| Term | Definition |
|---|
| Adversarial Machine Learning | Techniques that make AI models more resilient by exposing them to deceptive or malicious inputs. |
| Accuracy | The percentage of correct predictions made by an AI model out of all predictions. |
| Algorithm | A step-by-step procedure or set of rules designed to perform a specific task or solve a problem. |
| Algorithmic Bias | When an algorithm produces prejudiced results due to biases in the training data or its design. |
| Algorithmic Output | The results generated by an algorithm, such as predictions, decisions, or created content. |
| Artificial General Intelligence (AGI) | AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence. |
| Artificial Intelligence (AI) | The field focused on creating machines capable of performing tasks that require human-like intelligence, such as reasoning and learning. |
| AI Ethics | The study of moral principles and guidelines to ensure responsible and fair development and use of AI technologies. |
| AI Frameworks | Software libraries and tools that facilitate the development of AI applications by providing pre-built components and standardized processes. |
| AI Literacy | The ability to understand and effectively use AI technologies, including knowledge of AI concepts, data handling, and ethical considerations. |
| AI Model Goodness Measurement Metrics | Criteria used to evaluate the performance and effectiveness of AI models, such as accuracy, precision, and recall. |
| AI Ops | The application of AI techniques to enhance and automate IT operations, including monitoring and problem resolution. |
| Automated Machine Learning (AutoML) | The process of automating the selection, training, and tuning of machine learning models to streamline AI system development. |
| Automation | Using technology to perform tasks with minimal human intervention based on predefined rules or processes. |
| Bias | Systematic favoritism or prejudice in AI systems that can lead to unfair or discriminatory outcomes. |
| Brute Force Search | A method of solving problems by exhaustively searching through all possible solutions until the correct one is found. |
| Chatbot | An AI-powered software application designed to simulate human conversation through text or voice interactions. |
| ChatGPT | A large language model developed by OpenAI that generates human-like text responses based on user inputs. |
| Citation | The practice of acknowledging sources of information or ideas used in content creation to give proper credit and avoid plagiarism. |
| Cognitive Computing | AI systems designed to mimic human thought processes, including learning and problem-solving, to enhance decision-making. |
| Computer Vision | A field of AI that enables machines to interpret and understand visual information from the world, such as images and videos. |
| Copyright | Legal rights granted to creators for their original works, preventing unauthorized use or reproduction by others. |
| Creative Commons License | A public license that allows creators to specify how others can use, share, and build upon their work under certain conditions. |
| Data Architect | A professional responsible for designing and managing an organization’s data infrastructure to support AI and data science initiatives. |
| Datafication | The process of converting various aspects of life into digital data that can be analyzed and utilized for different purposes. |
| Data Lake | A centralized repository that stores large volumes of structured and unstructured data in their native formats for analysis and processing. |
| Data Manager | An individual who oversees the acquisition, storage, and governance of data to ensure its quality and compliance with regulations. |
| Data Privacy | The protection of personal and sensitive information from unauthorized access or misuse, ensuring data is handled securely. |
| Data Scientist | A professional who analyzes and interprets complex data to extract insights, build models, and inform decision-making processes. |
| Dataset | A collection of related data organized for analysis and used to train, validate, or test machine learning models. |
| Deep Learning | A subset of machine learning that uses multi-layered neural networks to learn from large amounts of data and perform complex tasks. |
| Deepfakes | AI-generated or manipulated media, such as videos or audio, that appear highly realistic but are fake. |
| Detectors | Tools or systems that identify content created by AI, distinguishing it from human-generated content. |
| Documentation | The process of recording details about AI systems, including their design, usage, and outputs, to ensure transparency and reproducibility. |
| Embedding | A representation of data, such as words or items, in a continuous vector space that captures their semantic relationships and patterns. |
| Ethics | The study of moral principles governing the development and use of AI technologies to ensure they benefit society and avoid harm. |
| Ethical Implications and Considerations | The potential moral consequences and responsibilities associated with integrating AI technologies in various sectors. |
| Emergent Behavior | Unpredictable or unintended actions and capabilities that arise in AI systems beyond their initial programming. |
| Fairness | Ensuring that AI systems treat all individuals and groups equitably, without bias or discrimination. |
| F Score | A performance metric that balances precision and recall to evaluate the accuracy of an AI model, often used in classification tasks. |
| Fabricated Content | False or invented information created by AI systems, such as fictional news articles or made-up statistics. |
| Generative AI | AI systems designed to create new content, such as text, images, or audio, by learning patterns from existing data. |
| Generative Adversarial Network (GAN) | A type of AI model where two neural networks compete to generate realistic data, improving the quality of generated content. |
| Genetic Resources (GR) | Biological materials containing valuable genetic information, such as plants or animals, relevant in AI contexts for data sovereignty and preservation. |
| Generative AI for Everyone | An introductory resource aimed at educating the general public about the basics and applications of generative AI. |
| Guardrails | Rules and constraints implemented in AI systems to ensure ethical behavior and prevent misuse of data or generation of harmful content. |
| Hallucination | When an AI system generates incorrect or nonsensical outputs that are not based on input data or reality. |
| Hyperparameter | Adjustable settings in machine learning models that influence the training process and model performance, often set before training begins. |
| ImageNet | A large-scale visual database used for training and benchmarking computer vision models, containing millions of labeled images. |
| Image Recognition | The capability of AI systems to identify and classify objects, people, or scenes within images or videos. |
