Have you ever felt like Joey when he buys a single encyclopedia?
Talking about AI can sometimes feel like that.
Everyone’s talking about the latest in AI systems, using new vocabulary, new use cases, and cutting-edge tech, and (let’s be honest) it can be pretty hard to keep up.
There are generative AI tools for content creation;
AI research bots that help you answer any question, and even self-driving cars that use the latest in AI technology to navigate the road.
It can all leave you feeling a little confused (just like Joey in that Friends episode.)
If you’re looking to brush up on your knowledge of the most common types of artificial intelligence that are in use today, read on.
This blog will break down the 7 different types of AI, what they’re currently being used for, and what that might mean for the future.
The 7 Types of AI: An Overview
In general, there are 7 types of artificial intelligence.
Each of the types represents a different capability or specialization.
So, what are the 7 types of AI? Let’s break it down.
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| Type of AI | Description | Examples |
| Narrow AI (ANI) | Specialized in one task, lacks memory, and cannot adapt beyond preconfigured parameters. | Siri, Netflix recommendation system, customer service chatbots. |
| Artificial General Intelligence (AGI) | Hypothetical AI with human-like intelligence capable of understanding, learning, and performing complex tasks independently. | Not yet developed. |
| Artificial Superintelligence (ASI) | Hypothetical AI surpassing human intelligence, capable of solving global problems but raising ethical concerns. | Not yet developed. |
| Reactive Machines | Basic AI that responds to real-time data with no memory or learning capabilities. | IBM’s Deep Blue chess AI, automatic doors, smart lights, self-checkout machines. |
| Limited Memory AI | AI with short-term memory allowing it to learn from past experiences and adapt in real-time. | Self-driving cars, fraud detection systems, drug discovery algorithms. |
| Theory of Mind AI | Hypothetical AI capable of understanding emotions, intentions, and beliefs, inspired by human psychology. | Conceptual; could lead to social robots or AI counselors. |
| Self-Aware AI | Hypothetical AI with self-awareness, capable of critical thinking, understanding emotions, and raising significant ethical questions. | Not yet developed (and may be impossible, although it could be the ultimate goal for some) |
1. Narrow AI (Artificial Narrow Intelligence – ANI)
Narrow AI is the most common type of AI in use today, and it’s likely that you’ll already be using narrow AI tools without even knowing it.
Narrow AI, like Siri, that customer service chatbot, or even Netflix’s recommendation algorithm, is ‘narrow’ in that it is really good at one task.
Unlike more complex forms of AI that we’ll look at later, narrow intelligence cannot think, reason, or adapt beyond specific programming; it can just perform one task within preconfigured parameters.
An important distinction between narrow AI and more advanced types of artificial intelligence is its lack of memory.
Narrow AI tools cannot store data and learn from it, nor can they apply learning from one task to another; all its actions must be preconfigured and rule-based.
2. Artificial General Intelligence (AGI)
Now, we’re moving beyond the realm of the real into a potential future type of AI.
Artificial general intelligence is currently hypothetical and would mean that a machine would have human intelligence, with the ability to understand, learn, and perform a huge range of complex tasks independently.
But it can get quite complex when we say ‘human intelligence’ when referring to AGI.
Theoretically, AGI would actually have intelligence that was indistinguishable from a human.
However, more accurately, the AGI would be more intelligent than a human, as its ability to process vast quantities of data would dramatically exceed that of a human brain.
Don’t worry, though.
AGI is still quite far away and would require significant breakthroughs in areas like neural network design, machine learning, and robotics to become reality.
3. Artificial Superintelligence (ASI)
Let’s take the hypothetical even further. A step beyond AGI is artificial superintelligence (ASI), which is intelligence that surpasses human capabilities at every level.
This is AI in one of its most capable forms, and it would be capable of performing complex tasks, reasoning, and solving problems using intellect that surpasses that of humanity.
And that’s a little scary.
Artificial superintelligence would not just replicate human capabilities; it would far exceed them, perhaps even branching into the possibility of self-awareness, human manipulation, and worse.
The implications of this kind of AI on human beings, our society, and the future are completely unpredictable, but it’s likely that this kind of AI would have the capability of solving global problems like poverty and climate breakdown.
The question is, do we really want to know the answer?
