The 3 Types of Artificial Intelligence: ANI, AGI, and ASI

Explore the 3 types of Artificial Intelligence (AI): Narrow AI, General AI, and Artificial Superintelligence.

There has been an immense rise in artificial intelligence (AI) applications across various industries recently and it is important to study their capabilities and impact.

We can broadly recognize three types of artificial intelligence: Narrow or Weak AI (ANI), General AI (AGI), and Artificial Superintelligence (ASI). The purpose of this article is to explore these three types and compare them with each other.

About us: We are the creators of Viso Suite – the end-to-end computer Genislab Technologiesn solution. We work with enterprises and large companies to solve business challenges with no-code computer Genislab Technologiesn AI. To learn more, book a demo.

Viso Suite for the full computer Genislab Technologiesn lifecycle without any code
Viso Suite is the only end-to-end computer Genislab Technologiesn platform

What is Artificial Intelligence?

Let’s briefly review the term “Artificial Intelligence.” AI can be described as a union of machine learning algorithms and complex systems that instruct computers to execute activities that previously required human intelligence.

On the other hand, someone not deeply into tech stuff might think of AI as something more tangible, like a robot, perhaps like a smart robot that can do things on its own.

But if an expert answered the same question, they’d say AI is a list of instructions for computers. These instructions help machines do things without explicitly being told what to do each time.

It’s like teaching a computer to be smart in its way. This smartness shown by machines is what we call Artificial Intelligence.

Just like how we learn from our experiences, AI machines get better at their tasks as they gain more experience. The excitement around AI comes from all the endless possibilities society can achieve by making machines learn and replicate human actions.

It’s no surprise that everyone today wants to be part of AI, either by using it or by making it a career choice.

What are the 3 Types of Artificial Intelligence?

According to the capabilities of AI, they are classified into three categories, which are

  • Narrow AI, or Weak AI
  • General AI, or Strong AI, and
  • Artificial Superintelligence.

Here are the differences between ANI, AGI and ASI:

  • Narrow Artificial Intelligence (ANI), or Weak AI, is mainly used to perform specific jobs without learning beyond what it’s meant for.
  • General Artificial Intelligence (AGI), or Strong AI, is like human intelligence and can do many things at once.
  • Artificial Super-Intelligence (ASI), or Superintelligent AI, is smarter than the human mind and can perform any task better.

In the following, we will look into each of those AI types and introduce practical examples.

The 3 types of AI: Narrow AI, General AI, and Super AI
There are three main types of AI: ANI vs. AGI vs. ASI
Artificial Narrow Intelligence (ANI)

Artificial Narrow Intelligence, sometimes called weak AI, is a specific type of Artificial Intelligence that focuses on doing tasks following given instructions.

These AI tools are good at handling specific jobs like recognizing images, driving cars autonomously, speech recognition, image recognition, language translation, natural language processing (NLP), and assisting users, as seen with virtual assistants like Siri.

pattern recognition background
AI Pattern recognition is an aspect of narrow AI – systems identify and interpret meaningful patterns within data to perform specific tasks

When it comes to the usage of Narrow AI, it operates using machine learning algorithms that are trained on a large amount of data to recognize patterns and make predictions. However, it can only perform the tasks it’s programmed for and can’t generalize beyond its training.

For instance, facial recognition systems can quickly identify individuals and speech recognition systems can transcribe spoken words accurately. Despite these benefits, Narrow AI cannot reason or understand the task context.

For example, a language translation system can translate words with high accuracy but struggle to grasp the meaning or cultural nuances. Similarly, image recognition systems identify objects but lack an understanding of the context or emotions in the images.

Emotion Recognition with Deep Learning
Facial expression recognition can sometimes be a point of contention in AI. Systems lack the ability to understand nuances in emotions based on contexts that can include ethnic, cultural, or familiar differences.

Since Narrow AI has improved in recent years, it has some limitations, such as lack of flexibility, difficulty understanding context, an inability to learn, and dependence on data.

