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Generative Artificial Intelligence

Generative Artificial Intelligence

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Welcome to the George Mason University Libraries Artificial Intelligence LibGuide, brought to you by the Mason Libraries AI Task Force. The purpose of this InfoGuide is to provide comprehensive resources to support students, researchers, and anyone interested in learning more about this field. The guide offers a collection of curated resources including articles, websites, databases, etc. relevant to artificial intelligence.

The Mason Libraries provide educational support and research assistance for patrons looking to explore and deepen their understanding of artificial intelligence. Below you will find a linked tool bar for this guide. If you have any questions, concerns, or comments about how to further improve the AI LibGuide, please contact Dr. Heidi Blackburn.

**Please note that the content within this guide is subject to continuous updates, reflecting the ongoing progression and transformations within the realm of artificial intelligence.**

This Info-Guide serves solely for informational purposes.

Welcome to the Artificial Intelligence - AI - InfoGuide!

What is AI?

Artificial intelligence (AI) is also referred to as machine learning (ML) although they are different. Similarly, Large Language Models (LLMs) are often referred to as AI and fit under the umbrella of AI with ML but neither demonstrates actual intelligence.

AI (Artificial Intelligence) "is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable [emphasis added]." (McCarthy, n.d.)

Intelligence of created systems and algorithms is typically compared to human intelligence. Sometimes LLMs and ML products can appear to have human intelligence, but it is simply the product of coding, not actual intelligence.

ML (Machine Learning) is "algorithms that give computers the ability to learn from data, and then make predictions and decisions". Examples include automatically detecting spam emails, suggesting videos to watch after finishing one, etc. (CrashCourse, 2017) 

LLMs (Large Language Models) "can generate natural language texts from large amounts of data. Large language models use deep neural networks, such as transformers, to learn from billions or trillions of words, and to produce texts on any topic or domain. Large language models can also perform various natural language tasks, such as classification, summarization, translation, generation, and dialogue." (Maeda & Chaki, 2023)

GPT (Generative Pre-trained Transformer) "models give applications the ability to create human-like text and content (images, music, and more), and answer questions in a conversational manner." (What Is GPT AI?, n.d.)

The information below was written by the IBM Data and AI Team

The Three Kinds of Artificial Intelligence Based on Capabilities

1. Artificial Narrow AI

Artificial Narrow Intelligence, also known as Weak AI, what we refer to as Narrow AI is the only type of AI that exists today. Any other form of AI is theoretical. It can be trained to perform a single or narrow task, often far faster and better than a human mind can. However, it can’t perform outside of its defined task. Instead, it targets a single subset of cognitive abilities and advances in that spectrum. Siri, Amazon’s Alexa and IBM Watson are examples of Narrow AI. Even OpenAI’s ChatGPT is considered a form of Narrow AI because it’s limited to the single task of text-based chat.

2. General AI

Artificial General Intelligence (AGI), also known as Strong AI, is today nothing more than a theoretical concept. AGI can use previous learnings and skills to accomplish new tasks in a different context without the need for human beings to train the underlying models. This ability allows AGI to learn and perform any intellectual task that a human being can.

3. Super AI

Super AI is commonly referred to as artificial superintelligence and, like AGI, is strictly theoretical. If ever realized, Super AI would think, reason, learn, make judgements and possess cognitive abilities that surpass those of human beings. The applications possessing Super AI capabilities will have evolved beyond the point of understanding human sentiments and experiences to feel emotions, have needs and possess beliefs and desires of their own.

The Four Types of Artificial Intelligence Based on Functionalities

1. Reactive Machine AI

Reactive machines are AI systems with no memory and are designed to perform a very specific task. Since they can’t recollect previous outcomes or decisions, they only work with presently available data. Reactive AI stems from statistical math and can analyze vast amounts of data to produce a seemingly intelligence output.

