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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>25 years of CNNs: Can we compare to human abstraction capabilities? · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1607.08366/</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/1607.08366/feed.xml" rel="self" type="application/rss+xml" /><item><title>25 years of CNNs: Can we compare to human abstraction capabilities? — mentioned by YeGoblynQueenne</title><link>https://news.ycombinator.com/item?id=41538930</link><guid isPermaLink="false">urn:hackerpapers:mention:1607.08366:41538930</guid><pubDate>Sat, 14 Sep 2024 10:59:15 GMT</pubDate><description>&lt;p&gt;&amp;quot;Designed&amp;quot; is not right. What gives &amp;quot;AI models&amp;quot; (i.e. deep neural nets) a hard time is that there are very few examples in the public training and evaluation set: each task has three examples. So basically it&amp;#x27;s not a test of intelligence but a test of sample efficiency. Besides which, it is unfair because it excludes an entire category of systems, not to mention a dominant one. If F. Chollet really believes ARC is a test of intelligence, then why not provide enough examples for deep nets or some other big data approach to be trained effectively? The answer is: because a big data approach…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1607.08366/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
