Data

Cumulative number of notable AI systems by domain

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What you should know about this indicator

  • Game systems are specifically designed for games and excel in understanding and strategizing gameplay. For instance, AlphaGo, developed by DeepMind, defeated the world champion in the game of Go. Such systems use complex algorithms to compete effectively, even against skilled human players.
  • Language systems are tailored to process language, focusing on understanding, translating, and interacting with human languages. Examples include chatbots, machine translation tools like Google Translate, and sentiment analysis algorithms that can detect emotions in text.
  • Multimodal systems are artificial intelligence frameworks that integrate and interpret more than one type of data input, such as text, images, and audio. ChatGPT-4 is an example of a multimodal system, as it has the capability to process and generate responses based on both textual and visual inputs.
  • Vision systems focus on processing visual information, playing a pivotal role in image recognition and related areas. For example, Facebook's photo tagging system uses vision AI to identify faces.
  • Speech systems are dedicated to handling spoken language, serving as the backbone of voice assistants and similar applications. They recognize, interpret, and generate spoken language to interact with users.
  • Recommendation systems offer suggestions based on user preferences, prominently seen in online shopping and media streaming. For instance, Netflix's movie suggestions or Amazon's product recommendations are powered by algorithms that analyze users' preferences and past behaviors.
  • 3D Modeling systems specialize in creating and manipulating 3D representations of objects, used in fields like architecture, engineering, and entertainment.
  • Audio systems process and generate sound, with applications in music composition, signal processing, and sound recognition.
  • Biology systems analyze biological data and simulate biological processes, aiding in drug discovery and genetic research.
  • Driving systems focus on autonomous vehicle technology, enabling cars to navigate and operate without human intervention.
  • Earth science systems utilize AI to study and model natural phenomena, assisting in weather forecasting and climate change studies.
  • Image generation systems create visual content from text descriptions or other inputs, used in graphic design and content creation.
  • Materials science systems apply AI to discover and design new materials with specific properties, speeding up material discovery.
  • Mathematics systems solve complex mathematical problems and perform symbolic calculations, aiding in theorem proving and optimization.
  • Medicine systems enhance healthcare by improving diagnostics and treatment planning.
  • Robotics systems combine AI with mechanical engineering to create autonomous robots for various industries.
  • Search systems enhance search accuracy and relevance on the internet or within databases.
  • Video systems analyze and generate video content, aiding in editing, surveillance, and content creation.

A foreign key field categorizing the system’s domain of machine learning. This field links to the ML Domains table, and domains are selected from the options in that table.

Cumulative number of notable AI systems by domain
Describes the specific area, application, or field in which an AI system is designed to operate. An AI system can operate in more than one domain, thus contributing to the count for multiple domains. The 2024 data is incomplete and was last updated 13 June 2024.
Source
Epoch (2024) – with major processing by Our World in Data
Last updated
June 3, 2024
Next expected update
August 2024
Date range
1950–2024
Unit
AI systems

Sources and processing

This data is based on the following sources

Retrieved on
June 3, 2024
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Epoch AI, ‘Parameter, Compute and Data Trends in Machine Learning’. Published online at epochai.org. Retrieved from: ‘https://epochai.org/data/epochdb/visualization’ [online resource]

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Notes on our processing step for this indicator

The count of notable AI systems per domain is derived by tallying the instances of machine learning models classified under each domain category. It's important to note that a single machine learning model can fall under multiple domains. The classification into domains is determined by the specific area, application, or field that the AI system is primarily designed to operate within. System domains with less than 10 systems are grouped under "Other."

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Citations

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To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Cumulative number of notable AI systems by domain”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska and Max Roser (2023) - “Artificial Intelligence”. Data adapted from Epoch. Retrieved from https://ourworldindata.org/grapher/cumulative-number-of-notable-ai-systems-by-domain [online resource]
How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Epoch (2024) – with major processing by Our World in Data

Full citation

Epoch (2024) – with major processing by Our World in Data. “Cumulative number of notable AI systems by domain” [dataset]. Epoch, “Parameter, Compute and Data Trends in Machine Learning” [original data]. Retrieved July 14, 2024 from https://ourworldindata.org/grapher/cumulative-number-of-notable-ai-systems-by-domain