Fortunately, the big ethical debates surrounding this type of artificial intelligence are entirely fictional… For now.
4. Reactive Machines
While the last two are examples of 100% hypothetical AI, reactive AI machines are one of the first machine learning models to ever be created and are still important pieces of tech in our everyday.
Reactive machines are the most basic form of AI.
These systems can only respond to real-time traffic data based on programmed rules and cannot learn or adapt over time.
They are limited memory machines or lack memory altogether, so their actions are entirely reactive.
An example of this is IBM’s Deep Blue chess AI system that defeated grandmaster Garry Kasparov in 1997 – a breakthrough in AI development.
Today, you might see reactive machines in robotics and automation, following a preconfigured set of instructions to manufacture something new.
Even more common, reactive machines power repetitive tasks, like automatic doors, automated plane navigation systems, voice commands (like in your Alexa or your home’s smart lights), and even those self-checkout machines you use at your local supermarket.
5. Limited Memory AI
While reactive machines have no memory whatsoever, and AGI has extensive memory to make and form connections between inputs, limited memory AI systems are a balance between the two.
Limited memory AI tools can store data from past experiences and learn from them to improve their performance.
An example of this would be a self-navigating car that learns from past routes to optimize journey times at different parts of the day, advanced algorithms for fraud detection, drug discovery, or even disease prevention.
Limited memory AI is unique because it can adapt to new situations by using its short-term memory, which means it’s capable of dynamic adjustments as the real-time data changes.
6. Theory of Mind AI
Theory of mind AI machines is a concept inspired by psychology and refers to machines that are capable of understanding the complexities of emotions, intentions, and human belief.
This is a challenge, as beliefs and the nuances of human language are not straightforward and are open to interpretation.
For us to develop theory of mind AI tools, significant breakthroughs in cognitive modeling, natural language processing, and machine learning would be required.
However, if it is possible, these AI machines could help humans understand and regulate emotions, working like a counselor or a psychologist.
Theory of mind could pave the way for social robots, that could act as friends, carers, or even partners, to humans.
7. Self-Aware AI
Yet, for some people, self-aware AI is the ultimate aim: a computer that is self-conscious and aware of its own existence.
This super AI would not only be capable of performing a specific task but would be able to understand emotions and morality and think critically about their actions and purpose.
However, of course, self-aware machines raise complex ethical questions.
Is it morally correct to bring life into the world and then use these superintelligent beings for repetitive tasks? Can a machine suffer? How different are machine emotions from human emotions?
All of these are questions that are impossible to answer but which might have significant implications for the future of AI research.
Why Narrow AI is the Most Common Type Today
So, with all these different types of AI, which is the most common type?
Well, the answer is clear: The most common type of artificial intelligence in use today is narrow AI.
That’s because it’s adaptable, scalable, and practical, meaning it can be deployed in a wide range of industries pretty easily.
While it can’t solve complex problems, it can eradicate repetitive or administrative tasks and can be scaled easily.
Plus, the technology is accessible and affordable today, meaning that it’s already at work, transforming the landscape of many industries, including healthcare, finance, manufacturing, retail, and entertainment.
Examples of Narrow AI in Everyday Life
It’s likely that you’ll have access to some narrow intelligence tools as we speak.
Here are a couple of examples of the most common forms you’ll encounter in your everyday life.
Firstly, those chatbots you’ve come to rely on, like ChatGPT or the customer service chatbot your utility provider uses, are all examples of narrow AI.
They use AI algorithms that are programmed to respond to your requests, helping you find answers, process data, and streamline your everyday.
Like chatbots, virtual assistants like Siri, Alexa, and Google Assistant respond to voice commands to help you do repetitive or mundane tasks, creating a playlist, calling your friend, or even sending a text message.
Even those recommendation engines that power your Netflix and YouTube accounts are examples of narrow AI.
They help aggregate your data and then use AI models to suggest what you’ll enjoy next.
These kinds of models aren’t just at use in your home – they also help businesses analyze and process data surrounding customer behavior, helping them deliver a more optimal experience over the long term
Finally, some of the tools you’ve come to rely on (like Undetectable.ai‘s AI SEO Writer, AI Essay Writer and Human Typer) make use of narrow AI models.
Even as they write humanized content that is indistinguishable from the real thing.
