4 Types of AI: Getting to Know Artificial Intelligence

Artificial intelligence (AI) technology creates opportunities to progress on real-world problems concerning health, education, and the environment. “Smart” buildings, vehicles, and other technologies can decrease carbon emissions and support people with disabilities. Machine learning, a subset of AI, has enabled engineers to build robots and self-driving cars, recognise speech and images, and forecast market trends. 

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

4 main types of artificial intelligence

Learning in AI can fall under the types: “narrow intelligence,” “artificial general intelligence,” and “superintelligence.” These categories demonstrate AI’s capabilities as it evolves—performing narrowly defined sets of tasks, simulating thought processes in the human mind, and performing beyond human capability. Then, Arend Hintze, researcher and professor of integrative biology at Michigan State University, defines four main types of AI [1]. They are as follows:

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 tend to be reactive because they use customer data, such as purchase or search history, 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 huge amounts of data, such as a customer’s entire Netflix history and feedback customised recommendations. Reactive AI, for the most part, is reliable and works well in inventions like self-driving cars. It doesn’t have the ability to predict future outcomes unless it has been fed 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 to react upon, 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

Beat at chess by IBM’s supercomputer: One of the best examples of reactive AI is when Deep Blue, IBM’s chess-playing AI system, beat Garry Kasparov in the late 1990s. Deep Blue could identify its own 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 made predictions based on the next moves for both players and selected the best move. 

Netflix recommendations: Netflix’s recommendation engine is powered by machine learning models that process the data collected from a customer’s viewing history to determine specific movies and TV shows they're likely to enjoy. Humans are creatures of habit—if someone tends to watch a lot of Korean dramas, Netflix will show a preview of new releases in that genre on the home page.

2. Limited memory machines

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 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 so its actions can be performed based on both past and present moment data. But 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 machines

Self-driving cars: A good example of limited memory AI is how self-driving cars observe other cars on the road for their speed, direction, and proximity. This information is programmed as 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 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 could be built in the future. As such, real-world examples do not exist yet.

If it is developed, the theory of mind AI could potentially understand the world and how other entities have thoughts and emotions. In turn, this could affect how they behave in relation to those around them.

Human cognitive abilities can process how our 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 would theoretically 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, a conscious understanding of their existence. This type of AI does not exist yet.

This goes a step beyond the theory of mind AI and understanding emotions. People can become aware of themselves and their state of being and sense or predict others’ feelings. For example, “I’m hungry” becomes “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. So much remains to be uncovered about the human brain’s intelligence and how memory, learning, and decision-making work.

Keep learning about artificial intelligence with AI expert Andrew Ng.

The four main types of AI are reactive machines performing specific tasks, limited memory machines learning from data, theoretical “theory of mind” AI understanding emotions, and the future possibility of self-aware AI.  While the first two types exist today, the latter two are conceptual.

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