charts: 6945

This table contains the information used by our Grapher datavisualisation tool to render charts.

The most relevant field is the config field that contains json stored as text. A JSON Schema is available that describes the content of this json data. The SQLite JSON_EXTRACT function can be used in SQL queries to extract the content of this JSON data into columns in a result table.

Data license: CC-BY

This data as json

id slug type config createdAt updatedAt lastEditedAt publishedAt lastEditedByUserId publishedByUserId isIndexable title subtitle note title_plus_variant configWithDefaults
6945 ai-training-computation-vs-parameters-by-domain ScatterPlot 
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    "note": "Parameters are estimated based on published results in the AI literature and come with some uncertainty. The authors expect the estimates to be correct within a factor of 10.",
    "slug": "ai-training-computation-vs-parameters-by-domain",
    "type": "ScatterPlot",
    "title": "Training computation vs. parameters in notable AI systems, by domain",
    "xAxis": {
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    "version": 19,
    "subtitle": "Computation is measured in total petaFLOP, which is 10\u00b9\u2075 [floating-point operations](#dod:flop) estimated from AI literature, albeit with some uncertainty. Parameters are variables in an AI system whose values are adjusted during training to establish how input data gets transformed into the desired output.",
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 2023-07-08 12:24:07 2024-07-17 16:46:48 2024-07-17 16:46:48 2023-09-01 13:35:07 74 46 1 Training computation vs. parameters in notable AI systems, by domain Computation is measured in total petaFLOP, which is 10¹⁵ [floating-point operations](#dod:flop) estimated from AI literature, albeit with some uncertainty. Parameters are variables in an AI system whose values are adjusted during training to establish how input data gets transformed into the desired output. Parameters are estimated based on published results in the AI literature and come with some uncertainty. The authors expect the estimates to be correct within a factor of 10. Training computation vs. parameters in notable AI systems, by domain () 
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    "version": 19,
    "logo": "owid",
    "entityType": "system",
    "facettingLabelByYVariables": "metric",
    "addCountryMode": "add-country",
    "compareEndPointsOnly": false,
    "type": "ScatterPlot",
    "hasMapTab": false,
    "stackMode": "absolute",
    "minTime": "earliest",
    "hideAnnotationFieldsInTitle": {
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    "id": 6945,
    "note": "Parameters are estimated based on published results in the AI literature and come with some uncertainty. The authors expect the estimates to be correct within a factor of 10.",
    "slug": "ai-training-computation-vs-parameters-by-domain",
    "title": "Training computation vs. parameters in notable AI systems, by domain",
    "subtitle": "Computation is measured in total petaFLOP, which is 10\u00b9\u2075 [floating-point operations](#dod:flop) estimated from AI literature, albeit with some uncertainty. Parameters are variables in an AI system whose values are adjusted during training to establish how input data gets transformed into the desired output.",
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}

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