Types of artificial intelligence and their application

If you’ve ever used Amazon’s Alexa or Apple’s Face ID, or interacted with a website’s chatbot, you’ve worked with and used artificial intelligence (AI) technology. In recent years, artificial intelligence has experienced many developments, the latest high-profile examples of which are GPT chat and Bing artificial intelligence. There are different types of artificial intelligence, and these categories are more than a simple classification, they tell us how far artificial intelligence has progressed, where it is going, and what the future holds. In this article, we will talk about 7 types of artificial intelligence from the artificial intelligence company Avir and get to know their details. Stay with us.

Types of artificial intelligence at a glance

Narrow AI: Artificial intelligence designed to perform very specific actions that cannot learn independently.

Artificial General Intelligence: One of the types of artificial intelligence that is designed to learn, think and function at the same level as humans.

Artificial Superintelligence: An artificial intelligence capable of surpassing human knowledge and abilities.

Reactive Machines: Artificial intelligence that is able to respond to external stimuli in real time and in the moment. This artificial intelligence is not able to build memory or store information for the future.

Limited Memory: One type of artificial intelligence that can store knowledge and use it to learn and train for future tasks.

Theory of Mind: Artificial intelligence that can sense and respond to human emotions, plus perform the tasks of limited memory machines.

Self-aware: Artificial intelligence that can recognize the emotions of others, plus it has self-awareness and intelligence at the level of human intelligence. Self-awareness is the final stage of artificial intelligence.

Types of artificial intelligence based on capabilities

All types of artificial intelligence can be divided into three types based on how they learn and how much they use their knowledge:

narrow or weak artificial intelligence;

artificial general intelligence;

Cloud artificial intelligence.

In the following, we will get to know the details of each of these artificial intelligences.

1. Narrow artificial intelligence

Narrow or weak artificial intelligence (ANI) describes artificial intelligence tools designed to perform very specific actions or commands. Narrow AI technologies are built to serve a specific type of cognitive capability and cannot independently learn skills beyond what they are designed for. This type of artificial intelligence often uses machine learning and neural network algorithms to complete these specified tasks.

For example, natural language processing AI is a limited type of AI, as it can recognize and respond to voice commands, but cannot do anything beyond that.

Some examples of narrow AI include image recognition software, self-driving cars, and AI virtual assistants like Siri.

2. General artificial intelligence

Artificial General Intelligence (AGI), also called strong artificial intelligence, is a type of artificial intelligence that can learn, think, and perform a wide range of tasks like humans. The goal of general AI design is to be able to create machines that are capable of multi-tasking and act as true and equally intelligent assistants to humans in everyday life.

Although general AI is still a work in progress, the foundation of AI can be built from technologies such as supercomputers, quantum hardware, and generative AI models such as ChatGPT.

3. Artificial intelligence

Artificial superintelligence (ASI) or super artificial intelligence is science fiction artificial intelligence. There are theories according to which, when artificial intelligence reaches the level of general intelligence, it will soon learn at such a speed that its knowledge and capabilities will become even stronger than human intelligence.

AI serves as the backbone technology for fully self-aware and other individualistic robots. Its concept is also what fuels the popular media trend of “AI takeovers,” as seen in movies like Ex Machina or I, Robot. But at this point, it’s all speculation.

“AI will become by far the most capable type of intelligence on Earth,” said David Rogenmoser, CEO of artificial intelligence writing firm Jasper. “This intelligence will have the intelligence of humans and will be much better at everything we do.”

Types of artificial intelligence based on performance

The division of AI based on function relates to how an AI uses its learning capabilities to process data, respond to stimuli, and interact with its environment. In this way, artificial intelligence can be classified according to four types of functions.

4. Reactive machines

The emergence of artificial intelligence began with the development of reactive machines, the most basic type of artificial intelligence. Reactive cars are exactly what their name says, reactive. They can respond to requests and tasks instantaneously, but are unable to store memory or learn from past experiences.

Machines cannot improve their performance through experience and can only respond to a limited number of inputs.

In practice, reactive machines can read and respond to external stimuli in real time. This makes these machines useful for performing basic autonomous functions, such as filtering spam from email inboxes or recommending movies based on recent Netflix searches.

