Types of AI: A Deep Dive Into Artificial Intelligence

Not long ago, AI was reserved for sci-fi. Today, AI technology is in our daily lives in ways you might not notice. With facial recognition software to unlock your phone, the autocorrect that prevents typos from ruining perfect emails, or streaming services that recommend your next binge, the future is now. 

But what exactly is AI, and how does it work? 

Here’s a breakdown of the basics, from the main types of AI to how it shapes the world. Whether you’re curious about what powers chatbots or how machine learning technology can predict your business’ next big move, we have you covered.

What’s AI? 

AI — short for artificial intelligence — refers to machines that simulate human intelligence. AI works by training algorithms on datasets, allowing machines to understand rules, recognize patterns, and make decisions. 

These simple rule-based systems rely on predefined instructions to make decisions, meaning, “If X happens, then do Y.” More complex AI systems, like machine learning technology and deep learning neural networks, learn from interactions. The new data and inputs you feed them improve their performances over time without constant human maintenance.

The 4 types of AI functionalities

AI functionality is categorized based on how it processes information and interacts with its environment. These four types of AI functionalities highlight progressive levels of AI, from simple rule-based systems to more advanced, self-aware technologies. 

1. Reactive machine AI

This is the most basic type of AI system. Reactive AI responds to specific stimuli in its environment without recalling past experiences or learning from new ones. These machines don’t have memory, so they make every decision independently of the previous one. 

Although reactive machine AI is the standard for straightforward, repetitive tasks, it’s capable of more complex, predictive tasks too. IBM’s Deep Blue, a chess-playing supercomputer that beat international grandmaster Garry Kasparov in 1997, is a go-to example. Although it was unable to remember past games or learn from new ones, Deep Blue could evaluate 200 million chess positions per second and make predictions about its opponent's possible moves. 

2. Limited memory AI 

These AI systems can analyze data from past experiences to improve their performance and outputs — think self-driving cars that track the speed and direction of other vehicles to avoid collisions. Limited memory AI temporarily stores and learns from previous interactions, meaning the more interactions, the better its ability to solve problems. But this memory isn’t permanent. Once data is no longer relevant, it’s discarded. 

Take Otter.ai’s natural language processing (NLP) capabilities. Otter uses AI to transcribe and summarize meetings, remembering details like recurring keywords and phrases to refine its accuracy. Once the meeting concludes, it discards the temporary data, making sure it doesn’t waste energy or space storing unnecessary information.

3. Theory of mind AI

If AI-powered machines begin to walk amongst us, they need to understand how the human mind works. Theory of mind AI refers to machines that comprehend and simulate human emotions, thoughts, and intentions. Understanding that all humans respond as independent beings would allow AI machines to interact on a deeper, more natural level. 

No real-life examples of theory of mind AI currently exist, although it’s often portrayed in pop culture. The film Her, where AI assistant Samantha can understand and engage with the emotional needs of users, is a good example. 

4. Self-aware AI

Self-aware AI is a theoretical technology that can understand its environment and have a sense of self. This type of AI would be able to recognize its existence, emotions, and potential impacts of its decisions. 

Unlike other types of AI systems, self-aware AI uses human reasoning informed by motivations similar to those in the human brain, such as self-preservation or the pursuit of specific goals. Although entirely hypothetical at this stage, self-aware AI is also a recurring theme in science fiction. 

3 types of AI capabilities

Some AI categories are purely speculative, but others are evolving rapidly. Here’s a breakdown of some existing AI capabilities — plus technologies researchers are still working on. 

1. Artificial narrow intelligence

Artificial narrow intelligence (ANI), also known as weak AI, is designed to perform specific tasks within a limited scope. That means narrow AI excels in the job it’s trained to do, but can’t adapt to tasks outside its programming. The types of AI systems we interact with today are mostly classified under this category.

For example, Google Maps uses ANI to provide directions, but it can't call one of your contacts to let them know you’re on the way. A virtual assistant like Siri uses ANI to listen to your prompts and act accordingly.

2. Artificial general intelligence 

Artificial general intelligence (AGI), also known as strong AI, is still a theoretical concept. It’s a type of AI that could intellectualize like humans, using reasoning to understand, learn, and apply knowledge to different tasks. 

Since AGI is still a concept, there aren’t any real-life examples. But researchers envision general AI's ability to learn from experience and adapt its knowledge base to perform tasks or solve problems it’s not specifically trained on. 

3. Artificial superintelligence 

Artificial superintelligence (ASI) is another hypothetical AI concept that wouldhuman intelligence and cognitive capabilities. From writing songs to solving problems, self-aware superintelligence will go beyond simple machine learning. Instead, it would have the social and emotional understanding to outperform human neural networks and evolve autonomously.

ASI is just a theory. It’s often portrayed in science fiction, but with dubious results — think fictional self-aware AI systems like 2001: A Space Odyssey’s HAL 9000 or Ava in Ex Machina.

Exploring additional AI applications

Virtual assistants, ChatGPT, and recommendation algorithms are the most well-known AI agents. And AI already powers advanced technologies that transform the way we work. 

Here are more examples of AI to familiarize yourself with:

Robotics

AI drives modern robotics, enabling robots to perform complex tasks with extreme precision. From robot-assisted surgery to autonomous drones mapping disaster zones, these AI systems use machine learning technology and sensors to navigate and adapt to different tasks. 

Computer vision

This type of AI uses machine learning to “see” and interpret the world around it. Often, computer vision uses massive datasets to train deep learning algorithms on image recognition. It’s commonly used in self-driving cars or identifying defects in manufacturing.

Machine learning

Similar to human neural networks, machine learning AI systems learn and improve through experience. Machine learning can store, access, and use information, creating improvements in its algorithm software autonomously. Chatbots like ChatGPT use machine learning models to understand and generate human-like texts by recognizing patterns and predicting desired outcomes. 

Deep learning

Deep learning is a subset of machine learning built to mimic the way the human brain processes information. It can analyze large datasets to identify complex patterns. Deep learning powers the most advanced AI systems, like self-driving cars, where the AI system continuously improves its decision-making based on vast amounts of sensory data. 

Expert systems

True to its name, these AI systems become experts in their trained field. Expert systems simulate human decision-making by using rules and knowledge bases to solve problems. AI is common in education for personalized tutoring, diagnostic support in medicine, and customer service for automated problem-solving.

Add Otter to your AI toolkit

Otter is revolutionizing AI at work as the first AI meeting assistant that auto joins, auto shares, and auto summarizes meetings. AI-powered meeting assistants are becoming standard in most enterprise settings, saving teams an average of 4 hours a week and increasing productivity by automatically generating action items, summaries, and follow-up emails. Try Otter now and see the difference smart technology can make. Schedule a demo today.