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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>X-Risk Analysis for AI Research · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2206.05862/</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/2206.05862/feed.xml" rel="self" type="application/rss+xml" /><item><title>X-Risk Analysis for AI Research — mentioned by noname120</title><link>https://news.ycombinator.com/item?id=35898652</link><guid isPermaLink="false">urn:hackerpapers:mention:2206.05862:35898652</guid><pubDate>Thu, 11 May 2023 06:52:50 GMT</pubDate><description>&lt;p&gt;Generalized artificial intelligence (AGI) comes with very real x-risks[1] (existential risks) and s-risks[2] (suffering risks). An expert survey of 738 researchers who published in NeurIPS and ICML was done last year[3]. Their median estimation that AI will have an “extremely bad” long term outcome is 5%, and 48% of the researchers estimate the probability to be at least 10%. This is worryingly high considering the absolutely catastrophic consequences of the scenarios. A minority of very vocal AI researchers (e.g. Yann LeCun) dismiss these risks entirely and claim that people read too much…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2206.05862/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CY</category></item></channel></rss>