 The most famous reactive machine was the IBM Deep Blue artificial intelligence machine, which was able to read motion cues and beat Russian chess grandmaster Garry Kasparov in a chess match in 1997. But beyond that, reactive AI cannot build on prior knowledge or perform more complex tasks. To apply artificial intelligence in more advanced scenarios, there must be evolutions in data storage and memory management.

5. Limited memory

The next step in the evolution of artificial intelligence is to create the capacity to store knowledge. According to Rafael Tena, senior AI researcher at insurance company Acrisure Innovation, it took almost three decades to achieve this breakthrough.

All of today’s AI systems are trained with large amounts of data that they store in their memory to form a reference model for solving future problems.

“There was a lot of progress (in limited memory) in the ’80s,” Tena said. But this process eventually slowed down. “There were small incremental changes … until deep learning kicked in.”

In 2012, the field of artificial intelligence made great progress. Recent innovations from Google and Image Net have made it possible for AI to store past data and use it to make predictions. This type of AI is known as memory-limited AI because it can build its own limited knowledge base and use that knowledge to improve over time. Today, the limited memory model represents most AI work.

“Almost every existing application that we know of falls under this category of artificial intelligence,” says Rajenmuser. “All of today’s artificial intelligence systems are trained with large amounts of training data that they store in their memory to form a reference model for solving future problems.”

Limited memory AI can be applied to a wide range of scenarios, from small-scale applications like chatbots to self-driving cars and other advanced use cases.

6. Theory of mind

In terms of AI advancements, limited memory technology is the furthest we’ve come, but it’s not the final destination. Machines with limited memory can learn from past experiences and store knowledge, but cannot understand subtle environmental changes and emotional cues or reach the same level of human intelligence.

“Current models have a one-way relationship,” says Rajenmuser. “Artificial intelligence tools like Alexa and Siri don’t feel and react when you yell at them.”

The concept of artificial intelligence that can understand and use other people’s emotions is still not fully understood. This concept is called “theory of mind,” a term borrowed from psychology that describes the human ability to read the emotions of others and predict future actions based on that information.

“Machines may do better than us 90 percent of the time, but that last ten percent, what you describe as common sense, is really hard to come by.”

Tena provided an example to show how a successful application of theory of mind could revolutionize technology: A self-driving car might perform better than a human driver most of the time because it doesn’t make the same human mistakes. But if you, as a driver, know that your neighbor’s kid likes to play down the street after school, you instinctively know to slow down while crossing the neighbor’s street—something an AI vehicle with limited memory can do. Primary will not be able to do.

Theory of mind can bring many positive changes in the world of technology, but it also carries its own risks. Because emotional cues are so subtle, it takes a long time for AI machines to fully read them and potentially make big mistakes during the learning phase. Some people also fear that when technologies can respond to emotional as well as situational signals, the result could mean the automation of some jobs and the disappearance of jobs. But there’s no need to worry just yet – Rajenmoser believes that this hypothetical future, however, is still a long way off.

“Right now, this intelligence is science fiction,” he said. “We’re not even close to developing that kind of AI, so nobody’s going to lose their job to AI.”

7. Self-awareness

The stage beyond theory of mind is when AI develops self-awareness, referred to as AI’s singularity. Once we reach that point, AI machines are thought to be out of our control, as they will not only be able to sense other people’s emotions, but also their own.

“People are afraid of both creating this kind of AI and the consequences of creating it, and they’re worried that this kind of AI will steal our jobs or take over our world,” Rajenmuser said. If this type of artificial intelligence is successfully created, no one knows what impact it will have on the world.”

“If this kind of artificial intelligence is successfully created, no one knows what impact it will have on the world.”

Steps are being taken by researchers and engineers to develop early versions of self-aware artificial intelligence. Perhaps one of the most famous is Sophia, a robot created by the robotics company Hanson Robotics.

Although Sophia is not technically a self-aware AI, her advanced application of current AI technologies offers a glimpse into the potential future of self-aware AI. It’s a promising future as well as a dangerous one – and there are debates about the morality of making AI self-aware and intelligent.

“AI is going to get a lot better at solving real-world use cases, I want to say that I don’t think it [means] the end of humans and the end of work,” Rajenmuser said. “We continue to see AI emerge in useful ways to augment the great work people are already doing.”