charts: 7861

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
7861 hardware-and-energy-cost-to-train-notable-ai-systems ScatterPlot 
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    "note": "This data is expressed in constant 2023 US$. The hardware costs are amortized and calculated by multiplying the training chip-hours by the reduced hardware cost, with an additional 23% for networking expenses. The costs of some models vary in certainty, as some use actual price data rather than estimates.",
    "slug": "hardware-and-energy-cost-to-train-notable-ai-systems",
    "type": "ScatterPlot",
    "title": "Hardware and energy cost to train notable AI systems",
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    "version": 7,
    "subtitle": "This data is expressed in US dollars, adjusted for inflation.",
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    "isPublished": true,
    "hideAnnotationFieldsInTitle": {
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    }
}
 2024-06-10 11:17:20 2024-06-20 15:01:28 2024-06-20 15:01:28 2024-06-20 15:01:28 72 72 1 Hardware and energy cost to train notable AI systems This data is expressed in US dollars, adjusted for inflation. This data is expressed in constant 2023 US$. The hardware costs are amortized and calculated by multiplying the training chip-hours by the reduced hardware cost, with an additional 23% for networking expenses. The costs of some models vary in certainty, as some use actual price data rather than estimates. Hardware and energy cost to train notable AI systems () 
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    "hasChartTab": true,
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    "entityType": "country or region",
    "facettingLabelByYVariables": "metric",
    "addCountryMode": "add-country",
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    "type": "ScatterPlot",
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    "stackMode": "absolute",
    "minTime": "earliest",
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    "id": 7861,
    "note": "This data is expressed in constant 2023 US$. The hardware costs are amortized and calculated by multiplying the training chip-hours by the reduced hardware cost, with an additional 23% for networking expenses. The costs of some models vary in certainty, as some use actual price data rather than estimates.",
    "slug": "hardware-and-energy-cost-to-train-notable-ai-systems",
    "title": "Hardware and energy cost to train notable AI systems",
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}

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