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Types of AI: Understanding Artificial Intelligence

Page Last Updated: February 2025

Genefa Murphy
Chief Marketing Officer, Udemy
Udemy types of AI

Artificial intelligence is woven into your daily life, from voice assistants like Siri to the algorithms shaping your social media feed. But not all AI is the same. There are several different types of AI, each designed for specific functions and operating in unique ways. 

In this guide, we’ll break down the key categories of AI—from the practical applications of narrow AI to the ambitious frontiers of general and super AI. You’ll learn how each type functions, the industries leveraging them, and how you can be part of this fast-evolving field.

Narrow AI (Weak AI)

Narrow AI (or weak AI) is artificial intelligence that’s expertly designed for a set task. Unlike more advanced forms of AI, it can’t think or learn beyond a single focus. The best examples of narrow AI are voice-controlled devices that can perform tasks such as setting alarms or making calls, but can’t necessarily understand complex context and respond. 

How Does Narrow AI Work?

Narrow AI works by following clearly defined steps, making it a great fit for repetitive jobs, such as language translation or voice recognition. The system doesn’t adapt to tasks outside its specialization, but it delivers fast, consistent results in the area where it’s trained. 

If you pursue a career working with narrow AI, you might go on to build tools that address everyday challenges, such as filtering emails, sorting images, tagging basic content, or responding to common customer questions. Companies rely on this first phase of AI solutions to save time and reduce human error. Because narrow AI sticks to a single objective, it’s relatively straightforward to program and maintain.

Narrow AI’s Data-Driven Performance

Narrow AI systems process vast amounts of data to make quick and accurate decisions. For example, a voice recognition app compares what you say to previously stored voice samples, identifying words or phrases it already recognizes. The data-driven method works well for real-time tasks and can improve over time as more information becomes available.

However, narrow AI systems have their limitations. They are designed for specific tasks and lack the ability to generalize or adapt to new situations outside their programmed scope. This means they can’t understand context beyond the data they’ve been trained on. Additionally, they rely heavily on large amounts of high-quality data to maintain accuracy, and if the data is biased, the AI’s decisions can also be biased (that’s true for all AI – we haven’t quite solved that one yet).

Everyday Examples of Narrow AI

Examples of narrow AI you’ve probably already come across include:

Data scientists, machine learning engineers, AI architects, software developers, and robotics engineers use narrow artificial intelligence to design and create specialized solutions to a wide range of problems.

Generative AI

Generative AI is a subset of artificial intelligence that creates new content, such as text, images, music, and videos, by learning patterns from existing data. Unlike narrow AI, which is limited to predefined tasks, generative AI can produce original content that mimics human creativity. Popular examples include text generation models like ChatGPT, Claude, Perplexity (the list is long), and image generation tools that can create realistic visuals from scratch – think Midjourney, DALL-E etc. 

How Does Generative AI Work?

Generative AI relies on advanced deep learning models, such as Generative Adversarial Networks (GANs) and Transformer architectures. These models learn from vast datasets, identifying complex patterns and structures. By understanding the data’s nuances, generative AI can produce new outputs that are coherent and contextually relevant. It doesn’t just replicate what it’s trained on—it generates novel content based on the patterns it has learned. That’s why when you use generative AI you can use the same prompt and get a different output each time. 

You could pursue a career in generative AI and end up working on developing cutting-edge tools for content creation, virtual reality environments, or even synthetic data generation for training other AI systems. You could also work in current fields like marketing, product management, design etc, and be an AI expert. In fact, for most roles today knowing AI and how to use it to be more effective and efficient is a baseline requirement. 

Opportunities and Limitations of Generative AI

Generative AI is powerful in its ability to create realistic and innovative content. It’s used for personalized marketing campaigns, virtual influencers, interactive storytelling, and even music composition. However, it also has limitations. The AI doesn’t truly understand the content it generates, leading to risks of producing biased, inaccurate, or inappropriate outputs. Additionally, the misuse of generative AI, such as deepfake technology, raises ethical concerns around misinformation and intellectual property. Plus its just super freaky to see what can be created with a few well-crafted prompts. 

Everyday Examples of Generative AI

You’ve likely encountered generative AI in various forms, including:

The versatility of generative AI continues to expand, driving new possibilities for digital content creation and more.

Artificial General Intelligence (AGI)(Strong AI)

Artificial General Intelligence, also referred to as strong AI, is what some call the next frontier. It exists today as part of several research projects and is designed to copy the way the human mind works, helping it process and assimilate information more flexibly than narrow AI. Because it can develop novel solutions that aren’t preprogrammed, AGI shows a similar level of creativity to human intuition.

If you become an expert in AGI’s advanced capabilities, you might land a role that involves creating chatbots that are capable of learning and adapting. This could include AI-based tools in healthcare or educational software that adapt based on the user to personalize lessons, hold conversations, and apply cognitive reasoning.

AGI shouldn’t be confused with generative AI. Generative AI is real and in the here and now. Generative AI as explained above refers to current deep learning models that can automatically generate text,  images, videos, and other content based on the models and data they were trained on. Think of Generative AI like a Chef with an infinite recipe book. It can already mimic complex recipes or it can use vast amounts of available data to create completely new dishes that are completely unexpected. AGI using the same analogy isn’t limited to known ingredients or techniques; it can walk into a kitchen it’s never seen before, invent entirely new cuisines, and even create tools that revolutionize how cooking is done. While Generative AI surprises us with unexpected dishes, AGI questions why we eat the way we do and redefines the entire concept of food.