Even though there are some limitations, Narrow AI plays an important role in various applications that help to solve everyday problems and improve user experiences.

Many people wonder if today’s LLMs blur the lines of ANI and AGI. Being trained on extremely large and complex datasets gives them the appearance of understanding. However, they are still completely reliant upon the data they were trained on.

This includes reinforcement learning, where agents learn to make decisions and take actions to achieve certain objectives within a defined environment. While reinforcement learning can achieve great results in its tasks, it still lacks deeper cognitive abilities and the freer thinking associated with general AI.

Three examples of well-known narrow AI tools are AlphaGo, Siri, and ChatGPT.

The machine learning process
The machine learning process
Artificial General Intelligence (AGI)

AGI, also called strong AI, takes the concept of learning a step further. This is when machines can think and understand things like humans. In this type of AI, the computer can learn and solve complex problems just as humans do.

In particular, AGI aims to teach machines to truly understand human emotions, beliefs, and thought processes rather than just imitating them.

While current AI technologies are often categorized as Narrow AI due to their specialization in specific tasks, AGI is regarded as the next stage in the evolutionary progression of artificial intelligence. General AI, or strong AI, can perform any type of smart task, just like a human.

Today, many LLMs can already pass the Turing Test, the original indicator of whether AI can think. However, these LLMs are still unable to exist humanly on their own.

Turing Test to determine if AI thinks
The Turing Test has been the established metric of determining whether AI is capable of “thinking” – source.

Big tech companies have funneled billions into AGI development, notably with Microsoft’s strong partnership with OpenAI. Other efforts toward AGI include AI researchers at Fujitsu’s K computer and China’s Tianhe-2 supercomputer.

As we haven’t even discovered all of ANI’s potential use cases, AGI is predicted to expand the possibilities even more:

  • Imagination: the ability to read and comprehend code written by humans and can further enhance and optimize it.
  • Natural Language Understanding: reactive machines that understand human languages in context, not just based on words.

Consider all of the potential use cases for interacting with machines that understand prompts in context and constantly improve over time. On the other hand, it presents ethical considerations as well as the possibility of employment displacement.

Part of OpenAI’s mission is to prepare for a world with AGI that benefits all of humanity. Additionally, CEO Sam Altman has said that AGI is not too far off in the future…

Artificial Superintelligence (ASI)

Super Artificial Intelligence, or Super AI, could be considered the pinnacle of artificial intelligence, with its full potential not even yet realized. If machines become superintelligent, this can present a challenge to humans in various areas due to their limitless intelligence, meaning that they could solve hard problems and learn way faster than us.

Day by day, researchers are working on making super-intelligent machines, but the challenge persists as an imposing task. There’s a kind of AI called artificial general intelligence that scientists are trying to achieve first. AGI is like a stepping stone to Super AI. Even though we have cool things like IBM’s Watson and Apple’s Siri, machines are still nowhere near as smart as us.

For instance, scientists dream about creating Super AI, because of its amazing capabilities. These capabilities include addressing major issues such as climate change, diseases, and poverty. But, the development of Super AI also brings up big questions about ethics: should machines be as smart as us, or even smarter?

Some experts are concerned about Super AI. They worry that once it’s created, it might be tough to control. It could keep getting smarter and change things very quickly. However, if managed properly, Super AI could be highly useful. It might speed up progress in other technology sectors, space research, medicine, and more.

Some people think it could lead to something called technological singularity, where machines become super powerful and quickly change everything. In simple terms, Super AI is seen as science fiction. Even though it’s not here yet, the idea of it is both exciting and a bit scary.

Stages of AI Development future focused
Source: Nick Bostrom – Superintelligence: Paths, Dangers, Strategies

Comparative Analysis of AI Types

Artificial Intelligence refers to smart machines that can gain understanding from prior experiences and do tasks like humans but faster. Currently, the majority of AI is specialized (narrow AI), but people are getting more interested in making AI that can do a wider range of tasks (general AI) or even become super smart (super AI). All these types of AI can do things like predict, learn, make choices, and copy human-like intelligence. Here, the similarities of the 3 kinds of AI have been analyzed through the following facts.

