<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>echen</author><text>Interestingly, we&amp;#x27;ve actually helped other AI companies build robust violence detectors precisely to prevent these kinds of violent, gore language model generations. You can read about it here &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.surgehq.ai&amp;#x2F;blog&amp;#x2F;ai-red-teams-and-adversarial-data-labeling-with-redwood-research&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.surgehq.ai&amp;#x2F;blog&amp;#x2F;ai-red-teams-and-adversarial-dat...&lt;/a&gt; (we&amp;#x27;re writing a more technical blog post soon, too; this one is fairly high-level) or the detailed description by Redwood Research at &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.alignmentforum.org&amp;#x2F;posts&amp;#x2F;k7oxdbNaGATZbtEg3&amp;#x2F;redwood-research-s-current-project&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.alignmentforum.org&amp;#x2F;posts&amp;#x2F;k7oxdbNaGATZbtEg3&amp;#x2F;redwo...&lt;/a&gt;&lt;p&gt;In short, one way to prevent your language models from devolving into violence (with extremely high safety guarantees) is by building &amp;quot;AI red teams&amp;quot; of labelers who try to trick it into generating something violent. Then you train your models to detect those strategies (just like other kinds of red teams find holes in your security, which you then patch). Then your &amp;quot;red data labeling teams&amp;quot; find new strategies to trick your AI into becoming violent, you train models to counter those strategies, and so on.</text><parent_chain><item><author>andrewstuart</author><text>I tried writing stories with GPT3 and often they&amp;#x27;d veer suddenly into extreme violence... &amp;quot;The children and the gardener planted corn, beans and carrots.  A rabbit hopped along and nibbled lettuce. And then the gardener went to the shed, got the shovel and killed the entire family&amp;quot;.  It would come up with some really disturbing stuff.  You could get some great stories out of it but much of the work is culling the disturbing stories out.</text></item></parent_chain></comment><story><title>Generating Children’s Stories Using GPT-3 and DALL·E</title><url>https://www.surgehq.ai//blog/generating-childrens-stories-using-gpt-3-and-dall-e</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>ffhhj</author><text>I have a friend who created this app for dream interpretation, and almost every entry is related to sex, death and violence. We don&amp;#x27;t really understand the kind of garbage we are feeding into our AIs.</text><parent_chain><item><author>andrewstuart</author><text>I tried writing stories with GPT3 and often they&amp;#x27;d veer suddenly into extreme violence... &amp;quot;The children and the gardener planted corn, beans and carrots.  A rabbit hopped along and nibbled lettuce. And then the gardener went to the shed, got the shovel and killed the entire family&amp;quot;.  It would come up with some really disturbing stuff.  You could get some great stories out of it but much of the work is culling the disturbing stories out.</text></item></parent_chain></comment><story><title>Generating Children’s Stories Using GPT-3 and DALL·E</title><url>https://www.surgehq.ai//blog/generating-childrens-stories-using-gpt-3-and-dall-e</url></story>