Below is a list of A.I. definitions and related terminologies:

• Active Learning: A type of machine learning where the model can actively ask for labels or feedback to improve its performance.
• Adversarial Examples: inputs that have been specifically designed to fool machine learning models into making incorrect predictions.
• Adversarial Training: A method used to improve the robustness of a model by training it on a dataset of adversarial examples.
• AI Ethics: The branch of AI that deals with the ethical considerations of AI systems, including issues of bias, accountability, transparency, and explainability.
• AI Governance: The study of how to design, regulate, and govern AI systems to ensure that they are ethical, transparent, and aligned with human values.
• AI Safety: The branch of AI that deals with ensuring that AI systems are safe and reliable, and do not cause unintended harm.
• Anomaly Detection: The process of identifying unusual or abnormal behavior in a system or dataset, which may indicate a problem or a need for further investigation.
• Artificial Intelligence (AI): The simulation of human intelligence processes by computer systems.
• Augmented Reality (AR): A technology that overlays digital information on the user’s view of the real world.
• Autoencoder: A type of neural network architecture used for unsupervised learning, which learns to reconstruct its input by training on a compressed or encoded version of the input.
• AutoML: A technique that automates the process of selecting and tuning machine learning models, allowing non-experts to build and deploy AI models easily.
• Autonomous Systems: A type of AI system that can operate independently and make decisions without human intervention.
• Backpropagation: An algorithm used to train neural networks, which propagates the error back through the network to adjust the weights and biases of the neurons.
• Bayesian Networks: A probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph.
• Cognitive Computing: A field of AI that focuses on creating systems that can mimic human thought processes and behaviors.
• Computational Creativity: The branch of AI that deals with the ability of computers to create new and original ideas, products or services.
• Computer Simulation: The use of computer models to simulate real-world systems or processes, which can be used to analyze and test AI algorithms and models.
• Computer Vision: A branch of AI that deals with enabling computers to interpret and understand visual data from the world, such as images and videos.
• Computer vision: A field of study that deals with how computers can be made to interpret and understand visual information, such as images and videos.
• Continual Learning: The ability of a model to learn and improve over time with new data and experiences.
• Curriculum Learning: A technique used to improve the performance of a machine learning model by presenting the training data in a logical and incremental order that is easy for the model to learn.
• Data Augmentation: A technique used to improve the performance of a machine learning model by artificially increasing the size of the training dataset by applying various transformations to the existing examples.
• Decision Tree: A type of algorithm used in supervised learning, where a model is trained to make a decision by learning simple decision rules inferred from the data features.
• Deep Learning: A subset of ML that uses neural networks with multiple layers, also known as deep neural networks, for tasks such as image and speech recognition.
• Dialogue Systems: A technology that uses AI to enable natural language communication between a computer and a human.
• Edge Computing: A method of processing data at the edge of the network, closest to the source of the data, rather than sending all data back to a central location for processing.
• Emotion recognition: A technology that uses AI to analyze and interpret human emotions based on facial expressions, speech patterns, or physiological signals.
• Ensemble Methods: A type of machine learning where multiple models are combined to improve the overall performance and robustness.
• Evolutionary Algorithms: A class of optimization algorithms that are inspired by the process of natural evolution and are used for tasks such as global optimization and feature selection.
• Expert Systems: A type of AI that uses a knowledge base and inference engine to solve problems that would normally require human expertise.
• Explainable AI (XAI): A research field focused on developing AI models that can provide human-understandable explanations of their decision-making processes.
• Federated learning: A type of machine learning where a model is trained across multiple decentralized devices or edge devices, instead of a central server.
• Fuzzy Logic: A form of mathematical logic that deals with reasoning about partially true or uncertain statements.
• Generative Adversarial Networks (GANs): A type of neural network architecture composed of two networks, a generator and a discriminator, that are trained to compete against each other in order to generate new, synthetic data that is indistinguishable from real data.
• Generative Model: A type of model that is trained to generate new examples that are similar to the training data.
• Genetic Algorithm: A type of optimization algorithm that is inspired by the process of natural selection and is used for tasks such as finding the global minimum of a function.
• Gradient Descent: An optimization algorithm used to minimize the error of a model by iteratively adjusting the parameters in the direction of the negative gradient of the error function.
• Human-AI collaboration: The process of working together with AI systems to achieve a common goal, where the strengths of both humans and AI are leveraged.
• Human-computer interaction (HCI): The study of how people interact with computers and how to design computer systems that are easy to use.
• Human-in-the-loop: A type of AI system where human input is used to improve the decision-making process or to provide feedback to the system.
• Human-robot interaction (HRI): The study of how people interact with robots and how to design robots that are easy to use.
• Hybrid Systems: An AI system that combines multiple techniques or approaches, such as rule-based and machine learning, to achieve better performance or functionality.
