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 450415,Compute_sponsor_large,,,2022-07-20 14:19:28,2023-06-15 05:05:42,,,,5515,21327,,{},0,,,,,,1,,,,,,,,,,,,,,,,, 450123,Equivalent_training_time_hours,,,2022-07-09 10:01:46,2023-06-15 05:05:42,,,,5515,21327,,{},0,,,,,,1,,,,,,,,,,,,,,,,, 450122,Compute_sponsor_all,,,2022-07-09 09:48:24,2023-06-15 05:05:42,,,,5515,21327,,{},0,,,,,,1,,,,,,,,,,,,,,,,, 450118,Cost_training_computation,,"Cost of training computation was calculated as the system's training computation divided by a floating-point operation (FLOP)/$ value. The FLOP/$ value was calculated using one of two methods: 1. The value of FLOP/s per $ at the time of the system's publication (according to the ""Our data"" trend line in Figure 1 here). 2. Dividing the theoretical peak throughput (including ""Tensor Core"" performance) by the reported unit price of the hardware that was actually used for training. The authors expect that Method 2 is more accurate on average. If an estimate via Method 2 is available, they report that estimate; otherwise, they use Method 1. Additionally, the authors made the following assumptions for all systems, in order to convert theoretical peak FLOP/s per $ into realized FLOP/$: 1. Hardware utilization is 35% 2. The amount of GPU time the given hardware is used for in its lifetime is 2 years.",2022-07-09 09:28:43,2023-06-15 05:05:42,,,,5515,21327,,"{""unit"": ""real 2020 US$"", ""zeroDay"": ""2020-01-01"", ""shortUnit"": ""$"", ""yearIsDay"": true, ""includeInTable"": true, ""numDecimalPlaces"": 0}",0,,,,,,1,,,,,,,,,,,,,,,,, 450117,Training_data_gb,,,2022-07-09 09:28:43,2023-06-15 05:05:42,,,,5515,21327,,"{""includeInTable"": true}",0,,,,,,1,,,,,,,,,,,,,,,,, 450116,Inference_time_ms,,,2022-07-09 09:28:43,2023-06-15 05:05:42,,,,5515,21327,,{},0,,,,,,1,,,,,,,,,,,,,,,,, 450114,Inference_computation_flop,,,2022-07-09 09:28:43,2023-06-15 05:05:42,,,,5515,21327,,"{""includeInTable"": true}",0,,,,,,1,,,,,,,,,,,,,,,,, 450113,Training_datapoints,,,2022-07-09 09:28:43,2023-06-15 05:05:42,,,,5515,21327,,"{""unit"": ""data points"", ""zeroDay"": ""2020-01-01"", ""yearIsDay"": true, ""includeInTable"": true}",0,,,,,,1,,,,,,,,,,,,,,,,, 450112,Parameters,,,2022-07-09 09:28:43,2023-06-15 05:05:42,,,,5515,21327,,"{""name"": ""Parameters"", ""unit"": ""parameters"", ""zeroDay"": ""2020-01-01"", ""shortUnit"": """", ""yearIsDay"": true, ""includeInTable"": true}",0,,,,,,1,,,,,,,,,,,,,,,,, 412581,Researcher_affiliation,,"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.",2022-03-20 21:59:37,2023-06-15 05:05:42,,,,5515,21327,,"{""includeInTable"": true}",0,,,,,,1,,,,,,,,,,,,,,,,, 312259,Training_computation_petaflop,,A petaFLOP is 10¹⁵ floating-point operations.,2022-02-15 16:45:06,2023-06-15 05:05:42,,,,5515,21327,,"{""name"": ""petaFLOP"", ""unit"": ""petaFLOP"", ""zeroDay"": ""2020-01-01"", ""shortUnit"": """", ""yearIsDay"": true, ""includeInTable"": true}",0,,,,,,1,,,,,,,,,,,,,,,,, 312258,Training_computation_petaflop_sec_day,,"A petaflop/s-day (pfs-d) consists of performing 10¹⁵ neural network operations per second for one day, for a total of about 10²⁰ operations.",2022-02-15 16:25:39,2023-06-15 05:05:42,,,,5515,21327,,"{""unit"": ""petaflop/s-days"", ""zeroDay"": ""2020-01-01"", ""shortUnit"": ""pfs-d"", ""yearIsDay"": true, ""includeInTable"": true}",0,,,,,,1,,,,,,,,,,,,,,,,, 312257,Training_computation_flop,,,2022-02-15 13:49:44,2023-06-15 05:05:42,,,,5515,21327,,"{""unit"": ""Floating-point operations"", ""zeroDay"": ""2020-01-01"", ""shortUnit"": ""FLOP"", ""yearIsDay"": true, ""includeInTable"": true}",0,,,,,,1,,,,,,,,,,,,,,,,, 312256,Domain,,,2022-02-15 13:49:44,2023-06-15 05:05:42,,,,5515,21327,,"{""includeInTable"": true}",0,,,,,,1,,,,,,,,,,,,,,,,,