Artificial general intelligence (AGI) refers to a still-hypothetical form of artificial intelligence with human-like cognitive abilities that allow it to understand, learn and perform a wide range of tasks. 

An AGI could theoretically adapt to various domains without specific programming. Unlike narrow or specialized artificial intelligence, AGI would have the potential to exhibit general intelligence and problem-solving capabilities comparable to human intelligence. 

As of mid-2023, no AI system is advanced enough to be considered AGI.

There are many concepts and categories within the broad realm of AI, including:

• Machine learning (ML): A subset of AI that involves the use of algorithms and statistical models to enable machines to learn from data and improve their performance on a task over time. It includes techniques like supervised learning (learning from labeled data), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through trial and error). 
• Neural networks: A class of algorithms inspired by the structure of the human brain. Deep learning is a subset of neural networks that involves complex architectures with many layers, enabling them to automatically learn hierarchies of features from data. 
• Natural language processing (NLP): A branch of AI that focuses on enabling computers to understand, interpret and generate human language. NLP technologies power chatbots, language translation, sentiment analysis and more. 
• Large language models (LLMs): Advanced artificial intelligence models that can understand and generate human-like text by processing vast amounts of training data. They have applications in natural language understanding, translation, content generation and more, revolutionizing how computers interact with and generate human language.
• Generative AI: Closely related to NLP and LLMs, generative AI refers to a class of AI models and techniques that have the ability to generate new content, such as text, images, or even music, that resembles human-created data. 
• Machine learning operations (MLOps): A set of practices and tools that bridge the gap between machine-learning development and production deployment. It involves streamlining the entire machine-learning lifecycle, from data preparation and model training to deployment, monitoring and continuous improvement, to make machine-learning projects more efficient and reliable.
• Computer vision: A field of AI that enables computers to interpret and understand visual information from the world. It’s used in applications like image and video analysis, object detection, facial recognition and autonomous vehicles. 
• Expert systems: These are AI systems designed to mimic the decision-making abilities of a human expert in a particular domain. These systems use rules and knowledge bases to make informed decisions.
• Robotics: Integrating AI with robotics to create intelligent machines that can perform physical tasks in the real world. This includes things like assembly line work, surgical procedures and exploration in hazardous environments.
• Artificial general intelligence (AGI): This is a still-hypothetical future AI system that possesses general human-like intelligence, including the ability to understand, learn and apply knowledge in a broad range of tasks, similar to the versatility of human intelligence — and considered by many to be the holy grail of AI.

Artificial intelligence, or AI, refers to the simulation of human intelligence by computers and other machines. Increasingly, there are AI applications that can problem-solve, understand and mimic human language, identify patterns, learn and reason.

Advancements in AI technologies in recent years have seen artificial intelligence adopted in industries ranging from health care to investing to manufacturing to national security and defense, and frequently put AI in the news.

But while AI systems have proven themselves adept at specific tasks, these systems have not (yet) become what researchers refer to as artificial general intelligence, or AGI — a hypothetical type of AI as smart and capable as a human that learns from experience, incorporates context into its understanding of the world, and adapts to new situations with acquired knowledge.