How many types of AI are you familiar with? I’m not talking about AI algorithms and models like generative AI, ChatGPT, and Dall-E here. I’m talking about the specific “categories” of AI that dictate how specific artificial intelligence solutions function.
After all, “Artificial Intelligence” is a broad concept that defines all technology that allows machines and computer systems to simulate human intelligence.
When we explore all the different types of AI that have emerged over the years and the concepts still materializing, we see an interesting storyline of how far this technology has come.
Here, I’ll introduce you to the seven specific types of AI you should know and what you can expect from these varying solutions.
The Different Types of AI: 7 Types to Understand
Depending on who you ask, different types of AI can be divided into different categories. Computer scientists and researchers categorize AI based on its capabilities and functionalities.
On a broad scale, some innovators also divide the “types of AI” we know today into two segments: Narrow or weak AI (which includes most of the AI tools we know today) and strong or super AI, which refers to the more theoretical (and slightly scarier) forms of AI with more human capabilities.
Let’s break these types of AI down.
The 3 Types of AI Based on Capabilities
The three kinds of AI identified by their capabilities are defined based on how they learn and how they can apply their knowledge to certain applications. Currently, there are three commonly referenced types of AI defined by capabilities, but only one of these types is universally accessible.
1. Narrow AI (Artificial Narrow AI)
Narrow AI, or Artificial Narrow AI (ANI), is also known as “weak AI,” which I consider to be a bit of a misleading definition overall. Ultimately, Narrow AI is really the only kind of artificial intelligence most of us are familiar with today. It’s the AI that powers everything from Siri to OpenAI’s ChatGPT and IBM Watson. Narrow AI is called “narrow” because it’s limited in scope.
Weak AI systems are designed to carry out specific tasks or commands, like generating an image from a text prompt or suggesting a route when you check your GPS. ANI solutions are built to excel in one specific area, but they still utilize advanced AI techniques, like neural network algorithms, machine learning, and deep learning.
For instance, natural language processing (NLP) is an incredible resource for customer service and voice-based search, but it can only understand and respond to voice commands.
Other examples of “Narrow AI” include, self-driving cars, AI virtual assistants (like ChatGPT), and image recognition tools.
2. General AI (AGI)
General AI, or Artificial General Intelligence (AGI), is the type of AI often referred to as “strong AI”. It might sound like the term we’d use to describe the latest cutting-edge solutions in the AI space, like generative AI or conversational AI. However, it only defines “theoretical” models.
Researchers believe that AGI solutions will think, learn, and perform various actions like human beings. They’ll be able to perform multifunctional tasks, act like human beings, showcase and understand emotions, and develop new skills over time.
If that sounds like another term for an AI system using machine or deep learning, AGI tools won’t require human input. They won’t need people to provide training for their underlying models. They’ll learn just like human beings do.
Right now, Artificial General Intelligence (AGI) is still a work in progress, and many ethical and government groups are concerned about it. While we haven’t been able to build an AGI model yet, supercomputers, quantum hardware, and generative AI models could pave the way to enabling this type of AI in the future.
3. Artificial Superintelligence (Super AI)
Super AI is another of the many types of AI that simply doesn’t exist at this point. It’s still just the stuff of science fiction. However, you might have said that about something like ChatGPT a few years ago. If Artificial Superintelligence (ASI), is every realized, then it would be able to think, reason, learn, and make judgements in a way that surpasses human beings.
In other words, these AI systems will possess more cognitive abilities than we currently dream of. ASI would potentially act as the backbone technology of AI systems that are entirely self-aware, have their own understanding of the world, and show a genuine consciousness.
Some theorists believe that Super AI will be able to feel emotions, express needs, and have beliefs of its own. The concept of Super AI fuels the popular concern that one day AI will be able to replace human beings entirely. Fortunately, right now, it’s all just speculation.
The 4 Types of AI Based on Functionality
When we define types of AI by functionality, we look at how systems apply their learning capabilities to process information, respond to data, and interact with an environment. There are four types of AI defined by their “functionality,” but again, only some of them exist right now.
1. Reactive Machine AI
Reactive machine AI is probably the simplest form of “narrow AI” we’ve encountered in recent years. As you might expect, these systems are “reactive”. They don’t have any memory and are designed to perform specific tasks. They can’t learn from past experiences or improve functionality based on previous outcomes or decisions.
Instead, they simply respond to the data they have at any moment. Reactive machine AI solutions were developed using statistical math and are excellent for performing basic automatic functions, like recommending items to watch on Netflix or filtering spam from your email inbox.
Plenty of examples of reactive machine AI, such as the IBM Deep Blue bot that beat Garry Kasparov (the Russian chess master) in a 1977 match. We also have simpler examples like Amazon’s AI recommendation engine, or the AI tool that scans for phishing and malware examples in your email platform.