Examples of Reactive Machine AI

  • IBM Deep Blue: IBM’s chess-playing supercomputer AI beat chess grandmaster Garry Kasparov in the late 1990s by analyzing the pieces on the board and predicting the probable outcomes of each move
  • The Netflix Recommendation Engine: Netflix’s viewing recommendations are powered by models that process data sets collected from viewing history to provide customers with content they’re most likely to enjoy

2. Limited Memory AI

Unlike Reactive Machine AI, this form of AI can recall past events and outcomes and monitor specific objects or situations over time. Limited Memory AI can use past- and present-moment data to decide on a course of action most likely to help achieve a desired outcome. However, while Limited Memory AI can use past data for a specific amount of time, it can’t retain that data in a library of past experiences to use over a long-term period. As it’s trained on more data over time, Limited Memory AI can improve in performance.

Examples of Limited Memory AI

  • Generative AI: Generative AI tools such as ChatGPT, Bard and DeepAI rely on limited memory AI capabilities to predict the next word, phrase or visual element within the content it’s generating
  • Virtual assistants and chatbots: Siri, Alexa, Google Assistant, Cortana and IBM Watson Assistant combine natural language processing (NLP) and Limited Memory AI to understand questions and requests, take appropriate actions and compose responses
  • Self-driving cars: Autonomous vehicles use Limited Memory AI to understand the world around them in real-time and make informed decisions on when to apply speed, brake, make a turn, etc.

3. Theory of Mind AI

Theory of Mind AI is a functional class of AI that falls underneath the General AI. Though an unrealized form of AI today, AI with Theory of Mind functionality would understand the thoughts and emotions of other entities. This understanding can affect how the AI interacts with those around them. In theory, this would allow the AI to simulate human-like relationships. Because Theory of Mind AI could infer human motives and reasoning, it would personalize its interactions with individuals based on their unique emotional needs and intentions. Theory of Mind AI would also be able to understand and contextualize artwork and essays, which today’s generative AI tools are unable to do.

Emotion AI is a theory of mind AI currently in development. AI researchers hope it will have the ability to analyze voices, images and other kinds of data to recognize, simulate, monitor and respond appropriately to humans on an emotional level. To date, Emotion AI is unable to understand and respond to human feelings.  

4. Self-Aware AI

Self-Aware AI is a kind of functional AI class for applications that would possess super AI capabilities. Like theory of mind AI, Self-Aware AI is strictly theoretical. If ever achieved, it would have the ability to understand its own internal conditions and traits along with human emotions and thoughts. It would also have its own set of emotions, needs and beliefs.

Emotion AI is a Theory of Mind AI currently in development. Researchers hope it will have the ability to analyze voices, images and other kinds of data to recognize, simulate, monitor and respond appropriately to humans on an emotional level. To date, Emotion AI is unable to understand and respond to human feelings.   

Additional capabilities and practical applications of AI technologies

Computer Vision 

Narrow AI applications with computer vision can be trained to interpret and analyze the visual world. This allows intelligent machines to identify and classify objects within images and video footage.

Applications of computer vision include:

  • Image recognition and classification
  • Object detection
  • Object tracking
  • Facial recognition
  • Content-based image retrieval

Computer vision is critical for use cases that involve AI machines interacting and traversing the physical world around them. Examples include self-driving cars and machines navigating warehouses and other environments.


Robots in industrial settings can use Narrow AI to perform routine, repetitive tasks that involve materials handling, assembly and quality inspections. In healthcare, robots equipped with Narrow AI can assist surgeons in monitoring vitals and detecting potential issues during procedures. Agricultural machines can engage in autonomous pruning, moving, thinning, seeding and spraying. And smart home devices such as the iRobot Roomba can navigate a home’s interior using computer vision and use data stored in memory to understand its progress.

Expert Systems

Expert systems equipped with Narrow AI capabilities can be trained on a corpus to emulate the human decision-making process and apply expertise to solve complex problems. These systems can evaluate vast amounts of data to uncover trends and patterns to make decisions. They can also help businesses predict future events and understand why past events occurred.

YouTube AI Crash Courses

"Artificial intelligence is everywhere and it's already making a huge impact on our lives. It's autocompleting texts on our cellphones, telling us which videos to watch on YouTube, beating us at video games, recognizing us in photos, ordering products in stores, driving cars, scheduling appointments, you get the idea. Today we're going to explain what AI can (and can't) do right now and explain how we got to where we are today."

- PBS Crash Course

You have probably heard of ChatGPT, deep fakes, and AI Generated Images/Videos/Sound. This 2-minute video explains what Generative AI and its concerns and hopes for the future.

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