4 Types of AI: Getting to Know Artificial Intelligence

Artificial intelligence (AI) technology has created opportunities to address real-world problems concerning health, education, and the environment, as it can sometimes do things more efficiently or methodically than humans can.

For example, "smart" buildings, vehicles, and other AI technologies can decrease carbon emissions and support people with disabilities [1]. Additionally, machine learning, a subset of AI, has enabled engineers to build robots and self-driving cars, recognise speech and images, and forecast market trends [2]. 

Read on to learn more about the four main types of AI and their functions in everyday life.

4 main types of artificial intelligence

Artificial intelligence has the capabilities to evolve, perform narrowly defined sets of tasks, simulate the thought processes of the human mind, and perform beyond human capability. Arend Hintze, researcher and professor of integrative biology at Michigan State University, defined four main types of AI as the following [3]: 

1. Reactive machines

Reactive machines are AI systems with no memory and are task-specific, meaning that an input always delivers the same output. Machine learning models often function as reactive machines because they take customer data, such as purchase or search history, and use it to deliver recommendations to the same customers.

This type of AI is reactive. It performs "super" AI because the average human would not be able to process vast amounts of data, such as a customer’s entire Netflix history, and then make customised recommendations based on that data. Reactive AI, for the most part, is reliable and works well in inventions like these. However, it can’t predict future outcomes unless you feed it the appropriate information.

Compare this to our human lives, where most of our actions are not reactive because we don’t have all the information we need in order to react, but we have the capability to remember and learn. Based on those successes or failures, we may act differently in the future if faced with a similar situation.

Examples of reactive machines

Deep Blue: One of the best examples of reactive AI is when Deep Blue, IBM’s chess-playing AI system, beat Garry Kasparov, a world chess champion, in the late 1990s. Deep Blue could identify its pieces and its opponent’s pieces on the chessboard to make predictions, but it did not have the memory capacity to use past mistakes to inform future decisions. It only makes predictions based on what moves could be next for both players and selects the best move [4].

Netflix recommendations: Machine learning models power Netflix’s recommendation engine to process the data collected from a customer’s viewing history to determine specific movies and TV shows that they will enjoy. Humans are creatures of habit. So, if someone tends to watch a lot of Korean dramas, for example, Netflix will show a preview of new releases on the home page.

2. Limited memory

The next type of AI in its evolution is limited memory. This algorithm imitates how our brains’ neurons work together, meaning it gets smarter as it receives more data to train on. Deep learning algorithms improve natural language processing (NLP), image recognition, and other types of reinforcement learning.

Unlike reactive machines, limited memory AI can look into the past and monitor specific objects or situations over time. Then, these observations are programmed into the AI to act based on past and present moment data. However, in limited memory, this data isn’t saved into the AI’s memory as experience to learn from, the way humans might derive meaning from their successes and failures. The AI improves over time as it’s trained on more data.

Example of limited memory artificial intelligence

Self-driving cars offer an excellent example of limited memory AI. These vehicles observe other cars on the road for speed, direction, and proximity. This information forms the basis of the car’s representation of the world, such as knowing traffic lights, signs, curves, and bumps in the road. The data helps the car decide when to change lanes so that it does not get hit or cut off by another driver. 

3. Theory of mind

The first two types of AI, reactive machines and limited memory, are types that currently exist. Theory of mind and self-aware AI are theoretical types that engineers could build in the future. As such, this type of AI doesn’t have any real-world examples yet.

Theory of mind AI could potentially understand the world and how other entities have thoughts and emotions. In turn, this affects how they behave in relation to those around them.

Human cognitive abilities are capable of processing how our own thoughts and emotions affect others, and how others affect us; this is the basis of our society’s human relationships. In the future, theory of mind AI machines could be able to understand intentions and predict behaviour, as if to simulate human relationships.

4. Self-awareness

The grand finale for the evolution of AI would be to design systems that have a sense of self and a conscious understanding of their existence. This type of AI does not exist yet.

This goes a step beyond theory of mind AI. It involves AI gaining the ability to understand emotions and predict others’ feelings. For example, thoughts such as "I know I am hungry" or "I want to eat lasagna because it’s my favourite food."

Artificial intelligence and machine learning algorithms are a long way from self-awareness. This is because researchers still have so much to uncover about the human brain’s intelligence and how memory, learning, and decision-making work in order to replicate these processes.

Keep learning about artificial intelligence with AI expert Andrew Ng.

Artificial Intelligence (AI) continues ushering in new opportunities to address real-world problems. Learning about AI can be fun and fascinating, even if you don’t want to become an AI engineer. The AI for Everyone course offered by DeepLearning.AI is especially designed for non-technical people to understand what AI is, including common terminology like neural networks, machine learning, deep learning, and data science. You’ll learn how to work with an AI team, build an AI strategy in your company, and much more.