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7 Types of Artificial Intelligence You Should Know

  • Bhumika Dutta
  • May 31, 2022
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There is no denying that the world is currently running on Artificial Intelligence. Many industries are adapting to the new changes and indulging in various technical upgrades, involving AI in their sector. AI is no more a buzzword that people ponder over, it is now the reality that is a part of our everyday lives. 

 

Artificial Intelligence is a very complex and broad spectrum of study, most of it being relatively unexplored. Many types of AI can help in different business solutions, and it is important to learn the different types. 

 

In this article, we are going to talk about and understand the different types of AI available in the market and learn their uses.


 

Artificial Intelligence:

 

We are on the verge of the AI Revolution, and there will be much more to come. This may be the ideal opportunity to brush up on our technical knowledge of AI and how we can contribute to the revolution by improving our ability to create effective solutions.

 

The process of creating intelligent machines from massive amounts of data is known as artificial intelligence. Human-like activities are performed by systems that learn from previous learning and experiences. It improves human efforts' speed, accuracy, and efficacy. To create computers that can make judgments on their own, AI employs complicated algorithms and procedures. Artificial Intelligence is built around Machine Learning and Deep Learning.

 

Consider a chatbot that assists restaurant patrons in booking a table to better understand the AI idea. By definition, a chatbot is computer software that has been programmed to answer hundreds of questions about booking a table. That's how it learns about a normal subject discussion flow. Following the training, the chatbot will be able to converse with consumers. However, if a consumer asks for meal recommendations rather than scheduling a table, the chatbot will be of little use because it has not been taught to do so.

 

Today, Artificial Intelligence is used in many sectors like transportation, healthcare, retail, banking, education, E-Commerce, and so much more. Let us now delve into its different types.

 

Also Read | History of AI


 

Types of Artificial Intelligence:

 

Before diving into the many forms of artificial intelligence, it's important to understand how AI is categorized. The two most important aspects in categorizing the various forms of AI are usefulness and technology. As a result, the categorization will take into account how the system works and what technologies it employs to do so.

 

Types of AI based on Capabilities:

 

Based on capabilities, there are three categories of Artificial Intelligence:

 

  1. Artificial Narrow Intelligence (ANI)

  2. Artificial General Intelligence (AGI)

  3. Artificial Super Intelligence (ASI)

 

Let us move forward and learn about each one of them briefly.


 

  1. Artificial Narrow Intelligence (ANI):

 

It is generally known as Narrow AI or Weak AI. Narrow AI is a sort of AI that is capable of doing a certain task intelligently. In the area of artificial intelligence, narrow AI is the most frequent and currently accessible AI. Because narrow AI is exclusively educated for one single activity, it cannot perform outside its field or boundaries. As a result, it's also known as "weak AI." 

 

When narrow AI reaches its boundaries, it might fail in unexpected ways. We can observe that this sort of Artificial Intelligence system can execute one or two jobs as we learn more about what is ANI. It takes advantage of the prior occurrences' training data and learning experiences. 

 

Some examples of Narrow AI include Apple’s Siri, IBM Watson’s supercomputer that uses an expert system approach combined with Machine learning and natural language processing, and other general AI tasks like online chess, self-driving cars, speech recognition, image recognition, etc. 


 

  1. Artificial General Intelligence (AGI):

 

General AI, or Artificial General Intelligence, is a sort of intelligence that can execute any intellectual work as efficiently as a person. It is connected to the Theory of Mind in terms of functioning. This form of AI is still in the works. Researchers are looking for practical ways to incorporate human emotions and other skills into machines so that they can behave like humans. Strong AI is another name for it.

 

There is currently no system that can be classified as general AI and execute any task as well as a person. So far, AI researchers have failed to build robust AI. They'd have to figure out how to make computers aware and program them with a comprehensive range of cognitive abilities. Microsoft has made a $1 billion investment in general AI through OpenAI.

 

Here are some examples of work in the field of general AI:

 

  • Fujitsu developed the K computer, which is one of the world's fastest supercomputers. It's one of the most major attempts at creating a powerful AI. Simulating a single second of brain activity required roughly 40 minutes. As a result, predicting when powerful AI will be realized is challenging.

 

  • The National University of Defense Technology in China constructed the Tianhe-2 supercomputer. It has a cps (calculations per second) record of 33.86 petaflops (quadrillions of cps). Although this sounds thrilling, the human brain is only capable of one exaflop, or a billion clock cycles every second.

 

Also Read | Differences between Narrow AI and General AI


 

  1. Artificial Super Intelligence (ASI):

 

Artificial Superintelligence (ASI)  or Super AI will very certainly be the apex of AI research, as ASI will be by far the most competent form of intelligence on the planet. Because of substantially bigger memory, quicker data processing and analysis, and decision-making skills, ASI will be far superior at everything they perform, in addition to mimicking human intellect.

 

Super AI is smarter than humans and can accomplish any work better than them. Artificial superintelligence refers to AI that has developed to be so similar to human sentiments and experiences that it not only understands them, but also elicits its own emotions, wants, beliefs, and desires. Its existence is still unknown. Super AI's key traits are the ability to reason, solve puzzles, make judgments, and make decisions on its own. 

 

Super AI is still a futuristic Artificial Intelligence idea. Real-world implementation of such systems is still a world-changing endeavor.

