The Different Types of Artificial Intelligence

An artist’s illustration of artificial intelligence (AI). This image explores generative AI and how it can empower humans with creativity. It was created by Winston Duke as part of the Visualising AI project launched by Google DeepMind.

We know that Artificial Intelligence can seem complex, so we're here to break it down.

To help you better understand AI, we've provided clear definitions for some common terms and an overview of the different types of AI.       

What is AI?

Artificial intelligence (AI) is not a new concept. It dates back to the early days of modern computing in the 1950s, with even older mathematical and theoretical foundations dating back further.

While there are many definitions of AI, we have adopted the OECD’s updated definition:

“An AI system is a machine-based system that, for a given set of explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment”.

To simplify things, we’ll talk you through some common terms used when talking about AI.

Categories of AI

Types of AI Based on Capabilities

• Artificial General Intelligence
  A type of AI that could learn to accomplish any intellectual task that humans can perform, or surpass human capabilities in many economically valuable tasks. While AGI is often mentioned in the news and on social media, there is currently no research that proves how AGI could be developed or achieved.

• Artificial Narrow Intelligence
  Artificial Narrow Intelligence (ANI), also known as weak AI or narrow AI, is designed to perform a specific set of tasks. All AI in existence today is narrow AI, usually using machine learning or deep learning techniques. Examples of narrow AI include internet search engines, recommendation systems and facial recognition. Such AI tools are designed to perform tasks within a single, defined set of problems.

• Artificial Superintelligence
  Artificial Superintelligence (ASI) is a speculative concept of AI that would far surpass human intelligence, exceeding in memory, data-processing and decision-making abilities.

Types of AI Based on Functionalities

• Limited Memory AI
  Limited Memory AI uses past data to improve decision-making in future tasks, learning from previous interactions to refine their outputs. A common example of an AI system using Limited Memory is ChatGPT.

• Reactive Machine AI
  Reactive Machines are the simplest type of AI capable of responding only to current data and not able to learn from past experiences. Famous examples of Reactive Machines include Netflix’s recommendation engine and IBM’s Deep Blue, the chess-playing computer.

• Self-Awareness AI
  Self-Aware AI is a theoretical form in AI that would have its own consciousness, and understand human emotions and thoughts.  

• Theory of Mind AI
  Theory of Mind AI is a theoretical form of Artificial General Intelligence (AGI) that would be capable of understanding human thoughts and emotions. This would allow for deeper human and AI interactions.

Different Branches of AI

• Computer Vision
  A form of AI used to understand and recognise images and videos and to analyse the elements of the content within them. For example, Google Photos uses computer vision to categorise photo files by their subject matter, grouping pictures of pets, people, landscapes or food together. Facebook also uses a form of computer vision to recognise faces in photographs and prompt you to tag someone. Computer vision can also be used for more complex analysis of images, such as using satellite imagery to map biodiversity by recognising characteristics of the landscape. Space Intelligence and Scottish Wildlife Trust are using computer vision to interpret large volumes of satellite data and map wildlife habitats to help restore, connect and protect Scotland’s natural environment.

• Deep Learning
  A more recent variation of neural networks, using many layers of artificial neurons (forming a ‘deep neural network’) to solve more difficult problems. Its popularity as a technique increased significantly from the mid-2000s onwards, and it is behind much of the wider interest in AI today. Deep learning is often used to classify information from images, text or sound.

• Expert Systems
  Expert Systems are a type of Artificial Narrow Intelligence (ANI) application designed to replicate human decision-making processes. These systems rely on large databases and predefined rules to make informed decisions. Due to their narrow scope, they cannot adapt beyond the specific tasks they are programmed to handle.

• Machine Learning
  A type of AI that has the capacity to learn without following explicit instructions. Machine learning systems use algorithms and statistical models to identify patterns in data and make predictions based on those patterns. There are several methods for training machine learning models, such as supervised learning, unsupervised learning and reinforcement learning.

• Natural Language Processing
  NLP involves using AI to enable computers to read, understand and translate human speech or text. NLP systems can identify words and interpret meaning from sentences, phrases, tone and context. Examples include online customer service chatbots that analyse typed questions and select the best prewritten response or generate specific, human-sounding answers. Google Translate also uses NLP to study large quantities of text and translations, along with corrections from users, to continually improve its automatic translations.

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