sources

This table contains all the sources for our datasets. The description column contains JSON encoded as text that contains useful additional fields like a longer descriptive text "additionalInfo".

Data license: CC-BY

1 row where datasetId = 5661 sorted by id descending

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id ▲ name description createdAt updatedAt datasetId additionalInfo link dataPublishedBy
22721 Sevilla et al. (2023) 
{
    "link": "https://docs.google.com/spreadsheets/d/1AAIebjNsnJj_uKALHbXNfn3_YsT6sHXtCU0q7OIPuc4/edit#gid=2071193799",
    "retrievedDate": "2023-06-02",
    "additionalInfo": "We update this chart with the latest available data from our source every month.\n\nThe affiliation of the research team building a particular notable AI system was classified according to the following:\n\u2014 Academia: 100% of researchers affiliated with academia\n\u2014 Collaboration, Academia-majority: 71\u201399% affiliated with academia\n\u2014 Collaboration: 30\u201370% affiliated with academia\n\u2014 Collaboration, Industry-majority: 71\u201399% affiliated with industry\n\u2014 Industry: 100% of researchers affiliated with industry\n\nThis data corresponds to the \"Organization Categorization\" column in the primary source data spreadsheet.\n\nThe authors selected the AI systems for inclusion based on the following necessary criteria:\n\u2014 Have an explicit learning component\n\u2014 Showcase experimental results\n\u2014 Advance the state of the art\n\nIn addition, the systems had to meet at least one of the following notability criteria:\n\u2014 Paper has more than 1000 citations\n\u2014 Historical importance\n\u2014 Important state-of-the-art advance\n\u2014 Deployed in a notable context\n\nThe authors note that: \"For new models (from 2020 onward) it is harder to assess these criteria, so we fall back to a subjective selection. We refer to models meeting our selection criteria as 'milestone models.'\"\n\nThe authors have published the following articles based on the data:\n\u2013 Sevilla, Heim, Ho, Besiroglu, Hobbhahn, & Villalobos (2022). <a href=\"https://arxiv.org/abs/2202.05924\" target=\u201d_blank\u201d>Compute Trends Across Three Eras of Machine Learning.</a> arXiv.\n\u2013 Villalobos, Sevilla, Besiroglu, Heim, Ho, & Hobbhahn (2022). <a href=\"https://arxiv.org/abs/2207.02852\" target=\u201d_blank\u201d>Machine Learning Model Sizes and the Parameter Gap.</a> arXiv.\n\u2013 Villalobos & Ho (2022). <a href=\"https://epochai.org/blog/trends-in-training-dataset-sizes\" target=\u201d_blank\u201d>Trends in Training Dataset Sizes</a>. Published online at epochai.org. [online resource]",
    "dataPublishedBy": "Sevilla, Villalobos, Cer\u00f3n, Burtell, Heim, Nanjajjar, Ho, Besiroglu, Hobbhahn, Denain, and Dudney (2023). Parameter, Compute, and Data Trends in Machine Learning.",
    "dataPublisherSource": "Published results in artificial intelligence and machine learning"
}
 2022-07-20 13:10:17 2023-06-02 21:17:43 Researcher Affiliation Notable AI Systems Data – Sevilla et al. (2023) 5661 We update this chart with the latest available data from our source every month. The affiliation of the research team building a particular notable AI system was classified according to the following: — Academia: 100% of researchers affiliated with academia — Collaboration, Academia-majority: 71–99% affiliated with academia — Collaboration: 30–70% affiliated with academia — Collaboration, Industry-majority: 71–99% affiliated with industry — Industry: 100% of researchers affiliated with industry This data corresponds to the "Organization Categorization" column in the primary source data spreadsheet. The authors selected the AI systems for inclusion based on the following necessary criteria: — Have an explicit learning component — Showcase experimental results — Advance the state of the art In addition, the systems had to meet at least one of the following notability criteria: — Paper has more than 1000 citations — Historical importance — Important state-of-the-art advance — Deployed in a notable context The authors note that: "For new models (from 2020 onward) it is harder to assess these criteria, so we fall back to a subjective selection. We refer to models meeting our selection criteria as 'milestone models.'" The authors have published the following articles based on the data: – Sevilla, Heim, Ho, Besiroglu, Hobbhahn, & Villalobos (2022). <a href="https://arxiv.org/abs/2202.05924" target=”_blank”>Compute Trends Across Three Eras of Machine Learning.</a> arXiv. – Villalobos, Sevilla, Besiroglu, Heim, Ho, & Hobbhahn (2022). <a href="https://arxiv.org/abs/2207.02852" target=”_blank”>Machine Learning Model Sizes and the Parameter Gap.</a> arXiv. – Villalobos & Ho (2022). <a href="https://epochai.org/blog/trends-in-training-dataset-sizes" target=”_blank”>Trends in Training Dataset Sizes</a>. Published online at epochai.org. [online resource] https://docs.google.com/spreadsheets/d/1AAIebjNsnJj_uKALHbXNfn3_YsT6sHXtCU0q7OIPuc4/edit#gid=2071193799 Sevilla, Villalobos, Cerón, Burtell, Heim, Nanjajjar, Ho, Besiroglu, Hobbhahn, Denain, and Dudney (2023). Parameter, Compute, and Data Trends in Machine Learning.

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CREATE TABLE "sources" (
	"id" INTEGER PRIMARY KEY AUTOINCREMENT,
	"name" VARCHAR(512) NULL  ,
	"description" TEXT NOT NULL  ,
	"createdAt" DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP ,
	"updatedAt" DATETIME NULL  ,
	"datasetId" INTEGER NULL, additionalInfo TEXT GENERATED ALWAYS as (JSON_EXTRACT(description, '$.additionalInfo')) VIRTUAL, link TEXT GENERATED ALWAYS as (JSON_EXTRACT(description, '$.link')) VIRTUAL, dataPublishedBy TEXT GENERATED ALWAYS as (JSON_EXTRACT(description, '$.dataPublishedBy')) VIRTUAL,
	FOREIGN KEY("datasetId") REFERENCES "datasets" ("id") ON UPDATE RESTRICT ON DELETE RESTRICT
);
CREATE INDEX "sources_datasetId" ON "sources" ("datasetId");