 

Also Read | What is Strong AI?



 

Types of AI based on Functionalities:

 

So far, we have learned about the classification of AI-based on capabilities or technicalities. Based on functionalities, there are four types of Artificial Intelligence:

 

  1. Reactive Machines

  2. Limited Memory

  3. Theory of Mind

  4. Self-Aware

 

  1. Reactive Machines:

 

Reactive Machines are the most primitive AI systems, with very limited capabilities. They stimulate the ability of the human mind to respond to various stimuli.

 

Basic operations are performed by reactive machines. This is the most basic level of artificial intelligence. These types provide an output in response to some input. There is no learning involved, and these machines lack memory-based capabilities. Any A.I. system begins with this step. A basic, reactive machine takes a human face as input and produces a box around it to recognize it as a face. The model does not save any data and does not learn.

 

When discussing reactive machines, static machine learning models are reactive machines. Their design is the most basic, and they are available on GitHub repositories all over the internet. These models are simple to download, trade, pass around, and import into a developer's toolbox. 

 

IBM's Deep Blue, which defeated chess Grandmaster Garry Kasparov in 1997, is another well-known example of a reactive AI computer. Deep Blue is unable to draw on any previous experiences or improve via practice. It can recognize the pieces on a chessboard and understand how they move. Deep Blue can forecast what movements it and its opponent will make next. It ignores everything before the present time and examines the chessboard pieces as they currently stand, selecting one of several probable future moves.


 

  1. Limited Memory:

 

A limited memory AI system was one of the first AI systems to 'learn' from its experiences, but its memory is short-lived. They can utilize this historical data for a limited time, but they cannot add it to their experience library. The 'learning' process now leads to technical competence and the ability to make informed judgments. 

AI systems today, particularly those that use Deep Learning, have been 'taught' to respond and learn. Limited memory AI systems combine the capacity to react to and learn from prior data.  

 

Almost all present AI applications that we are aware of fall under this category. All modern AI systems, including deep learning systems, are taught using enormous amounts of training data that they store in memory to create a reference model for addressing future issues. For example, an image recognition AI is taught to name items it scans using hundreds of photos and their labels. When an AI scans an image, it utilizes the training photos as references to comprehend the contents of the image supplied to it, and it labels new images with increasing accuracy based on its "learning experience."

 

There are three types of machine learning models that may accomplish Limited Memory:

 

  • Learning via reinforcement:

 

Through several rounds of trial and error, these models evolve to make better predictions. Computers are taught to play games like Chess, Go, and DOTA2 using this technique.

 

  • Long-term and short-term memory (LSTMs):

 

Researchers reasoned that using previous data to anticipate the next item in a sequence, particularly in language, would be beneficial, therefore they devised a model based on the Long Short Term Memory. The LSTM labels more current information as more significant and those from the past as less essential when predicting the following parts in a sequence.

 

  • E-GAN or Evolutionary Generative Adversarial Networks:

 

Because the E-GAN has memory, it evolves with each development. The model generates something that grows. Because statistics is a math of chance, not a math of exactitude, growing entities do not always pursue the same route. The model may identify a better path, a path of least resistance, as a result of the changes. The model's following generation mutates and evolves in the direction of its ancestor's incorrect route.

 

Also Read | Generative Adversarial Network


 

  1. Theory of Mind:

 

Theory of Mind AI should be able to grasp human emotions, personalities, and beliefs, as well as interact socially, but it is still a long way off. These are still in their early stages, but some examples include self-driving automobiles. A.I. begins to engage with human ideas and emotions in this sort of A.I. The researchers are working to deliver meaningful advances in several disciplines of AI. Multi-dimensional AI development might speed up the development of 'Theory of Mind' AI systems in the next few years.

 

Machine learning models can now help a human complete a task a lot. Alexa and Siri, for example, have a one-way interaction with artificial intelligence. When you shout at Google Maps to drive you somewhere else, it does not give emotional support or remark, Instead, Google Maps continues to provide the same traffic reports and ETAs as before, with no regard for your anguish. Come to think of it, if developed, AI theory of mind AI will be a better companion to humans.

 

Artificial Emotional Intelligence and advances in decision-making theory are two fields of study that are tackling this issue. Michael Jordan discussed part of his Decision-Making research at the Future of ML and AI with Michael Jordan and Ion Stoica event on May 13th, and further information was presented at the ICLR 2020 conference.


 

  1. Self Aware AI:

 

Self-aware AI is just speculative. Such systems are aware of their internal characteristics, moods, and situations, as well as human emotions. These machines will be more intelligent than humans. This sort of AI will be able to comprehend and evoke emotions in individuals with whom it interacts, as well as have its feelings, wants, and beliefs.

 

Given the current state of affairs, this form of AI seems unlikely. However, in the future, it may be conceivable to reach a level of superintelligence. Elon Musk and Stephen Hawkings, for example, have repeatedly cautioned us about AI's development.


 

Conclusion:

 

We may be a long way from constructing self-aware machines that can fix all problems. However, we should concentrate our efforts on figuring out how a computer can train and learn on its own and make judgments based on previous experiences.

 

It's difficult to imagine the status of our planet when more powerful varieties of AI become available. However, there is a long way to go, since AI development is still in its infancy compared to where it is expected to go.

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