These tools are ‘narrow’ because they do one thing exceptionally well – writing content for businesses that rely on content marketing practices.
Key Technologies Behind Narrow AI
But how do these tools work? What’s the hidden tech behind narrow AI that allows it to be so versatile in its application?
Machine Learning (ML)
Machine learning, with elements like deep learning, is the ability of AI systems to learn from data and improve performance over time.
This is a fundamental feature of narrow AI, which can’t be programmed to respond directly to every possible prompt or input, but rather must be able to process data and use it to make connections and synthesize new conclusions.
Natural Language Processing (NLP)
In a nutshell, NLP is the technology that enables machines to understand, interpret, and generate human language.
It’s what allows AI to communicate effectively with users in a way that feels natural.
Think about that ChatGPT prompt that responds to your prompt in a language and tone of voice you can understand or the Undetectable AI humanizer tool, which goes a step beyond analyzing language to create unique, human-sounding text.
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Computer Vision
Finally, computer vision helps AI to ‘see’ visual information from the world, such as facial recognition or image analysis.
To do this, AI models analyze visual data by breaking it into pixels and identifying patterns or features (like edges, colors, and shapes), then making connections to find specific objects it can recognize.
Benefits of Using Narrow AI Today
Narrow AI is everywhere, quietly making life simpler and work more efficient.
It takes on repetitive and time – consuming tasks, freeing us humans up to focus on creative or more complex tasks.
This lightens the administrative burden and gets things done faster.
Another big advantage is how scalable it is — narrow AI can handle massive amounts of data and interactions all at once, something no human could manage.
It’s also incredibly accurate, catching details humans might miss, like spotting fraud in banking or detecting early-stage illnesses in medical scans.
Plus, because it’s affordable and accessible, it’s not just businesses that benefit.
Individuals can use narrow AI to help them in their daily life or even in their
Challenges and Limitations of Narrow AI
But, while narrow AI certainly has its benefits, there are some drawbacks to the technology.
The most obvious is that these tools are not flexible.
Each system is built to do one thing really well and can’t adapt to tasks outside its specific programming.
For example, the AI behind Netflix’s recommendations isn’t going to help you book a flight— it’s just not built for that, and its memory and deep learning limitations mean that it won’t ever have the capability to.
This means that different uses require different AI models that need to be programmed and built specifically for that one use case.
That means potential duplications of work, expensive programming requirements, and complex tech stacks to do multiple actions as required.
Another issue is data.
To work well, narrow AI needs huge amounts of high-quality, unbiased data. If the data is flawed, the AI’s output will be too.
We’ve already seen this when it comes to AI bias, and this is a challenging hurdle to overcome with serious real-world consequences.
And finally, there are the ethical concerns.
Automation is great for efficiency, but it can also lead to job displacement and other societal challenges that need careful consideration.
While AI can automate repetitive tasks, there are many people who rely on those tasks for their livelihood.
To what extent should we allow AI to take over those roles, and are there enough creative, challenging roles to go around?
Will General or Superintelligent AI Take Over?
But something we’ve only touched on in this article is the debate about super AI algorithms taking over the world.
There are serious ethical, moral, and legal concerns about some of these artificial intelligence types. What will this debate mean for our future?
At present, the idea of general AI or superintelligent AI remains firmly in the realm of science fiction.
That said, the prospect of these advanced forms of AI is worthy of conversation.
On one hand, they could solve huge global problems.
On the other, they raise serious questions about control, safety, and the impact on humanity.
For now, though, we’re still a long way off, and narrow AI remains the most practical and impactful form of artificial intelligence in use today.
Final Thoughts
All of the 7 types of AI are shaping the present.
Narrow AI is already here, making daily tasks smoother and industries more efficient.
It’s in your Netflix recommendations, your smart home system, and many of the content creation tools you might use at work or in your studies.
And AI won’t stop there.
As the different types of AI continue to evolve, we’re in for an exciting future, with new types of artificial intelligence impacting our roles, even our relationships, and our decision-making.
For now, the focus remains on refining and responsibly using the tools we already have – tools that, when used well, can make life better for everyone.
In the meantime, the next time the topic of conversation shifts to, “What is the most common kind of AI used today?” You’ll be equipped with an intelligent answer (Friends humour notwithstanding).