7 Types of Artificial Intelligence (AI) for Beginners

Artificial Intelligence
Written by:
App Academy
Published on:
February 29, 2024
"AI" letters in a design image

Discover 7 types of AI: from Reactive Machines to Theory of Mind. Explore applications from self-driving cars to natural language processing. Dive into AI's future with App Academy.

If you’ve been hearing about artificial intelligence (AI) recently, you’re not alone. AI has become massively popular in recent years: think of Siri, Alexa, ChatGPT, and Dall-e. In this piece, we’ll explore what AI is, the different types of AI, and what they’re all used for.

What is Artificial Intelligence?

Artificial intelligence leverages complex, human-created algorithms to perform tasks intelligently. From autonomous vehicles to advanced medical diagnostics, its applications are vast. The future of AI holds promise for even more profound integration into our daily lives, with advancements in fields like natural language processing (NLP), computer vision, and autonomous robotics on the horizon.

There are seven types of artificial intelligence you should know about. These can be further broken down by capabilities vs. functionalities. Let’s take a look.

AI by Capability

“Capability” refers to the specific levels of abilities that an AI system possesses. It encompasses the range of tasks, functions, or skills that the AI system is designed to perform autonomously.

Artificial Narrow Intelligence (ANI)

Artificial Narrow Intelligence (ANI), also referred to as Narrow AI or Weak AI, refers to AI systems designed for specific, predefined tasks. These systems excel in a single cognitive capability and cannot autonomously acquire additional skills beyond their intended purpose. 

ANI relies on technologies such as machine learning (ML) and neural networks to perform these designated tasks. Examples of ANI in use today include image recognition software, self-driving cars, and AI virtual assistants like Siri. 

Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI, also known as strong AI or general AI) represents the next level of artificial intelligence, where machines possess the capacity to understand, learn, and execute a broad spectrum of intellectual tasks like humans. 

Unlike Narrow AI, which specializes in specific tasks, AGI aims to replicate human-like cognitive abilities. It can adapt its knowledge and skills to different situations, demonstrating a level of adaptability and versatility comparable to human intelligence.

As of now, researchers have yet to achieve AGI. The challenge lies in programming machines with full cognitive ability — including consciousness. Once realized, AGI could revolutionize industries and everyday life. It has the potential to act as highly intelligent assistants to humans, capable of performing multifaceted tasks across different domains.

Artificial Superintelligence (ASI)

Artificial Superintelligence (ASI), or “super AI,” represents an advanced level of artificial intelligence that surpasses human cognitive capabilities. This hypothetical concept envisions AI systems with profound understanding of human emotions, needs, beliefs, and even the ability to generate emotions and desires of their own. ASI's potential capabilities include autonomous thinking, complex problem-solving, and independent decision-making.

If realized, ASI would have the unprecedented capacity to outperform humans in virtually any intellectual task. It would possess the ability to learn and adapt at an exponential rate, making it vastly superior in knowledge and skills to human intelligence. ASI could drive innovation, revolutionize industries, and autonomously manage complex systems.

As fascinating as ADI might be, ethical, safety, and philosophical concerns must be addressed before ASI becomes a reality, leaving the development of ASI to science fiction. 

AI by Functionality

An AI's functionality is determined by how it interacts with its surroundings, processes data, and responds to stimuli.

Reactive Machines

Reactive machines are characterized by their lack of memory and task-specific functionality. This means that given a particular input, they will consistently produce the same output. They’re often exemplified by machine learning models that utilize specific data, like customer purchase history, to generate tailored recommendations for those same customers.

These AI systems excel in handling vast amounts of data, such as a customer's entire viewing history on Netflix, delivering personalized suggestions that would be near-impossible for an average human to process. Reactive AI, while reliable for its defined tasks, can only forecast future outcomes if it has been provided with the pertinent information.

