Some recognized types and forms of AI are:

Artificial General Intelligence (AGI): An AI with the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to a human being.

Artificial Narrow Intelligence (ANI): AI systems that are specialized in performing a single task or a narrow range of tasks, such as image recognition or language translation.

Artificial Superintelligence (ASI): A hypothetical form of AI that surpasses human intelligence and capabilities across all areas, potentially leading to rapid advancements and significant changes in society.

Bayesian Networks: Probabilistic models that represent a set of variables and their conditional dependencies via a directed acyclic graph, often used for reasoning under uncertainty.

Chatbots: AI programs designed to simulate conversation with human users, typically through text or voice interactions, used in customer service and virtual assistants.

Cognitive Computing: AI systems that aim to mimic human thought processes in complex situations, involving self-learning systems that use data mining, pattern recognition, and natural language processing.

Deep Learning: A subset of machine learning involving neural networks with many layers (deep neural networks) that can learn and make decisions from large amounts of data.

Expert Systems: AI programs that emulate the decision-making abilities of a human expert in a specific field, using a knowledge base and a set of rules.

Genetic Algorithms: Optimization algorithms inspired by the process of natural selection, used to solve problems by evolving solutions over generations.

Machine Learning (ML): A type of AI that enables systems to learn from data and improve their performance over time without being explicitly programmed.

Natural Language Processing (NLP): A field of AI focused on the interaction between computers and humans through natural language, enabling machines to understand, interpret, and generate human language.

Neural Networks: Computing systems inspired by the human brain's structure, consisting of interconnected nodes (neurons) that process information in layers.

Reinforcement Learning (RL): A type of machine learning where an agent learns to make decisions by performing actions and receiving rewards or penalties, aiming to maximize cumulative rewards.

Robotics: The integration of AI with robots, enabling them to perform tasks autonomously or semi-autonomously, often involving physical interactions with the environment.

Rule-Based Systems: AI systems that apply predefined rules to data in order to produce outcomes, commonly used in decision support and automation.

Supervised Learning: A type of machine learning where the model is trained on labeled data, learning to map inputs to outputs based on example input-output pairs.

Swarm Intelligence: A type of artificial intelligence based on the collective behavior of decentralized, self-organized systems, often inspired by biological systems like ant colonies or bird flocks.

Unsupervised Learning: A type of machine learning where the model is trained on unlabeled data, discovering hidden patterns or intrinsic structures within the data.

Weak AI: Another term for Artificial Narrow Intelligence (ANI), focused on performing specific tasks rather than possessing general cognitive abilities.