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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Quantifying the Carbon Emissions of Machine Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1910.09700/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1910.09700/feed.xml" rel="self" type="application/rss+xml" /><item><title>Quantifying the Carbon Emissions of Machine Learning — HN submission</title><link>https://news.ycombinator.com/item?id=30587276</link><guid isPermaLink="false">urn:hackerpapers:submission:1910.09700:30587276</guid><pubDate>Mon, 07 Mar 2022 13:08:00 GMT</pubDate><description>&lt;p&gt;Ask HN: What is your take on carbon emissions of Machine Learning&lt;/p&gt;&lt;p&gt;At the latest since the widely cited article on Green AI [1], there has been increased discussion about the energy consumption of ML. Whenever I talk to colleagues about this, we always agree that there is a dangerous potential lying dormant here, but somehow it cannot be properly grasped. There is simply a lack of really good data. Calculators for emission like from Victor Schmidt and the whole Yoshua Bengio Team at Mila [2] [3] are very good tools for your own use, but can tell you little about the overall situation. So here are two questions for the Hackernews community: 1) How do you…&lt;/p&gt;&lt;p&gt;11 points; 10 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1910.09700/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CY</category></item><item><title>Quantifying the Carbon Emissions of Machine Learning — HN submission</title><link>https://news.ycombinator.com/item?id=29837623</link><guid isPermaLink="false">urn:hackerpapers:submission:1910.09700:29837623</guid><pubDate>Fri, 07 Jan 2022 10:54:06 GMT</pubDate><description>&lt;p&gt;Ask HN: What is your take on carbon emissions of Machine Learning&lt;/p&gt;&lt;p&gt;At the latest since the widely cited article on Green AI [1], there has been increased discussion about the energy consumption of ML. Whenever I talk to colleagues about this, we always agree that there is a dangerous potential lying dormant here, but somehow it cannot be properly grasped. There is simply a lack of really good data. Calculators for emission like from Victor Schmidt and the whole Yoshua Bengio Team at Mila [2] [3] are very good tools for your own use, but can tell you little about the overall situation. So here are two questions for the Hackernews community: 1) How do you…&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1910.09700/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CY</category></item></channel></rss>
