Types of AI: Everything You Need to Know

AI is a hot topic right now, with established companies like OpenAI, Google, Anthropic, Perplexity, and newer players like DeepSeek pouring billions of dollars into development to power software products like ChatGPT and Claude. But what are the distinctions between different models, classifications, and training methods? This is important for understanding the various AI platforms available, so we’re covering the categories that AI models can fall into, as well as common comparisons between classifications like AGI and ASI.

Machine Learning vs. AI: What is ML?

Starting with some groundwork, AI (artificial intelligence) is a broad term used to describe a wide variety of different technologies. At AI’s core is a machine’s ability to mimic human-like behaviors such as learning and problem-solving. Under the “umbrella” of AI, there are several smaller categories that get progressively more advanced.

ML (machine learning) is one of these AI subsets, focusing on pattern recognition. It’s a relatively rudimentary method of AI (compared to others we’ll discuss below) that teaches systems to perform specific, narrow tasks without explicit programming. Instead, ML relies on “learning” from patterns in data over time to improve accuracy and efficiency.

For example, a machine learning algorithm could be used for image recognition to identify cars. It would start with a basic training dataset, but might initially flag anything with wheels as a car (including motorcycles, buses, etc.). Over time, supervised learning would correct what is and isn’t a car, and the algorithm learns to look for more specific features and shapes, improving how well it can scan images and correctly identify cars.

Artificial Intelligence vs. Machine Learning vs. Deep Learning

So what about “deep learning”? This is yet another subset of AI (and a subset of machine learning) that uses even more advanced methods to improve performance. Deep learning leverages neural networks, which are designed to mimic brains by using nodes (like artificial neurons) to send signals to each other, working together to solve more complex problems. Each node performs a simple calculation based on the information passed from the previous node. This method of learning and improving enables deep learning algorithms and neural nets to be significantly better at predictions based on previous knowledge.

Another difference to keep in mind, which often comes as a result of model complexity, is transparency. As algorithms become more complicated and advanced (like deep learning and neural networks), the more difficult it becomes for developers, testers, and users to interpret why they came to a decision. It’s much easier to see where a line of reasoning went astray when there are only a handful of factors to consider, but when models have billions or trillions of parameters, it’s much more difficult to pinpoint where an issue stemmed from.

AI vs. RPA (Robotic Process Automation)

Another common AI comparison is to RPA (Robotic Process Automation). For context, RPA is a type of software that uses virtual agents to automate repetitive tasks like data entry, generating reports, processing transactions, etc. As an example of their differences, RPA might input, validate, and organize data — an AI would then analyze and make decisions based on the data. While there is overlap between RPA and basic AI models, RPA’s method of mimicking rule-based actions separates it from AI, which learns from patterns. In this way, RPA is not a subset of AI like machine learning is.

Types & Categories of AI Models

The main ways that AI is currently categorized or classified are by their capabilities and their functionality. Let’s take a closer look at each of the types within these two frameworks.

Capability-Based

Narrow AI: This makes up most current AI models, where the system is designed to perform a single, specific task (such as coding, image generation, or writing) but can’t expand or apply their skills outside of what they’re programmed to do.
General AI (or Artificial General Intelligence): The next step from narrow AI, AGI is used to describe models that have broader cognitive capabilities, able to match or slightly surpass humans across a variety of tasks, from recommending restaurants and booking your flights to driving your car — all in one model.
Artificial Superintelligence: ASI is a theoretical step further, where AI surpasses human intelligence and the top human minds in every field, able to even perform tasks and solve problems that would be impossible for humans.

Functionality-Based

Reactive: These systems respond to user inputs but have no memory to enable previous mistakes to inform future decisions. Because of this, a specific input will always have the same output.
Limited Memory: As the name suggests, this next type of machine has the ability to evaluate past inputs and actions to learn and improve over time. This is possible through the neural network structure of nodes we discussed earlier.
Theory of Mind: A theoretical category of AI, these would be able to understand that other entities have thoughts, emotions, desires, and intentions, and also how its own actions influence those. This would represent a huge step towards more personalized, human-like interactions with AI.
Self-Aware: The endgame of AI evolution in many sci-fi stories is a self-aware system that has developed consciousness and goes a step beyond understanding the emotions and feelings of others and would hypothetically understand its own existence and internal processes.

