<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Learning Scaling is Predictable, Empirically · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1712.00409/</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/1712.00409/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Learning Scaling is Predictable, Empirically — mentioned by gdiamos</title><link>https://news.ycombinator.com/item?id=43216281</link><guid isPermaLink="false">urn:hackerpapers:mention:1712.00409:43216281</guid><pubDate>Sat, 01 Mar 2025 05:27:47 GMT</pubDate><description>&lt;p&gt;Scaling laws just say that accuracy improves predictably - https://arxiv.org/pdf/1712.00409 That’s what we put in our 2017 paper. It does mean that there is a simple way to keep building smarter LLMs. I have never seen a clear definition of what AGI is and what it means to achieve it&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1712.00409/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Deep Learning Scaling is Predictable, Empirically — HN submission</title><link>https://news.ycombinator.com/item?id=42260442</link><guid isPermaLink="false">urn:hackerpapers:submission:1712.00409:42260442</guid><pubDate>Wed, 27 Nov 2024 22:28:18 GMT</pubDate><description>&lt;p&gt;Deep Learning Scaling Is Predictable, Empirically&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1712.00409/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Deep Learning Scaling is Predictable, Empirically — mentioned by dpflan</title><link>https://news.ycombinator.com/item?id=40080687</link><guid isPermaLink="false">urn:hackerpapers:mention:1712.00409:40080687</guid><pubDate>Thu, 18 Apr 2024 21:03:31 GMT</pubDate><description>&lt;p&gt;The comments here are trending towards &amp;quot;stop cramming &amp;#x27;AI&amp;#x27; into everything&amp;quot;. I am curious how the end-user consumer (versus &amp;#x27;AI&amp;#x27; for enterprise/business) differ in experience and use. We are in the beginning of this AI-fication, and it seems deep learning models are doing really well and that DL can predictably scale [1.]; therefore, do we have to wait a bit for really life-changing AI for the end-user consumer? I can see AI in enterprise/business being extremely useful in different industries, but at the same time, is the current &amp;#x27;AI&amp;#x27; actually good/useful for the end-user consumer? [1.]…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1712.00409/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Deep Learning Scaling is Predictable, Empirically — mentioned by gdiamos</title><link>https://news.ycombinator.com/item?id=38046076</link><guid isPermaLink="false">urn:hackerpapers:mention:1712.00409:38046076</guid><pubDate>Sat, 28 Oct 2023 01:17:44 GMT</pubDate><description>&lt;p&gt;We showed this in our original scaling law paper: https://arxiv.org/pdf/1712.00409.pdf The slope of the power law is determined by the problem and dataset. Compute, parameter count, and data move you along the curve. Change in architecture/bias is a constant offset. So architecture can give an advantage, but that advantage can be overcome by scale.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1712.00409/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Deep Learning Scaling is Predictable, Empirically — HN submission</title><link>https://news.ycombinator.com/item?id=16240860</link><guid isPermaLink="false">urn:hackerpapers:submission:1712.00409:16240860</guid><pubDate>Fri, 26 Jan 2018 17:12:39 GMT</pubDate><description>&lt;p&gt;Deep Learning Scaling Is Predictable, Empirically&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;3 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1712.00409/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