• Imitation Learning: A type of machine learning where a model learns to imitate the behavior of an expert or a human.
• Inference Engine: A component of an AI system that uses the knowledge base and logical reasoning to draw conclusions and make decisions.
• Knowledge Base: A collection of knowledge in a structured format that can be accessed and used by an AI system.
• Knowledge Representation and Reasoning (KRR): The branch of AI that deals with representing knowledge in a form that a computer can understand and reason with.
• Knowledge Representation: The process of encoding knowledge in a format that can be understood and used by an AI system.
• Learning to Learn: A type of machine learning where a model learns to improve its own learning process.
• Machine Learning (ML): A subset of AI that focuses on the development of algorithms and statistical models that enable systems to automatically improve their performance with experience.
• Machine vision: A type of AI that deals with the ability of machines to interpret and understand visual information from the world, such as images and videos.
• Meta-learning: A type of machine learning where a model learns to learn, by adapting quickly to new tasks with small amounts of data.
• Monte Carlo Tree Search (MCTS): A method used in game AI to approximate the optimal decision by simulating multiple possible future game states.
• Multi-Agent systems: A type of AI system that consists of multiple agents that interact and coordinate with each other to achieve a common goal.
• Multimodal Learning: a type of machine learning that allows the system to learn from multiple modalities such as audio, video, text and images
• Multi-task Learning: A type of machine learning where a model is trained to perform multiple tasks simultaneously and share information between them.
• Natural Language Generation (NLG): A technology that uses AI to automatically generate human-like text from structured data.
• Natural Language Processing (NLP): A branch of AI that focuses on the interaction between computers and humans in natural language.
• Natural Language Processing (NLP): A field of AI that deals with the ability of computers to understand, interpret, and generate human language.
• Natural Language Understanding (NLU): A technology that uses AI to automatically extract meaning from human-generated text.
• Neural Networks: A type of ML algorithm that is modeled after the structure and function of the human brain and is used for tasks such as image recognition and natural language processing.
• One-shot learning: A type of machine learning where a model must learn to recognize new objects or classes with only one or a few examples.
• Particle Swarm Optimization (PSO): A method used to optimize a model by simulating the behavior of a swarm of particles that move in the search space guided by their own and their neighboring particles’ best performances.
• Predictive Analytics: A type of AI that uses statistical and machine learning techniques to extract insights and make predictions from data.
• Predictive Maintenance: A type of AI application that uses data from sensors and historical data to predict when equipment or machinery is likely to fail, so that maintenance can be scheduled before the failure occurs.
• Predictive Modeling: The use of statistical techniques to analyze historical data and make predictions about future events or trends.
• Q-Learning: A type of reinforcement learning algorithm that learns to make decisions by approximating the optimal action-value function using a Q-table.
• Random Forest: A type of algorithm used in supervised learning, where multiple decision trees are trained and combined to improve the overall accuracy and robustness of the model.
• Reinforcement Learning: A type of ML where a model learns to make decisions by maximizing a reward signal.
• Reward Shaping: A technique used in reinforcement learning to guide the agent towards the goal by defining additional intermediate rewards.
• Robotics: The branch of AI that deals with the design, construction, and operation of robots.
• Robotics: The branch of engineering and science that deals with the design, construction, operation, and application of robots.
• Robust AI: AI that can operate in dynamic, uncertain and potentially adversarial environments.
• Robustness: the ability of a model to perform well on inputs that are slightly different from the ones it was trained on.
• Rule-based Systems: An AI system that uses a set of rules to make decisions or solve problems.
• Self-Organizing Maps: A type of unsupervised neural network that can be used for dimensionality reduction and visualization of high-dimensional data.
• Self-Supervised Learning: A type of unsupervised learning where the model learns from the input data without the need for explicit labels.
• Semi-Supervised Learning: A type of machine learning where the model is trained on a dataset that contains both labeled and unlabeled examples.
• Supervised Learning: A type of ML where a model is trained on labeled data, in order to predict the output for new, unseen input.
• Support Vector Machines (SVMs): A type of algorithm used in supervised learning, that finds the boundary or hyperplane that maximally separates different classes in a high-dimensional space.
• Transfer Learning: A technique used to adapt a pre-trained model to a new task by fine-tuning its parameters on a new dataset.
• Unsupervised Learning: A type of ML where the model is not given any labeled data, and instead must find patterns or relationships in the input on its own.
• Virtual Assistant: A type of AI application that uses natural language processing and machine learning to understand and respond to human queries in a conversational manner.
• Virtual Reality (VR): A technology that uses computer-generated simulations of 3D environments to create immersive experiences for users.
• Zero-shot Learning: A type of machine learning where a model must recognize new objects or classes without any examples of them during training.

This is an ongoing list of AI terminology and definitions; I hope you find it helpful.