyond a certain point can lead to better generalization. Machine learning

Deep Learning


















































Term Complexity Definition Example Cross-references
Deep learning Intermediate A subfield of machine learning that utilizes artificial neural networks with multiple layers to analyze data. Image recognition, natural language processing, and speech recognition. Artificial neural network, Machine learning
Artificial neural network (ANN) Intermediate A computing system inspired by the structure and function of the human brain, consisting of interconnected nodes (neurons) that process and transmit information. Used in image recognition, natural language processing, and other AI applications. Deep learning
Convolutional neural network (CNN) Advanced A type of neural network specifically designed for processing image data, using convolutional layers to extract features from images. Used in image classification, object detection, and facial recognition. Deep learning, Artificial neural network
Recurrent neural network (RNN) Advanced A type of neural network that can process sequential data, such as text or speech, by maintaining a hidden state that captures information from previous time steps. Used in natural language processing, machine translation, and speech recognition. Deep learning, Artificial neural network
Generative adversarial network (GAN) Advanced A type of neural network that consists of two networks, a generator and a discriminator, that compete against each other to generate realistic data. Used to generate images, videos, and other types of data. Deep learning, Artificial neural network

Natural Language Processing


















































Term Complexity Definition Example Cross-references
Natural language processing (NLP) Intermediate A branch of AI that focuses on enabling computers to understand, interpret, and generate human language. Chatbots, language translation, and text summarization. Natural language understanding (NLU), Natural language generation (NLG)
Chatbot Beginner A computer program designed to simulate conversation with human users, typically through text or voice interactions. Customer service bots on websites or virtual assistants like Siri and Alexa. Natural language processing (NLP)
Natural language understanding (NLU) Advanced A subfield of NLP that focuses on enabling computers to understand the meaning of human language. Sentiment analysis, question answering, and text classification. Natural language processing (NLP)
Natural language generation (NLG) Advanced A subfield of NLP that focuses on enabling computers to generate human-like text. Writing news articles, composing emails, and creating chatbot responses. Natural language processing (NLP)
Large language model (LLM) Intermediate A type of AI model trained on a massive amount of text data to perform various NLP tasks. LLMs have been instrumental in the recent advancements in generative AI, enabling the creation of sophisticated chatbots and text generation tools. GPT-3, BERT, and LaMDA. Natural language processing (NLP)

Computer Vision




































Term Complexity Definition Example Cross-references
Computer vision Intermediate A field of AI that enables computers to “see” and interpret images and videos. Computer vision plays a crucial role in enabling AI systems to perceive and understand the visual world, opening up possibilities in various domains like robotics, autonomous vehicles, and medical imaging. Object detection, image classification, and facial recognition. Image classification, Object detection
Image classification Intermediate The task of assigning a label to an image based on its content. Identifying whether an image contains a cat or a dog. Computer vision
Object detection Advanced The task of identifying and locating specific objects within an image. Detecting and locating cars in a traffic image. Computer vision

Conclusion



This glossary provides a foundation for understanding the terminology surrounding artificial intelligence. It is important to remember that AI is a rapidly evolving field, with new terms and concepts emerging constantly. The definitions and categorizations presented here may evolve as the field progresses. Continuous learning and a critical understanding of the ethical and societal implications of AI are essential for navigating this dynamic landscape.