id,name,description,createdAt,updatedAt,datasetId,additionalInfo,link,dataPublishedBy
21327,Sevilla et al. (2023),"{""link"": ""https://docs.google.com/spreadsheets/d/1AAIebjNsnJj_uKALHbXNfn3_YsT6sHXtCU0q7OIPuc4/edit#gid=0"", ""retrievedDate"": ""2023-06-02"", ""additionalInfo"": ""We update this chart with the latest available data from our source every month.\n\nThe authors selected the AI systems for inclusion based on the following necessary criteria:\n— Have an explicit learning component\n— Showcase experimental results\n— Advance the state of the art\n\nIn addition, the systems had to meet at least one of the following notability criteria:\n— Paper has more than 1000 citations\n— Historical importance\n— Important state-of-the-art advance\n— 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– 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.\n– 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.\n– 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]"", ""dataPublishedBy"": ""Sevilla, Villalobos, Cerón, 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""}",2022-02-15 13:49:44,2023-06-02 21:05:39,5515,"We update this chart with the latest available data from our source every month.

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=0,"Sevilla, Villalobos, Cerón, Burtell, Heim, Nanjajjar, Ho, Besiroglu, Hobbhahn, Denain, and Dudney (2023) Parameter, Compute, and Data Trends in Machine Learning."