Only reactive machine AI and limited memory AI currently exist, with both theory of mind and self-aware AI still purely theoretical<\/p>\n<\/li>\n<\/ul>\n
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Reactive machines lack memory and are task-specific, providing the same output for a given input, whereas limited memory AI can learn from the data it is given, improving its output over time<\/p>\n<\/li>\n<\/ul>\n
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You can use reactive machines when you need to process large amounts of data<\/p>\n<\/li>\n<\/ul>\n
Learn more about the four main types of AI and their functions in everyday life. Afterward, if you want to build your AI skills, consider enrolling in the Google AI Essentials Specialization. In as little as one day, you can learn to use AI to make more informed decisions, write AI prompts, and develop strategies to stay updated on evolving AI technology<\/p>\n
What are the 4 main types of AI?<\/h2>\n
Learning in AI can fall under the types \u201cnarrow intelligence,\u201d \u201cartificial general intelligence,\u201d and \u201csuper AI.\u201d These categories demonstrate AI\u2019s capabilities as it evolves: performing narrowly defined sets of tasks, simulating thought processes in the human mind, and performing beyond human capability<\/p>\n
Watch this video from IBM’s Introduction to Artificial Intelligence (AI) course to learn more about these categories:<\/p>\n
There are four main types of AI as defined by Arend Hintze, researcher and professor of integrative biology and computer science at Michigan State University [1<\/a>]. They are as follows:<\/p>\n
1. Reactive machines AI<\/h3>\n
Reactive machines are AI systems that have no memory and are task-specific, meaning that an input always delivers the same output. Machine learning models tend to be reactive machines because they, for example, take customer data, such as purchase or search history, and use it to deliver recommendations to the same customers.\u00a0<\/p>\n
This type of AI is reactive. It performs \u201csuper\u201d AI because the average human would not be able to process huge amounts of data, such as a customer\u2019s entire Netflix history, and provide customized recommendations. Reactive AI, for the most part, is reliable and works well in applications such as self-driving cars. It doesn\u2019t have the ability to predict future outcomes unless it has been fed the appropriate information.<\/p>\n
Compare this to our human lives, where most of our actions are not reactive because we don\u2019t have all the information we need to react upon, but we have the capability to remember and learn. Based on those successes or failures, we may act differently in the future if faced with a similar situation<\/p>\n
Examples of reactive machines<\/h4>\n
Beat at chess by IBM\u2019s supercomputer:<\/b> One of the best examples of reactive AI is when Deep Blue, IBM\u2019s chess-playing AI system, beat Garry Kasparov in the late 1990s. Deep Blue could identify its own and its opponent\u2019s pieces on the chessboard to make predictions, but it did not have the memory capacity to use its past mistakes to inform future decisions. It only made predictions based on what moves could come next for both players and selected the best move.\u00a0<\/p>\n
Netflix recommendations: Netflix\u2019s recommendation engine is powered by machine learning models that process the data collected from a customer\u2019s viewing history to determine specific movies and TV shows that they will enjoy. Humans are creatures of habit. If someone tends to watch a lot of Korean dramas, Netflix will show a preview of new releases in this genre on the home page<\/p>\n
2. Limited memory AI<\/h3>\n
The next type of AI in its evolution is limited memory. This algorithm imitates the way our brains\u2019 neurons work together, meaning that it gets smarter as it receives more data to train on. Deep learning algorithms improve natural language processing (NLP), image recognition, and other types of reinforcement learning<\/p>\n
Limited memory AI, unlike reactive machines, can look into the past and monitor specific objects or situations over time. Then, these observations are programmed into the AI so that its actions can be performed based on both past and present moment data. But in limited memory, this data isn\u2019t saved into the AI\u2019s memory as experience to learn from, the way humans might derive meaning from their successes and failures. The AI improves over time as it\u2019s trained on more data.<\/p>\n
Example of limited memory artificial intelligence<\/h4>\n
