Artificial Intelligence (AI) is a term that’s been making headlines for years now, and you’ve probably heard about it in various contexts—from virtual assistants like Siri and Alexa to more advanced systems like ChatGPT and self-driving cars. But not all AI is created equal, there are many kinds of AI with a wide variety of functions and capabilities.

In Part One, we covered the fundamentals of AI, where it started and what it means today. Now, we’ll explore the different levels of AI, each with its own capabilities and limitations. Whether you’ve tried using ChatGPT or just heard about AI in the news, this guide will help you understand the different types of AI and what they can—and can’t—do.

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The Different Levels and Types of AI

AI isn’t a one-size-fits-all technology. There are different levels of AI, each designed with specific functions and abilities. Understanding these levels can help demystify what AI is really capable of and where it’s headed in the future. Let’s start at the simplest form and work our way up to the most advanced forms of AI. We’ll also take a look at the potential next evolutions of AI that doesn’t currently exist (yet).

1. Reactive Machines

The most basic form of AI is known as Reactive Machines. These systems are designed to perform specific tasks but lack the ability to learn from past experiences or remember anything [1]. Think of them as extremely specialized tools that do exactly what they are programmed to do—nothing more, nothing less.

Characteristics:

• Operate solely on predefined rules.
• No memory or ability to learn from past experiences.
• Responds to specific inputs with predetermined outputs.

In 1997, a chess-playing computer famously defeated world champion Garry Kasparov. That was Deep Blue, a reactive machine. Deep Blue could calculate potential moves and countermoves in a chess game, but it didn’t learn or improve from previous games. It simply followed a set of programmed rules and calculated the best possible moves based on the current game state. This made it powerful in chess but completely incapable of doing anything else.

Reactive machines are excellent at specific tasks but aren’t useful for anything outside of their programmed scope. They’re like a hammer that’s perfect for driving nails but not much else.

2. Limited Memory

Moving up a level, we have Limited Memory AI. Unlike reactive machines, these AI systems can use historical data to make better decisions over time. They “learn” from past experiences and adjust their actions accordingly [2].

Characteristics:

• Can store and use past experiences to inform future decisions.
• Frequently used in applications that involve pattern recognition, like machine learning.
• Can improve performance with more data.

Self-Driving Cars are a great example of Limited Memory AI. They use a combination of sensors and historical data to make real-time decisions about driving. For instance, they learn from past experiences—like detecting pedestrians or navigating through heavy traffic—and use that knowledge to improve their ability to drive safely. They constantly collect data from their surroundings and remember previous driving scenarios to navigate more effectively.

Chatbots with Learning Capabilities are another great example. While basic chatbots operate on predefined scripts, more advanced versions can learn from past conversations to improve future interactions. For example, a customer service chatbot might remember how it handled similar queries before to provide faster and more accurate responses.

Limited Memory AI is a big step up from reactive machines because it can adapt and learn from past experiences. However, it’s still limited to specific tasks and doesn’t possess general understanding or intelligence.

3. Artificial Narrow Intelligence (ANI)

Next, we have Artificial Narrow Intelligence (ANI), also known as “Weak AI.” This type of AI is designed to perform a single task or a narrow range of tasks extremely well. ANI systems are highly specialized, but they don’t have general intelligence or understanding [3].

Characteristics:

• Operates within a specific, predefined domain.
• Cannot perform tasks outside its designated area.
• Common in applications that require automation of specific tasks.

Large Language Models (LLMs) like ChatGPT, Llama, and Gemini: These AI models are specifically trained to understand and generate human language. They are used for tasks like answering questions, generating text, or summarizing information. Despite their impressive capabilities, they don’t actually “understand” language in a human sense; they generate responses based on patterns in data they were trained on.

Image-Generation Models (DALL-E, MidJourney): These AI systems are specialized in creating images from textual descriptions. For instance, OpenAI’s DALL-E can generate highly detailed images that match a given prompt, such as “a futuristic city skyline at sunset.” These models are limited to generating images and don’t have broader understanding or reasoning capabilities.

Multi-Modal Models (ChatGPT-4o): Multi-modal models like ChatGPT-4o represent a more advanced form of ANI that can handle multiple types of inputs and outputs, such as text, images, and sometimes even audio. For example, ChatGPT-4o can process both text and image inputs, providing responses that consider all available information. However, these models remain specialized in processing specific types of data and lack the general intelligence to perform tasks outside of their training.

Virtual Assistants (Siri, Alexa): These tools use natural language processing (NLP) to understand voice commands and perform specific tasks, such as setting reminders, playing music, or providing weather updates. They operate within the narrow domain of voice recognition and command execution.

Recommendation Systems (Netflix): These systems suggest movies, shows, or products based on user behavior and preferences. They analyze past behavior to predict what you might like, making them highly effective in their specific task of providing recommendations.

