7 Types of Artificial Intelligence

If you’ve ever used Amazon’s Alexa, Apple’s Face ID or interacted with a chatbot, you’ve interacted with artificial intelligence (AI) technology. 

There are a lot of ongoing AI discoveries and developments, most of which are divided into different types. These classifications reveal more of a storyline than a taxonomy, one that can tell us how far AI has come, where it’s going and what the future holds. 

These are the seven types of AI to know, and what we can expect from the technology.

Capability-Based Types of Artificial Intelligence

Based on how they learn and how far they can apply their knowledge, all AI can be broken down into three capability types: Narrow AI, general AI and super AI. Here’s what to know about each.
 

1. Narrow AI

Narrow AI, also known as artificial narrow intelligence (ANI) or weak AI, describes AI tools designed to carry out very specific actions or commands. ANI technologies are built to serve and excel in one cognitive capability, and cannot independently learn skills beyond its design. They often utilize machine learning and neural network algorithms to complete these specified tasks.

For instance, natural language processing is a type of narrow AI because it can recognize and respond to voice commands, but cannot perform other tasks beyond that. 

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

2. Artificial General Intelligence (AGI)

Artificial general intelligence (AGI), also called general AI or strong AI, describes AI that can learn, think and perform a wide range of actions similarly to humans. The goal of designing artificial general intelligence is to be able to create machines that are capable of performing multifunctional tasks and act as lifelike, equally-intelligent assistants to humans in everyday life. 

Though still a work in progress, the groundwork of artificial general intelligence could be built from technologies such as supercomputers, quantum hardware and generative AI models like ChatGPT.  

3. Artificial Superintelligence

Artificial superintelligence (ASI), or super AI, is the stuff of science fiction. It’s theorized that once AI has reached the general intelligence level, it will soon learn at such a fast rate that its knowledge and capabilities will become stronger than that even of humankind. 

ASI would act as the backbone technology of completely self-aware AI and other individualistic robots. Its concept is also what fuels the popular media trope of “AI takeovers.” But at this point, it’s all speculation.

“Artificial superintelligence will become by far the most capable forms of intelligence on earth,” said Dave Rogenmoser, CEO of AI writing company Jasper. “It will have the intelligence of human beings and will be exceedingly better at everything that we do.”

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Functionality-Based Types of Artificial Intelligence

Functionality concerns how an AI applies its learning capabilities to process data, respond to stimuli and interact with its environment. As such, AI can be sorted by four functionality types.
 

4. REACTIVE MACHINE AI

Reactive machines are just that — reactionary. They can respond to immediate requests and tasks, but they aren’t capable of storing memory, learning from past experiences or improving their functionality through experiences. Additionally, reactive machines can only respond to a limited combination of inputs. Reactive machines are the most fundamental type of AI.

In practice, reactive machines are useful for performing basic autonomous functions, such as filtering spam from your email inbox or recommending items based on your shopping history. But beyond that, reactive AI can’t build upon previous knowledge or perform more complex tasks.

Reactive Machine AI Examples

• IBM Deep Blue: IBM’s reactive AI machine Deep Blue was able to read real-time cues in order to beat Russian chess grandmaster Garry Kasparov in a 1997 chess match. 
• Netflix Recommendation Engine: Media platforms like Netflix often utilize AI-powered recommendation engines, which process data from a user’s watch history to determine and suggest what they would be most likely to watch next.

5. LIMITED MEMORY AI

Limited memory AI can store past data and use that data to make predictions. This means it actively builds its own limited, short-term knowledge base and performs tasks based on that knowledge.

The core of limited memory AI is deep learning, which imitates the function of neurons in the human brain. This allows a machine to absorb data from experiences and “learn” from them, helping it improve the accuracy of its actions over time. 

Today, the limited memory model represents the majority of AI applications. It can be applied in a broad range of scenarios, from smaller scale applications, such as chatbots, to self-driving cars and other advanced use cases.

Limited Memory AI Examples

• Chatbots and Virtual Assistants: Chatbots and virtual assistants are forms of limited memory AI that use deep learning to mimic human conversation. As users interact more with these systems, they learn from this data and remember details about the user, allowing them to provide relevant and personalized responses.
• Self-Driving Cars: Self-driving cars continually observe and process environmental data around them as they travel on the road. This helps them predict when they need to turn, stop or avoid an obstacle. 

6. THEORY OF MIND AI

Theory of mind refers to the concept of AI that can perceive and pick up on the emotions of others. The term is borrowed from psychology, describing humans’ ability to read the emotions of others and predict future actions based on that information. Theory of mind hasn’t been fully realized yet, and stands as the next substantial milestone in AI’s development. 

Theory of mind could bring plenty of positive changes to the tech world, but it also poses its own risks. Since emotional cues are so nuanced, it would take a long time for AI machines to perfect reading them, and could potentially make big errors while in the learning stage. Some people also fear that once technologies are able to respond to emotional signals as well as situational ones, the result could mean automation of some jobs.

Theory of Mind AI Example

• Rafael Tena, senior AI researcher at insurance company Acrisure, provided an example to illustrate how a successful theory of mind application would revolutionize the technology: A self-driving car may perform better than a human driver the majority of the time because it won’t make the same human errors. But if you, as a driver, know that your neighbor’s kid tends to play close to the street after school, you’ll know instinctively to slow down while passing that neighbor’s driveway — something an AI vehicle equipped with basic limited memory wouldn’t be able to do.

7. Self-Aware AI

Self-aware AI describes artificial intelligence that possesses self-awareness. Referred to as the AI point of singularity, self-aware AI is the stage beyond theory of mind and is one of the ultimate goals in AI development. It’s thought that once self-aware AI is reached, AI machines will be beyond our control, because they’ll not only be able to sense the feelings of others, but will have a sense of self as well. 

Self-Aware AI Example

• Perhaps one of the most famous of these is Sophia, a robot developed by robotics company Hanson Robotics. While not technically self-aware, Sophia’s advanced application of current AI technologies provides a glimpse of AI’s potentially self-aware future. It’s a future of promise as well as danger — and there’s debate about whether it’s ethical to build sentient AI at all.

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