Generative AI vs. Predictive AI

The other categories you may come across when looking at AI models are generative vs. predictive systems. The distinction here is fairly straightforward — generative AI creates new content (text, images, audio, etc.) based on existing patterns in training data. Predictive AI analyzes historical data (such as medical records or weather patterns) to forecast future events and behavior. This is why generative AI (like ChatGPT, Gemini, Claude, Perplexity, and DeepSeek) is often used for innovation and creative pursuits like writing, design, and coding. On the other hand, predictive AI is used more often in analysis-based applications across healthcare, finance, and fraud detection.

What is the Most Common Type of AI?

The most popular types of AI right now are narrow AI models that are pushing the boundaries of this classification, paired with limited memory functionalities. Companies like OpenAI, Google, and more aim to break into the next categories (AGI and theory of mind) to create more capable software products to help users.

What Type of AI is ChatGPT?

ChatGPT is a narrow AI with limited memory, because it’s designed to simply generate human-like text responses based on user inputs. While it can generate convincing answers, it doesn’t actually understand the context or meaning behind them. Generative models like ChatGPT (at least in their current state) simply associate words that commonly appear around each other in patterns, which is what separates them from AGI.

The limited memory designation comes from its ability to retain some knowledge (especially in newer iterations like 4o), but technical restraints restrict the amount. Because of this, you may notice that it forgets earlier context or details in longer or more complex conversations.

Narrow AI (Current AI) vs. General AI (AGI)

Now that we understand the core types of AI, let’s assess some of the more common comparisons in more detail. The first major distinction is between narrow AI and AGI, which is particularly relevant to us right now because this transition point is where our technology is currently placed.

As we defined earlier, AGI is the next step beyond narrow AI, where machines can perform a wide variety of tasks, at or surpassing human capabilities. The other distinction is the level of “understanding” that each model has — narrow AI is trained to do a very specific task, and doesn’t have any context for why it’s performing said task. General AI’s wider range of capabilities relies on understanding context and why it’s deciding to take certain actions.

In the context of virtual assistants, narrow AI is represented by Siri or Alexa, who can perform basic tasks like initiating phone calls or searching for simple answers to questions. An AGI assistant might look like Jarvis from the Iron Man movies, able to make reservations, translate languages, perform complex data analysis, offer personalized recommendations, control devices, and more.

AGI vs. ASI (Artificial Superintelligence)

The next step in our AI capability categorization is moving from AGI to ASI. While both have general capabilities, ASI surpasses this by achieving superhuman performance across every domain. Superintelligence also relies more heavily on self-learning, improving its knowledge and abilities to evolve without human intervention or training. Skynet from the Terminator movies is commonly classified as a self-aware ASI. This type of artificial intelligence is also where unforeseen consequences of self-awareness and our ability to reign in the models become far more important.

How is AI Trained?

The last AI distinction we’ll cover in this article is around how they identify patterns and relationships between data points. Training and learning is typically broken down into three sub-types:

Supervised Learning: Similar to learning with a teacher, the AI is trained with a dataset that labels inputs with the corresponding correct outputs. The goal is for the AI model to learn by understanding how inputs are mapped to outputs.
Unsupervised Learning: Similar to taking a test, the AI is trained with an unlabeled dataset and is expected to identify patterns and relationships on its own. This is ideal for larger datasets where it isn’t feasible to label every input and might result in insights that human supervisors may not have found.
Reinforcement Learning: AI models can also be trained using a reward and penalty system. In this method, the AI uses trial and error to make decisions and receives feedback from a supervisor in the form of a reward or penalty based on the accuracy or quality of its outputs.

Stay Ahead of the Curve With High-Quality AI Tools

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Types of AI: Generative, ML, AGI, ASI, & More