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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning to Understand Goal Specifications by Modelling Reward · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1806.01946/</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/1806.01946/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning to Understand Goal Specifications by Modelling Reward — mentioned by blueyes</title><link>https://news.ycombinator.com/item?id=17267141</link><guid isPermaLink="false">urn:hackerpapers:mention:1806.01946:17267141</guid><pubDate>Fri, 08 Jun 2018 16:38:18 GMT</pubDate><description>&lt;p&gt;Chatbots proposed to solve a problem that current technology is bad at: understanding natural language. Very few experts claimed that algorithms could master the combinatorics of natural language beyond a few very narrow domains. On top of the technical risk, the voice and chat UX does not give a lot of visibility to users, as this post points out. What happened was: algorithmic advances in other areas lead to analogous reasoning by non-experts whose self-promotion aligned with the media&amp;#x27;s need for something new, big and understandable. And then it failed. The irony, of course, is that…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1806.01946/"&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>