Self-driving cars:<\/b> A good example of limited memory AI is the way self-driving cars observe other cars on the road for their speed, direction, and proximity. This information is programmed as the car\u2019s representation of the world, such as knowing traffic lights, signs, curves, and bumps in the road. The data helps the car decide when to change lanes so that it does not get hit or cut off by another driver.\u00a0<\/p>\n
3. Theory of mind AI<\/h3>\n
The first two types of AI, reactive machines and limited memory, are types that currently exist. Theory of mind and self-aware AI are theoretical types that could be built in the future. As such, there aren\u2019t any real-world examples yet<\/p>\n
If it is developed, theory of mind AI could have the potential to understand the world and how other entities have thoughts and emotions. In turn, this affects how they behave in relation to those around them<\/p>\n
Human cognitive abilities are capable of processing how our own thoughts and emotions affect others and how others\u2019 affect us. This is the basis of our society\u2019s human relationships. In the future, theory of mind AI machines could understand intentions and predict behavior, as if simulating human relationships<\/p>\n
What type of AI is ChatGPT?<\/h3>\n
ChatGPT is an example of generative AI, a kind of artificial intelligence powered by large language models (LLMs) that are created by training algorithms on massive amounts of data. Generative AI is capable of creating original outputs in response to user inputs or “prompts.” ChatGPT’s ability to produce responses resembling human language has made it a common type of AI used for chatbots and virtual assistants.<\/p>\n
That said, while tools like ChatGPT, Google Gemini, or Microsoft Copilot may produce responses that give the impression of a self-aware AI, they aren’t really. Instead, their responses are actually<\/a> just the result of the algorithm identifying the statistically most likely output based on its training data and the user’s prompt<\/p>\n
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<\/p>\n4. Self-awareness AI<\/h3>\n
The grand finale for the evolution of AI would be to design systems that have a sense of self, a conscious understanding of their existence. This type of AI does not exist yet<\/p>\n
This type of AI goes beyond theory of mind AI and understanding emotions to being aware of itself, its state of being, and being able to sense or predict others\u2019 feelings. For example, \u201cI\u2019m hungry\u201d becomes \u201cI know I am hungry\u201d or \u201cI want to eat lasagna because it\u2019s my favorite food.\u201d\u00a0<\/p>\n
Artificial intelligence and machine learning algorithms are a long way from self-awareness because there is still so much to uncover about the human brain\u2019s intelligence and how memory, learning, and decision-making work<\/p>\n
Learn more about artificial intelligence with free resources<\/h2>\n
Discover fresh insights into your career or learn about trends in your industry by subscribing to our LinkedIn newsletter, Career Chat! Or if you want to keep learning more about AI tools and how to use them, check out these free re<\/p>\n
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Watch on YouTube:<\/b>How AI is Transforming Business (And Your Morning Coffee)<\/a><\/p>\n<\/li>\n<\/ul>\n
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Find your path:<\/b>AI Learning Roadmap: From Beginner to Expert<\/p>\n<\/li>\n<\/ul>\n
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Access online glossaries:<\/b>Artificial Intelligence Glossary: Learn AI Vocabulary<\/p>\n<\/li>\n<\/ul>\n
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\nStart 7-day free trial<\/p>\nArticle sources<\/h2>\n
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The Conversation. \u201cUnderstanding the Four Types of AI, From Reactive Robots to Self-Aware Beings, https:\/\/theconversation.com\/understanding-the-four-types-of-ai-from-reactive-robots-to-self-aware-beings-67616.\u201d Accessed May 5, 2026<\/p>\n<\/li>\n<\/ol>\n
Keep reading<\/h2>\n
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- What Are the Differences Between Machine Learning and AI?
\nFebruary 12, 2026<\/li>\n - What Does AI Stand For?
\nFebruary 6, 2025<\/li>\n - What Is an AI Engineer? (And How to Become One)
\nMay 27, 2026<\/li>\n - What Is Deep Learning? Definition, Examples, and Careers
\nDecember 1, 2025<\/li>\n - How Does AI Work? Basics to Know
\nJuly 15, 2026<\/li>\n - Is AI Dangerous?
\nFebruary 7, 2026<\/li>\n<\/ul>\nUpdated on May 31, 2026
\nWritten by:<\/p>\nCoursera Staff<\/h3>\n
Editorial Team<\/p>\n
Coursera\u2019s editorial team is comprised of highly experienced professional editors, writers, and fact<\/p>\n
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals<\/p>\n
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- What Are the Differences Between Machine Learning and AI?
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