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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity &amp; Creativity · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/0709.0674/</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/0709.0674/feed.xml" rel="self" type="application/rss+xml" /><item><title>Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity &amp; Creativity — mentioned by dr_dshiv</title><link>https://news.ycombinator.com/item?id=38646611</link><guid isPermaLink="false">urn:hackerpapers:mention:0709.0674:38646611</guid><pubDate>Thu, 14 Dec 2023 20:11:38 GMT</pubDate><description>&lt;p&gt;I came to Schmidhuber&amp;#x27;s work learning about his approach on creativity and curiosity and I loved it so much. It’s still so relevant. And beautiful, actually. https://arxiv.org/pdf/0709.0674.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/0709.0674/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity &amp; Creativity — mentioned by xgk</title><link>https://news.ycombinator.com/item?id=14981495</link><guid isPermaLink="false">urn:hackerpapers:mention:0709.0674:14981495</guid><pubDate>Thu, 10 Aug 2017 16:41:31 GMT</pubDate><description>&lt;p&gt;what it is they have against repetition They find it boring. Would you come to HN every day if the linked articles never changed? I&amp;#x27;m quite enamoured with Schmidthuber&amp;#x27;s theory of creativity [1, 2] Schmidhuber develops a theory of artificial curiosity and creativity for an autonomous agent. The agent is equipped with an adaptive predictor trying to predict future events from the history of previous events and actions. A reward-maximising, reinforcement learning, adaptive controller is steering the controller and gets curiosity reward for executing action sequences that improve the predictor.…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/0709.0674/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