| Input Data | The information provided to an AI system for processing, analysis, or training purposes. |
| Indigenous Cultural Sovereignty | The right of Indigenous peoples to control and preserve their cultural knowledge, practices, and data. |
| Indigenous Data Sovereignty | The authority of Indigenous communities to govern data related to their people and lands, ensuring privacy and control over information. |
| Intelligent Tutoring System (ITS) | AI-powered educational platforms that provide personalized instruction, feedback, and guidance to learners. |
| Intellectual Property (IP) | Legal rights that protect creations of the mind, such as inventions, literary works, and designs. |
| Intellectual Property (IP) – Indigenous | IP rights specifically related to the cultural and traditional knowledge of Indigenous peoples, respecting their unique heritage and sovereignty. |
| Large Language Model (LLM) | A type of AI model trained on vast amounts of text data to understand and generate human-like language. |
| Learning Loss | The decline in educational skills and knowledge that can occur when AI technologies are overused or misused in learning environments. |
| Machine Learning (ML) | A subset of AI that involves training algorithms to learn patterns from data and make predictions or decisions without being explicitly programmed. |
| Manipulated Content | Information that has been altered to distort its original meaning or context, such as edited photos or videos. |
| Misinformation | Incorrect or misleading information that is spread without the intent to deceive. |
| Misleading Content | Information that is presented in a way that causes misunderstanding, even if the content is technically accurate. |
| Natural Language | The languages used by humans for everyday communication, which AI systems aim to understand and generate. |
| Natural Language Generation (NLG) | The process by which AI systems create human-like text based on input data. |
| Natural Language Processing (NLP) | A field of AI focused on enabling machines to understand, interpret, and generate human language. |
| Natural Language Understanding (NLU) | A branch of NLP that focuses on comprehending the meaning and context of human language. |
| Neural Network | An AI model inspired by the human brain, consisting of interconnected nodes that process and learn from data. |
| Overfitting | When a machine learning model learns the training data too well, including its noise and errors, resulting in poor performance on new data. |
| Object Recognition | The ability of AI systems to identify and classify objects within visual data like images and videos. |
| Outputs | The results or responses generated by an AI system after processing input data. |
| Patchwork Plagiarism | A form of plagiarism where content from multiple sources is combined without proper attribution, often by rearranging or slightly modifying the original text. |
| Pattern Recognition | The ability of AI systems to identify and categorize patterns within data, enabling tasks like image classification and speech recognition. |
| Precision | A metric that measures the proportion of true positive results among all positive predictions made by an AI model. |
| Predictive AI Models | AI models designed to analyze historical and current data to forecast future events or behaviors. |
| Predictive Analytics | The use of statistical techniques and machine learning to analyze data and make predictions about future outcomes. |
| Principle | Fundamental beliefs or rules that guide the development and use of AI technologies. |
| Prescriptive Analytics | An advanced form of analytics that recommends actions based on data analysis to help organizations make better decisions. |
| Prompt | The input or instruction given to an AI system to generate a specific output or response. |
| Prompt Engineering | The process of designing effective and precise prompts to guide AI models in generating desired outputs. |
| Public Domain (PD) | Creative works that are not protected by copyright and can be freely used by anyone without permission. |
| Quantum Computing | A computing technology that leverages quantum-mechanical phenomena to perform operations at speeds much faster than traditional computers, enhancing AI capabilities. |
| Recall | A metric that measures the proportion of true positive results that were correctly identified by an AI model out of all actual positives. |
| Reinforcement Learning (RL) | A type of machine learning where an agent learns to make decisions by receiving rewards or penalties based on its actions in an environment. |
| Self-aware | A theoretical level of AI that possesses consciousness and self-awareness similar to humans, currently not realized. |
| Self-Determination | The right of Indigenous peoples to control their own political status and pursue their economic, social, and cultural development. |
| Sentiment Analysis | The use of AI to determine the emotional tone or opinion expressed in a piece of text. |
| Structured Data | Organized data that is easily searchable and stored in predefined formats like databases. |
| Supervised Learning | A machine learning approach where models are trained on labeled data to learn the relationship between inputs and outputs. |
| Token | A basic unit of text, such as a word or part of a word, used by language models to process and generate language. |
| Training Data | The dataset used to teach AI models by providing examples for learning patterns and making predictions. |
| Transfer Learning | A machine learning technique where a model developed for one task is reused as the starting point for a model on a second task. |
| Turing Test | A test proposed by Alan Turing to determine if a machine can exhibit intelligent behavior indistinguishable from that of a human. |
| Traditional Cultural Expressions (TCE) | Cultural artifacts and practices passed down through generations, such as art, music, and storytelling, which are protected under cultural sovereignty. |
| Traditional Knowledge (TK) | The knowledge, practices, and innovations developed by Indigenous peoples over generations, often related to cultural and environmental contexts. |
| Transparency | The practice of being open and clear about the data, algorithms, and processes used in AI systems to build trust and accountability. |
| Underfitting | When a machine learning model is too simple to capture the underlying patterns in the data, leading to poor performance on both training and new data. |
| Unstructured Data | Data that lacks a predefined format, making it difficult to search and analyze using traditional methods, such as text, images, and audio. |
| Unsupervised Learning | A machine learning approach where models are trained on unlabeled data to discover hidden patterns and structures. |
| Validation | The process of evaluating an AI model’s performance on unseen data to ensure it generalizes well and avoids overfitting. |
| Voice Recognition | The ability of AI systems to understand and interpret human speech, converting it into text or commands. |