Developmental Challenges of Strong AI

Developmental challenges when it comes to AGI include:

Future AGI-related roles may include AI trainer, AI ethicist, AI cognitive scientist, and advanced AI researcher — calling for the fields of computer science, psychology, ethics, and robotics to combine expertise and work together on these groundbreaking projects. 

Super AI (Artificial Superintelligence)

Super AI is a hypothetical form of artificial intelligence that would have cognitive power far beyond human capabilities. It would be capable of gathering, learning, understanding, and problem-solving across all intellectual domains to a degree that surpasses human capabilities in every field. 

Potential Impact of Super AI

Although super AI is still largely theoretical, it could potentially solve massive global challenges by reviewing huge datasets and drawing connections people miss. Researchers see it as a technology that can sift through scientific studies and economic data to find new ways to address issues such as disease, famine, poverty, and resource allocation.

If you study super AI’s foundations, you might learn about futuristic AI models and the frameworks needed to process information at immense scale. In the future, you could join a cutting-edge research lab or become a thought leader guiding discussions on topics such as the ethics of letting AI make large-scale decisions. 

Ethical and Safety Considerations of Super AI

When AI reaches a point where it can make choices on its own, questions arise around responsible use. Experts debate how much power machines should have and how to align AI-based decisions with our shared values. Just look up Safe Super Intelligence (SSI) to learn more about the type of companies emerging in this space. 

If you want to guide the ethics of super AI, you could combine tech studies with law, criminal psychiatry, sociology, or public policy. They explore how to craft regulations and oversight methods that protect individual rights while still fostering innovation. Career paths here include politician, AI policy advisor, corporate ethics officer, or researcher in an interdisciplinary institute. 

Super AI’s Current Research Status

Super AI is currently a hypothetical concept, but it will likely reshape existing professions and create new ones focused on AI augmentation, complex systems, human-AI collaboration, risk mitigation, and AI-driven socioeconomic innovation. 

The Four AI Categories: Reactive, Limited Memory, Theory of Mind, and Self-Aware AI

The four categories of AI describe AI’s development from basic reactive machines that respond to stimuli to more advanced limited memory AI that learns from experience. Theory of mind AI and self-aware AI are hypothetical and would involve AI understanding human emotions and potentially possessing a degree of consciousness.

Reactive AI

Reactive AI makes decisions in the moment. It doesn’t store information about past events or rely on learned experiences. Instead, it only looks at the current input and responds on the spot. As such, reactive AI is a good choice for tasks such as automated monitoring, dynamic gaming scenarios, quick feedback loops, or any setup where fast reactions matter more than long-term memory. 

Common uses of reactive AI include:

Some reactive AI career opportunities include: 

Limited Memory AI

Limited memory AI blends current and past data to make informed decisions. It keeps track of previous inputs or actions, and then uses that historical insight to predict outcomes or improve tasks. Limited memory AI uses “short term” memory but can improve over time as the system learns based on experiences. 

Self-driving cars are a well-known example, analyzing real-time sensor data alongside information from past trips so they can drive around safely. The system continually refines its algorithm as fresh data arrives, helping improve predictions and decisions in the future.

Examples of limited memory AI in action include:

Some career paths involving limited memory AI include:

Theory of Mind AI

Theory of Mind AI can understand and model human emotions, context, intentions, and social cues. The aim is to build software that interacts in more natural ways, sensing a user’s feelings and adjusting its responses. In the future, you might see AI tutors that know when a student is confused, or virtual companions that recognize when someone is stressed and adjust the “lesson” style or content accordingly. 

Potential applications of theory of mind AI include:

Some theory of mind AI work opportunities include:

Self-Aware AI

Self-aware AI raises questions about consciousness and accountability—just ask HAL 9000 from 2001: A Space Odyssey. If an AI can reflect on its own existence, should it have protections similar to human beings? Scholars, sociologists, lawmakers, and tech experts debate these topics, shaping policies that might one day govern how self-aware AI interacts with society.

Ethical and Philosophical Considerations of Self-Aware AI

Self-aware AI forces the public and experts to think about what it means to be conscious and whether machines are capable of true self-awareness. To manage the potential impact of self-aware AI, experts stress the need for clear ethical guidelines to govern the development and application of these hyper-advanced systems. 

Career avenues involving self-aware AI include:

How to Learn AI and Advance Your Career

To learn AI and advance your career in artificial intelligence, you can follow these practical steps:

1. Start by Becoming Aware: Enroll in online courses (specifically Udemy courses!). 

2. Get Hands-On: Work on real-world projects

3. Stay up-to-date

4. Get Connected: Network & Create an Eco-System 

Take the First Step Toward AI Mastery Today

Artificial intelligence exists in diverse forms, from the precision of narrow AI to the conceptual potential of super AI. Each category offers distinct capabilities and challenges, providing a roadmap for how technology can solve complex problems and create new opportunities. 

Why not try one of the following courses?

Invest in practical training that prepares you for a future where AI drives innovation and growth. We regularly update our courses to reflect the latest advancements, so you’re always learning the most relevant and effective techniques and skills to start or advance your career journey. The possibilities are limitless.