Here are some of the most requested and/or popular types of AI, listed alphabetically, along with brief descriptions are:

* Artificial General Intelligence (AGI): A theoretical type of AI that has the ability to understand, learn, and apply knowledge across a wide range of tasks at a human-like level.

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

* Expert Systems: AI programs that mimic the decision-making abilities of a human expert by using a set of rules and a knowledge base.

* Machine Learning (ML): A type of AI that allows 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.

* 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.

* 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.

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

* Chatbots: AI programs designed to simulate conversation with human users, often used in customer service and support.

* Computer Vision: A field of AI that enables computers to interpret and make decisions based on visual data, widely used in areas like facial recognition, medical imaging, and autonomous vehicles.

* Deep Learning: A subset of machine learning involving neural networks with many layers, used in applications like speech recognition, image classification, and natural language processing.

* Machine Learning (ML): A type of AI that allows systems to learn from data and improve over time, used in predictive analytics, recommendation systems, and fraud detection.

* Natural Language Processing (NLP): AI that enables computers to understand, interpret, and generate human language, widely used in voice assistants, sentiment analysis, and language translation.

* Predictive Analytics: AI techniques used to analyze current and historical data to make predictions about future events, commonly used in finance, marketing, and healthcare.

* Recommendation Systems: AI algorithms that provide personalized content suggestions to users, extensively used by e-commerce sites, streaming services, and social media platforms.

* Robotics: AI integrated with robots to perform tasks autonomously or semi-autonomously, used in manufacturing, logistics, and healthcare.

* Speech Recognition: AI that can convert spoken language into text, used in virtual assistants, transcription services, and hands-free computing.

* Virtual Assistants: AI-powered software agents that perform tasks or services based on user commands, such as Apple's Siri, Amazon's Alexa, and Google Assistant.

These types of AI have gained significant commercial traction and are widely implemented across various industries, making them among the most requested AI technologies.

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