2. Limited Memory AI
Limited memory AI encompasses the most exciting types of AI applications that have emerged in recent years. This type of artificial intelligence can store data, recall past events, monitor specific situations over time, and make predictions. It can actively build on its own knowledge and become more effective at completing tasks.
The core technology powering limited memory AI is deep learning, an enhancement of the machine learning concept that allows computers to mimic the function of a human brain. Deep learning gives machines access to things like neural networks, which allow them to absorb data from experiences and apply them to future tasks.
Notably, this type of AI only has “limited” memory. It can retain information for a short time, but that’s it. However, it can improve its performance as it accesses more data.
Today, examples of limited-memory AI are everywhere. Generative AI applications like ChatGPT, Google Gemini, and Dall-E use limited memory AI to generate content. Virtual assistants like Siri and Alexa combine limited memory AI with natural language processing to respond to users.
Even self-driving cars (while still in development) use limited-memory AI to understand the world around them and make intelligent decisions about when to speed up, slow down, make a turn, or hit the brakes.
3. Theory of Mind AI
Now, we come to one of the types of AI in the functionality segment that’s still theoretical. Theory of Mind AI falls under the “General Artificial Intelligence” umbrella. While it hasn’t been realized yet, it would be able to understand other people’s thoughts and emotions.
The term “Theory of Mind” even comes from psychology. It describes how human beings can read the emotions of others and predict their actions based on that information. Because Theory of Mind AI could understand human reasoning and motives, it would be able to personalize interactions with people based on their unique emotional needs.
It would also be able to understand and contextualize content and artwork, which generative AI apps can’t do comprehensively at this stage. Theory of Mind AI could deliver numerous benefits to the tech world, but it also poses risks. It will take a long time for AI to understand how to process human emotion, after all. Plus, if AI can process emotions, it would be more effective at replacing human beings in certain careers.
While Theory of Mind AI hasn’t been realized yet, there’s plenty of research out there discussing the concept, such as papers that highlight how a self-driving car would use Theory of Mind to make emotional and ethical decisions in a car.
Plus, Emotion AI, a concept within the Theory of Mind landscape, is in development. AI researchers believe this will help AI systems analyze voices, images, and other types of data to understand and respond to humans on an emotional level.
4. Self-Aware AI
Perhaps one of the scariest types of AI for most people, Self-Aware AI is a functional AI class given to applications that would possess the capabilities of Super AI systems. It refers to systems that possess self-awareness, and many experts refer to the development of this technology as the AI point of singularity.
Like the Theory of Mind AI, Self-Aware AI doesn’t exist yet. However, if we create these systems, they can understand themselves, their functions and traits, and human emotions or thoughts. They would also have their own emotions, beliefs, and needs.
Although this might sound exciting, experts believe if we ever create self-aware AI, we’ll reach a point where technology is beyond our control. This presents some risks.
No examples of self-aware AI have been produced yet, although Hanson Robotics raised some concerns when they introduced Sophia, a bot with limited memory capabilities that seems to replicate human behavior and thought patterns.
The Types of AI We Can Access Today
Our current AI solutions all fall into the “narrow AI.”
However, it’s worth remembering that Narrow AI is anything but narrow. The applications for this form of artificial intelligence are numerous. For instance, we already have:
- Computer vision: Narrow AI solutions with computer vision can interpret and analyze visual information. They can identify and classify objects within video and image footage, enabling image recognition and classification, facial recognition, object tracking, content-based image retrieval, and more. They can even power autonomous self-driving cars.
- Natural language processing: Natural language processing in narrow AI empowers machines to understand and interpret written and spoken human language. It uses neural-network-based algorithms to process data and respond to human beings. We’ve seen this technology in chatbots, virtual assistants, and contact centers.
- Machine learning: Machine learning systems, using supervised and unsupervised learning, use limited-memory AI technologies to gradually improve and become more efficient in their roles over time. Some modern AI solutions even use neural networks and generative adversarial networks to build on the capabilities of machine learning and deep learning.
- Robotics: AI systems powered by narrow AI appear frequently in the robotics landscape. Reactive AI, in particular, can commonly be found in the industrial landscape, where machines complete repetitive tasks like sorting products. In healthcare, robotic systems can even use narrow AI to help surgeons monitor vitals and detect issues.
- Expert systems: Expert systems with narrow AI functionality can be trained on huge volumes of data. They can emulate the human decision-making process with neural networks and deep learning. Plus, they can evaluate vast amounts of data to uncover trends and help businesses predict future events.
Understanding the Types of AI
Ultimately, only a fraction of the types of AI identified by computer scientists are accessible today. That could be a good thing or a bad thing, depending on your perspective.
What we can see, as we browse through all of the types of AI mentioned above, is that AI is growing more advanced. We started off with simple reactive systems based on statistical models. Now, we have solutions that can create content, learn from their previous actions, and improve over time.
Years from now, concepts like “General AI” and “Super AI” may become more than theoretical. However, it remains to be seen what that will mean for the future of the human race.
