variables: 815792
Data license: CC-BY
This data as json
id | name | unit | description | createdAt | updatedAt | code | coverage | timespan | datasetId | sourceId | shortUnit | display | columnOrder | originalMetadata | grapherConfigAdmin | shortName | catalogPath | dimensions | schemaVersion | processingLevel | processingLog | titlePublic | titleVariant | attributionShort | attribution | descriptionShort | descriptionFromProducer | descriptionKey | descriptionProcessing | licenses | license | grapherConfigETL | type | sort | dataChecksum | metadataChecksum |
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815792 | Annual number of AI systems by researcher affiliation | AI systems | 2023-11-01 14:47:44 | 2024-05-05 19:08:35 | 1950-2024 | 6286 | { "unit": "AI systems", "numDecimalPlaces": 0 } |
0 | yearly_count | grapher/artificial_intelligence/latest/epoch_aggregates_affiliation/epoch#yearly_count | 2 | minor | Describes the sector (Industry, Academia, or Collaboration) where the authors of an AI system have their primary affiliations. | [ "The authors of the Epoch dataset have established a set of criteria to identify key AI systems, which they refer to as \u201cnotable\u201d. To be considered notable, these systems must demonstrate the ability to learn, show tangible experimental results, and contribute advancements that push the boundaries of existing AI technology. The AI system must also have received 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. The authors recognize the difficulty in evaluating the impact of newer AI systems since 2020 due to less data being available; because of this, they also employ subjective judgement 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." ] |
Processing involved calculating total number of AI systems developed within each category of reseacher affiliation for each year. 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. | int | [] |