Updated on 16th May, 22 667 Views

This blog will be very helpful if you want to explore AI and its types. This blog will cover the following topics:

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If you are to mention a technology that has revolutionized the 21st century entirely, it will, without question, be AI. AI has slowly seeped into our everyday lives, which is why it is crucial to have an understanding of this technology. This blog on the types of AI with examples will help you understand the different stages and categories of AI.

What is Artificial Intelligence?

The term “artificial intelligence” was coined in 1956 by John McCarthy. According to him, Artificial Intelligence is the science and engineering of making intelligent machines. AI is a cross-disciplinary approach, combining multiple fields of studies such as mathematics, linguistics, computer science, psychology, etc.

The intelligent machines in AI are developed from enormous volumes of data. The systems learn from past learning and experiences to perform humanlike tasks enhancing the speed, precision, and effectiveness of tasks. Complex algorithms and methods are implemented in Artificial Intelligence to build machines that can make decisions on their own. The core of AI comprises machine learning and deep learning.

Some sectors of business that use AI are:

  • Transportation
  • Healthcare
  • Banking
  • Retail
  • Entertainment
  • E-commerce

Now that we have covered what AI is, let us take a look at its different types.

Different Types of Artificial Intelligence

As we know, AI has become a part of our day-to-day lives. That is why it is important to understand the different types of AI. Artificial Intelligence can be categorized into various types based on capabilities and functionalities, lets learn one by one the different types of artificial intelligence.

Artificial Intelligence TypesBased on Capabilities

Artificial Narrow Intelligence (ANI)

  • ANI is a types of Artificial Intelligence that is used for only one narrow task. It is one of the most common types of AI in use today.
  • Because ANI is not too intelligent to do its own work beyond its limitations, it is also known as a weak AI.
  • Some examples of ANI are self-driving cars, chess-playing machines, image recognition, speech recognition, and purchasing suggestions on e-commerce sites.
  • However, each ANI contributes to the building of a strong Artificial Intelligence.

Some examples of ANI are mentioned below:

  • Apple’s Siri is an example of an ANI that functions with a limited predefined range. It often has difficulty with tasks outside its range of abilities. 
  • IBM Watson is another example of an ANI that applies machine learning, natural language processing, and cognitive computing to process information and answer queries. 
  • Other examples of ANI include Google Translate, recommendation systems, image recognition software, Google’s page-ranking algorithm, and spam filtering.

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Artificial General Intelligence (AGI)

  • Among types of Artificial Intelligence, AGI has the ability to think and make decisions like humans.
  • The purpose behind AGI is to make a system that is smarter and can act like a human on its own.
  • Although they do not exist currently, researchers are focusing on developing machines based on AGI.

Some examples of AGI are mentioned below:

  • The K computer, built by Fujitsu, is one of the more prominent attempts at achieving AGI. It takes close to 40 minutes to simulate a single second of neural activity.
  • The supercomputer, Tianhe-2 holds the record for 33.86 petaflops or quadrillions of cps (calculations per second). While that sounds exciting, the human brain is capable of significantly more, one exaflop or a billion cps.

Artificial Super Intelligence (ASI)

Hypothetically, ASI surpasses human intelligence. It can perform tasks better than humans. This concept sees AI evolved so much so that it is akin to human sentiments and experiences, i.e., it evokes emotions, beliefs, needs, and desires of its own. 

Some of the critical characteristics of ASI include thinking, making judgments, solving puzzles, and making decisions on its own.

  • ASI is a types of Artificial Intelligence where machines will surpass human intelligence and will be able to perform any task better than humans.
  • Also known as strong AI, ASI has the ability to think, solve puzzles, reason, plan, learn, communicate, and make judgments.
  • Currently, there is no proper example of ASI. However, with some of the industry leaders being focused on building strong AI, ASI will be materialized soon.

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Artificial Intelligence TypesBased on Functionalities

Depending on the functionality of AI-based systems, AI can be categorized into the following four types:

  • Reactive Machines
  • Limited Memory
  • Theory Of Mind
  • Self-awareness

Reactive Machines

  • Among types of Artificial Intelligence, Reactive machines are the most basic and oldest AI system. They are reactive and do not use past memories in current decision-making.
  • Reactive machines involve a computer system perceiving the world and acting on what it sees.
  • Reactive machines focus on just the current scenario and then react to it.
  • As per Rodney Brooks, an Artificial Intelligence researcher, reactive machines do not have an idea of the wider world; therefore, they cannot function beyond the specific tasks assigned to them.
  • Examples of reactive machines are IBM’s Deep Blue Systems and Google’s AlphaGo.

Limited Memory

  • Limited memory machines have the ability to learn from historical data to make decisions.
  • However, the data stored in the limited memory can be accessed only for some period of time.
  • Chatbots, self-driving vehicles, and virtual assistants such as Siri are some examples of limited memory machines.

Limited-memory Artificial Intelligence is used in self-driving vehicles. It monitors how other vehicles are moving around a particular vehicle, at present and as time passes. This collected data then gets added to the static data of the Artificial Intelligence machine such as traffic lights and lane markers. 

Data like this can help a vehicle decide when to change lanes or avoid cutting off another vehicle. Mitsubishi Electric has been aiming to improve this technology for self-driving cars.

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Theory of Mind

  • Theory of mind understands people, creatures, emotions, and objects in the world and interacts accordingly.
  • Among all the types of artificial intelligence, this AI is not developed yet. Nonetheless, some researchers are investing effort in its creation.

A real-world applications of AI are theory-of-mind and AI is the robot head, Kismet, which was built in the late 90s by Dr. Cynthia Breazeal, an MIT researcher. This robot head can mimic as well as recognize human emotions, which are both key advancements in this technology. However, Kismet cannot follow gazes or convey attention to humans.

Another example where this type of Artificial Intelligence is implemented is Sophia from Hanson Robotics. The cameras present in Sophia’s eyes, which work together with computer algorithms, allow her to see. This gave her the ability to sustain eye contact, recognize individuals, and follow faces.

Self-awareness

  • Self-awareness is considered the final stage in the development of Artificial Intelligence.
  • Machines have their own consciousness and self-awareness.
  • Machines with self-awareness will be more intelligent than human beings.
  • No such machines are currently in existence; this is a hypothetical concept so far.

Self-awareness AI hypothetically has the ability to understand its internal conditions, traits, and states as well as perceive human emotions. Machines using this type of Artificial Intelligence  will not only be able to understand and evoke emotions in those they interact with but also have emotions, beliefs, and needs of their own.

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Conclusion

It is difficult to imagine how our world will change when more advanced types of AI come into existence. What is known for sure is that AI is still in its elementary phase and has a long way to go.

Instead of focusing on how far we are from creating self-aware machines, we should turn our attention and efforts toward understanding how a machine can self-train and make decisions based on past experiences.

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