Share of notable AI systems by researcher affiliation
What you should know about this indicator
- The authors of the Epoch dataset have established a set of criteria to identify key AI systems, which they refer to as notable. These systems must demonstrate the ability to learn, show tangible experimental results, and contribute advancements that push the boundaries of existing AI technology. In terms of notability, the AI must have garnered extensive academic attention, evidenced by a high citation count, hold historical significance in the field, mark a substantial advancement in technology, or be implemented in a significant real-world context. Recognizing the difficulty in evaluating the impact of newer AI systems since 2020 due to less available data, the authors also employ subjective judgment in their selection process for recent developments.
- Systems are classified as "Industry" when their authors have ties to private sector entities, "Academia" when the authors come from universities or scholarly institutions, and "Industry - Academia Collaboration" if a minimum of 30% of the authors represent each sector.
Sources and processing
This data is based on the following sources
How we process data at Our World in Data
All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.
At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.
Notes on our processing step for this indicator
For each year starting from 1950, the total number of AI systems in each resercher affiliation category was calculated by adding that year's count to the previous years' counts. This provides a running total or cumulative count of AI systems for each year and researcher affiliation.
To streamline the categorization of researcher affiliations, the original data underwent the following transformations:
Consolidating Collaborations:
- All variations of "Industry - Academia Collaboration" entries, regardless of their capitalization or leaning (towards academia or industry), were unified into a single "Collaboration" category.
Grouping Other Affiliations:
- Affiliations explicitly labeled as "Research Collective" or "research collective", as well as those under "Government" and "Non-profit", were re-categorized under the "Other" label.
The aforementioned changes were implemented to make visualizations more coherent and concise.
Reuse this work
- All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
- All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.
Citations
How to cite this page
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: Share of notable AI systems by researcher affiliation”, part of the following publication: Charlie Giattino, Edouard Mathieu, Veronika Samborska and Max Roser (2023) - “Artificial Intelligence”. Data adapted from Epoch. Retrieved from http://staging-site-fix-deleted-assets-in-cache/grapher/cumulative-share-of-notable-artificial-intelligence-systems-by-researcher-affiliation [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 minor processing by Our World in DataFull citation
Epoch (2024) – with minor processing by Our World in Data. “Share of notable AI systems by researcher affiliation” [dataset]. Epoch, “Parameter, Compute and Data Trends in Machine Learning” [original data]. Retrieved June 14, 2024 from http://staging-site-fix-deleted-assets-in-cache/grapher/cumulative-share-of-notable-artificial-intelligence-systems-by-researcher-affiliation