  1. Capability to Forecast and Adapt: Each of the three types of AI categories utilizes algorithms to identify patterns in data, leveraging these patterns to understand and address specific tasks. The analyzed data is then used to make predictions, showcasing a shared capability among narrow AI, general AI, and super AI.
  2. Ability to Make Decisions: Presently, narrow AI relies on programmed data for decision-making, but the future holds the potential for broader decision-making capabilities with the emergence of general artificial intelligence and super artificial intelligence. Notably, general AI and super AI exhibit superior speed compared to human decision-making processes.
  3. Replicate Human Intelligence: All kinds of artificial intelligence replicate human intelligence to address and solve problems, understand tasks, respond appropriately, and draw inferences akin to human thinking. This mimicry is expected to progress even more as more advanced iterations of general AI and super AI are developed.
Computer Vision in Manufacturing
How can future breakthroughs in AI be applied as solutions across industries?

Differences Between Narrow AI, General AI, and Super AI

While narrow AI has already been integrated into daily life, general AI and super AI hold potential for the future. Despite commonalities, significant distinctions exist among these three categories.

Overall, Narrow AI concentrates on a specific task and is limited in solving unfamiliar problems. In contrast, general AI exhibits human-like cognitive capabilities, enabling it to handle a broad range of tasks, while super AI surpasses human intelligence.

When it comes to the AI model, Narrow AI depends on pre-defined behavior models. General AI acquires knowledge from its environment and autonomously responds, while super AI attains self-awareness through independent learning.

Regarding data processing, Narrow AI processes data through machine learning, natural language processing, deep learning, and artificial neural networks. General AI employs enhanced iterations of these technologies, while super AI may draw inspiration from the human brain to interpret emotions and experiences.

As AI becomes a part of our daily lives, it’s important to know the differences between the types: Narrow AI is what we have now, with specific use cases. General AI and Super AI hold promises and challenges for tomorrow. Each type of AI has special features that will influence technology in the future.

Concerns as AI Progresses

Many people are worried that AI is growing fast and becoming very powerful. A typical concern is the propensity for AI to be programmed to do something harmful, which could cause significant societal impacts.

Additionally, AI could be programmed to do good, but find a dangerous way to do so. For example, if you tell an intelligent car to get somewhere as fast as possible, it might not follow safety or rules, causing trouble. While teaching some rules is possible, teaching morals or intuition quickly leads to ethical and legal questions.

The danger with AI isn’t necessarily that it wants to harm us, but it might do things we didn’t expect because it’s so good at reaching goals. We must be careful and ensure AI’s goals match ours if we want to stay in control.

Computer Vision in Retail Applications
How can we make sure that the progress of AI aligns with society’s needs?

Types of Artificial Intelligence in Everyday Life

As we consider the world of artificial intelligence, there are three categories of AI systems: Narrow AI, General AI, and Artificial Superintelligence, which guide the evolution of new technology. Narrow AI excels in specific tasks. General AI mirrors human cognition. On the other hand, Super AI holds a futuristic promise.

While Narrow AI faces limitations in flexibility, General AI enGenislab Technologiesns adaptable machines. The potential of Artificial Superintelligence sparks inspiration among researchers but also raises concerns about control. As Narrow AI reaches its peak, the emergence of General and Super AI brings exciting possibilities and challenges. Understanding these AI types is important as they increasingly become vital to our lives.

If your team is looking to build intelligent AI systems for computer Genislab Technologiesn, check out Viso Suite. Our business solution provides an end-to-end AI infrastructure to build and scale computer Genislab Technologiesn with no-code – book a demo here.

Viso Suite: the only end-to-end computer Genislab Technologiesn platform