Limited Memory

Limited memory AI possesses the ability to consider past information. A prominent example is found in self-driving cars, which actively observe the speed and direction of other vehicles. This task necessitates continuous monitoring and the ability to identify specific objects over a span of time, a capability beyond instantaneous perception.

In the case of self-driving cars, these observations are integrated into their preprogrammed understanding of the environment, encompassing elements such as lane markings, traffic lights, and road curvature.

Limited memory AI is an illustration of the temporary nature of current AI data retention. Past experiences are held for a practical duration, after which they’re discarded, setting a clear distinction from systems that can draw from extensive libraries of accumulated knowledge for future learning.

Theory of Mind

In psychological terms, the Theory of Mind refers to the understanding that individuals, creatures, and objects in the world possess thoughts and emotions that influence their behavior. The next wave of AI, Theory of Mind machines, aims to incorporate this understanding, paving the way for a more sophisticated level of artificial intelligence. 

Unlike the existing types of AI, such as reactive machines and limited memory systems, Theory of Mind AI and self-aware AI (see below) are still only theoretical models yet to be realized. As of now, there are no concrete real-world examples of these advanced AI types.

However, there is enormous potential for Theory of Mind AI. If successfully developed, it could revolutionize AI's capacity to comprehend the world and sympathize with other entities, understanding their thoughts and emotions. This understanding would fundamentally influence how AI interacts and behaves in relation to its environment and the entities within it.


Self-aware AI represents the pinnacle of artificial intelligence, characterized by a level of sophistication beyond what currently exists. It would possess a conscious understanding of its own existence. This means it would be capable of introspection, recognizing its internal states, and potentially even experiencing human emotions.

Developing self-aware AI is a monumental challenge because it requires a profound understanding of human consciousness, memory, learning processes, and decision-making mechanisms. Currently, this concept remains purely theoretical, as the field of AI is still unraveling the complexities of the human brain's intelligence. 

To achieve self-awareness in AI, researchers would not only need to comprehend consciousness but also construct machines capable of possessing it. 

Branches of AI

AI is being used in a variety of industries across a variety of tasks. Here’s how you might already interact with AI today.

Machine Learning

Machine learning (ML) is a branch of artificial intelligence focused on developing algorithms that enable systems to learn from data. Scientists utilize ML across a spectrum of applications, ranging from image recognition to spam filtering and natural language processing. 

Deep Learning

Deep learning leverages artificial neural networks to extract intricate insights from data. Scientists employ deep learning algorithms to address a wide array of complex tasks, spanning natural language processing (NLP — see below), image recognition, and speech recognition. 

Natural Language Processing

Natural language processing (NLP) focuses on the interaction between computers and human language. NLP techniques are used to comprehend and process human language in diverse applications such as speech recognition and text analysis. Through advanced algorithms, NLP enables machines to interpret, extract meaning, and generate human-like responses from textual and spoken language inputs.


Robotics encompasses the design, construction, and operation of robots, and AI plays a central role in this field. Machine learning algorithms enable robots to adapt and refine their actions based on data from their environment. This facilitates tasks such as autonomous navigation, object recognition, and even complex decision-making. 

As a result, robots equipped with AI are employed across a wide spectrum of industries, including manufacturing, healthcare, and transportation, where they can perform tasks efficiently and autonomously.

Expert Systems

Expert systems are computer programs designed to replicate the decision-making processes of human experts in a specific domain. These systems rely on a knowledge base, which is a repository of information and rules, to make informed decisions or provide recommendations. 

They’re extensively used in a range of applications, such as medical diagnoses, financial planning, and customer service. Through continuous learning and updates to their knowledge base, expert systems can evolve and improve their decision-making abilities over time. 

App Academy’s Unique Approach to AI

App Academy gives you the coding skills and experience you need to take your career to the next level. Our curriculum includes learning Python and JavaScript, two foundational languages for AI. Additionally, we’re blending AI into our curriculum by teaching you how to incorporate it into the programs you create. 

Visit us today to learn more about coding, AI, and App Academy’s coding bootcamps.


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