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Artificial Intelligence (AI) has materialized as one of the most life-changing technologies of this century. It is outright going to change people’s daily lifestyle and how they interact socially. From chatbots, voice assistants to self-driving cars and facial recognition – AI is becoming increasingly ubiquitous in our daily lives. There are several types of AI, each with its unique capabilities and limitations. Not all AI is created equal.

In this blog post, let us break down the types of AI into different sections and explore their uses.

Understanding the types of AI classification

Reactive AI

Reactive machines may be the most rudimentary type of AI. They are designed to react to specific situations without prior knowledge or understanding of the environment and do not have any memory or ability to learn from past experiences. They analyze data from the environment and provide responses based on pre-programmed rules. It is a popular approach to AI because it allows machines to quickly respond to changing conditions, making them ideal for a wide range of applications. 

One of the most well-known examples of reactive AI is the self-driving car. A self-driving car uses a variety of sensors, cameras, and algorithms to perceive its environment and react to it in real-time. For example, if a pedestrian suddenly steps into the road, the car will use its sensors to detect the pedestrian and apply the brakes to avoid a collision. (provide links to tesla FSD etc.)

Another example of reactive AI is the Roomba, a popular robotic vacuum cleaner. The Roomba uses sensors to detect obstacles and navigate around them while cleaning the floor. It does not have any prior knowledge of the environment, but it can react to changes in the environment and adjust its behavior accordingly.

Other examples of reactive chess-playing computers, voice assistants like Siri and Alexa, patient vitals monitoring devices, etc.

Limited Memory AI

Limited memory AI systems can store limited data and recall this past data. They use this information to make decisions and provide better responses. It is used in situations where there is too much data to analyze, making it difficult for the AI to process everything in real time. These systems can be trained using large datasets and can improve their performance over time. 

Examples of limited memory AI include facial recognition technology used by law enforcement agencies and chatbots used in customer service. One of the most common examples of limited memory AI is the recommendation engine used by streaming services such as Netflix and Hulu. These engines use limited memory to analyze a user’s watch history and provide recommendations for new content based on their preferences. The recommendation engine does not have access to every piece of data the user has ever watched, but it uses a limited amount of historical data to make predictions about what the user might like.

Another example of limited memory AI is in the stock market. Stock market analysts use limited memory AI to analyze historical data and predict future trends. The AI does not have access to every single data point in the stock market’s history, but it uses a limited amount of data to make predictions about future trends. 

Theory of Mind

Theory of Mind (ToM) is a cognitive skill that allows humans and some animals to attribute mental states, such as beliefs, desires, and intentions, to themselves and others. This ability is crucial for understanding and predicting the behavior of others in social situations. Theory of Mind AI aims to develop artificial systems that possess this same understanding of mental states, enabling them to interact more effectively and naturally with humans or other agents.

These systems are still in the experimental phase and as research in this field continues to advance, we can expect to see even more innovative applications of theory of mind AI in the future.

One of the most promising applications of theory of mind AI is in the field of social robotics. Social robots are designed to interact with humans in social situations, such as greeting visitors, assisting customers, or providing companionship to the elderly. Theory of Mind AI allows these robots to interpret facial expressions, body language, and vocal cues to gauge the emotional state of the people they interact with.For instance, a social robot may recognize that its owner is feeling stressed and offer to play soothing music or suggest relaxation techniques. In a hos

One of the biggest challenges in AI is creating machines that can truly understand and interpret human emotions and intentions, which are often complex and difficult to read. Additionally, there are ethical considerations to be addressed, such as ensuring that these machines respect human privacy and autonomy. As this field so will the regulations and laws governing them.


Self-aware AI systems are the most advanced type of AI. They have consciousness and the ability to think and reason like humans. These systems are still purely theoretical and are being studied by researchers in the field of artificial general intelligence.  This development represents a significant paradigm shift, as machines are now capable of not only processing data and making decisions but also understanding their own existence and purpose.

The self aware AI maybe seen in the terminator movies. In these movies the looming presence of Skynet, the Cyberdyne Systems-created AI defense program that becomes self-aware and plots humanity’s downfall is a morbid albeit perfect example of a self aware AI.

In conclusion, AI is a rapidly evolving technology that is metamorphosing the world we live in. There are several types of AI, each with its unique capabilities and limitations. From reactive machines to self-aware systems, each type of AI has the potential to revolutionize different industries and improve our lives. It is important to understand these different types of AI to fully appreciate the potential of this technology and to navigate its ethical implications.

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