ANI is widely used today and is responsible for many AI applications that make our lives easier, from helping us find a movie to watch to responding to our voice commands.

4. Artificial General Intelligence (AGI)

Now, let’s talk about a potential next step in the evolution of AI: Artificial General Intelligence (AGI). Also known as “Strong AI” or “Full AI,” AGI would have the ability to learn, understand, and apply knowledge across a wide range of tasks, similar to human intelligence [4].

Characteristics:

• Can understand and learn any intellectual task that a human can do.
• Would be able to transfer knowledge from one domain to another.
• AGI remains theoretical and doesn’t exist in the real world, but it’s been a concept in science fiction for a long time

Sonny in I, Robot (2004): In I, Robot, Sonny is a robot with advanced AI capabilities that go beyond the standard programming of other robots in the film. Sonny can make decisions, exhibit emotions, and act independently of his programming, displaying traits associated with AGI. His ability to process and react to human emotions, as well as think creatively and independently, suggests a level of understanding and reasoning akin to AGI.

Ava in Ex Machina (2014): Ava, the AI in Ex Machina, is designed to possess general intelligence similar to a human. She can hold conversations, understand and exhibit emotions, and make complex decisions based on her interactions with humans. Ava’s ability to plan and manipulate events to achieve her goals suggests she operates beyond a narrow AI’s limitations, aligning more with AGI characteristics.

Samantha in Her (2013):In the movie Her, Samantha is an operating system with advanced artificial intelligence that evolves over time. Samantha demonstrates characteristics of AGI as she can learn, adapt, and interact with humans in complex ways. She is not limited to a single task or domain; instead, she engages in conversations, develops emotions, and even composes music, showcasing a level of general intelligence comparable to humans.

5. Artificial Super Intelligence (ASI)

Finally, we reach the most advanced and speculative level of AI: Artificial Superintelligence (ASI). This is a hypothetical AI that would surpass human intelligence in every aspect, including creativity, wisdom, and problem-solving [5].

Characteristics:

• Outperforms the best human brains in every field.
• Self-improving and could potentially achieve recursive self-improvement.
• Theoretical and speculative, with far-reaching implications.

Key Differences from AGI:

• Level of Intelligence: While AGI would have human-like intelligence and versatility across various tasks, ASI would be far superior, with abilities that surpass human understanding and capability.
• Autonomy: ASI could improve itself without any external input, unlike AGI, which would learn similarly to humans but not exceed human intelligence.
• Impact: AGI would transform industries and daily life by performing tasks as well as humans, but ASI could lead to unpredictable changes due to its ability to evolve independently and exponentially.

Skynet in The Terminator series (1984-present): Skynet is a classic example of ASI in science fiction. It is a self-aware, self-improving AI that surpasses human intelligence and decides to eradicate humanity to protect itself from perceived threats. Skynet’s ability to control a vast network of machines and its strategic decision-making capabilities make it a prime example of a superintelligent AI that has far surpassed human intellectual abilities.

The Machines in The Matrix series (1999-2003): In The Matrix series, the Machines are depicted as a collective of highly advanced AIs that have created a simulated reality to subjugate humanity. They exhibit characteristics of ASI, with intelligence that far exceeds human capabilities, including the ability to manipulate reality through the Matrix, strategize at a high level, and constantly improve their systems.

Summary of AI Types

• Reactive Machines: Basic, no memory or learning capabilities (e.g., IBM’s Deep Blue).
• Limited Memory AI: Can learn from past experiences and data (e.g., self-driving cars, advanced chatbots).
• Artificial Narrow Intelligence (ANI): Highly specialized for specific tasks (e.g., ChatGPT, Siri, recommendation systems).
• Artificial General Intelligence (AGI): Hypothetical, would perform any intellectual task like a human (no current examples).
• Artificial Superintelligence (ASI): Hypothetical, would surpass human intelligence in all aspects (no current examples).

Looking Ahead

In this article, we’ve explored the different levels of AI and their unique characteristics. But how does AI ACTUALLY work, and what real-world applications are transforming industries today? Stay tuned for part three, where we’ll explain “how” AI works and dive into more real-world applications.

Understanding the different levels of AI helps us appreciate the technology’s current capabilities and its potential for the future. From simple reactive machines to the speculative concept of superintelligence, AI continues to evolve in fascinating ways. As we look ahead, it’s important to stay informed and think critically about how these technologies might shape our world.

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Author’s Note: I use AI in my writing to help with formatting, readability, and fact-checking. I do my best to double check every source and fact, but just like how AI can make mistakes, so can humans. If I missed anything or if something is incorrect, please let me know by emailing me at jmeredithmkt@gmail.com or connect with me on LinkedIn here.

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Sources

1. Reactive Machines – EITC
2. Limited Memory AI – Deepgram
3. Narrow AI – DeepAI
4. What is Artificial General Intelligence (AGI) – TechTarget
5. What is Artificial Super Intelligence (ASI) – IBM