Most of us use AI chatbots daily now, but have you ever wondered how this technology actually works, or what kinds of systems power these chatbots?
The breakthrough in artificial neural networks happened around 2012, and the technology has evolved and branched out in different directions ever since.
In fact, the subset of AI that powers chatbots like Claude and ChatGPT doesn't represent the entire field. So what kinds of artificial intelligence are out there? Let's find out.
The term Artificial Intelligence (AI) refers to computer systems designed to perform tasks that usually require human intelligence, such as:
In other words, at its core, AI is a field of science about creating machines that can think.
Unlike traditional software, which follows pre-programmed rules, AI systems can improve their performance over time by analyzing data and adjusting their approach based on what they learn.
The idea of AI isn't new. Scientists and researchers have been working on it since the 1950s.
However, it wasn't until recently that advances in computing power and the accumulation of sufficient data allowed for the creation of AI systems that can be used in the real world. One example is the family of GPT AI models that powers the famous ChatGPT chatbot.
Did you know? ChatGPT isn’t AI.
The trademark name ChatGPT refers to a website with a chat interface. But the AI component that runs in the background is OpenAI's AI models, such as GPT-5, GPT-4.1, and Sora 2.
ChatGPT is proprietary, but you can interact with GPT models through many interfaces. Each interface has its own pros, like Overchat AI — a service where you can chat with over 50 AI tools and models in one place.
AI systems can be categorized in different ways, but one of the most useful frameworks divides them into four types based on their capabilities and functionality.
These are:
Let’s learn about each of them in more detail:
Reactive machines are the most basic type of AI. They can analyze current situations and respond to them, but they don't have memory or the ability to learn from past experiences.
These systems are designed to perform specific tasks extremely well.
For example, IBM's Deep Blue — the chess-playing computer that defeated world champion Garry Kasparov in 1997 — was a reactive machine. It could evaluate millions of chess positions and choose the best move, but it couldn't remember previous games or learn new strategies.
Reactive machines are fast and reliable for narrowly defined tasks, but they lack the flexibility to adapt to new situations outside their programming.
Limited memory AI systems can learn from historical data and make decisions based on past experiences. This is the type of AI most commonly used today, and it powers the chatbots and virtual assistants, like ChatGPT and Google Gemini, we interact with regularly.
These systems use machine learning algorithms to identify patterns in data and improve their performance over time.
Self-driving cars, for instance, use limited memory AI to observe other vehicles, road conditions, and traffic patterns, then apply that knowledge to navigate safely.
Did you know? The limited memory aspect means these systems don't retain all their experiences indefinitely. They work with recent data and specific training datasets rather than building a comprehensive, permanent memory like humans do.
If you’re curious to learn more about how these systems work under the hood, read our introduction to artificial intelligence.
Theory of mind AI is a big leap forward from the current AI systems we know and use today — unfortunately, it doesn't exist yet.
This type of AI would understand that humans, animals, and objects in the world have thoughts, emotions, and expectations that influence their behavior.
In essence, theory of mind AI would recognize that others have their own mental states and perspectives. It could interpret emotions, anticipate needs, and engage in more natural, intuitive interactions with people.
Researchers are working toward this capability, but we're still in the early stages. Creating AI that truly understands human psychology and social dynamics remains one of the field's greatest challenges.
Self-aware AI is the most advanced and hypothetical type. This would be AI with its own consciousness, self-awareness, and subjective experiences — essentially, machines that know they exist and can think about their own thinking.
This type of AI belongs firmly in the realm of science fiction for now.
We don't yet understand consciousness well enough in humans to replicate it in machines, and many researchers question whether creating self-aware AI is even possible or desirable.
Most discussions about self-aware AI focus on its ethical implications and potential risks rather than practical development timelines.
Another important distinction in AI categorization is between narrow AI and general AI.
☝️ All AI systems today are narrow AI. General AI is a future goal, and experts debate when — or if — it will be achieved. Some predict it could arrive within decades.
With that, we've covered the four main types of artificial intelligence that exist today, in the past, and in the future.
If you’re interested in learning more about AI and the differences between machine learning systems and deep learning algorithms, check out our article on the differences between basic and generative AI.
