<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>krisoft</author><text>&amp;gt; In my mind, understanding a thing means you can justify an answer.&lt;p&gt;Do you understand cats? If I show you a picture of either a cat or a dog do you think you can tell which one it is? I think most people could solve that challenge, and if pressed they could vax poetically about what makes them think it is a cat. Maybe they would mention the shape of an ear, or talk about feline grace or what have you. But is that really a “justification”? Let alone one they can “defend”? How would “defending” even work in this situation?</text><parent_chain><item><author>6gvONxR4sf7o</author><text>&amp;gt; The &amp;quot;language models don&amp;#x27;t really understand anything&amp;quot; corner is getting smaller and smaller.&lt;p&gt;In my mind, understanding a thing means you can justify an answer. Like a student showing their work and being able to defend it. An answer with a proof understands the answer with respect to the proof it provides. E.g. to understand an answer with regards to first order logic, it&amp;#x27;ll have to be able to defend a logical deduction of that answer.&lt;p&gt;These models still can&amp;#x27;t justify their answers very well, so I&amp;#x27;d say they&amp;#x27;re accurate but only understand with respect to a fairly dumb proof system (e.g. they can select relevant passages or just appeal to overall accuracy statistics). They&amp;#x27;re still far from being able to justify answers in the various ways we do, which I&amp;#x27;d say means that by definition that they still don&amp;#x27;t understand with regards to the &amp;quot;proof systems&amp;quot; that we understand things with regards to.&lt;p&gt;Maybe the next step will require increasingly interesting justification systems.</text></item><item><author>maxwells-daemon</author><text>The &amp;quot;language models don&amp;#x27;t really understand anything&amp;quot; corner is getting smaller and smaller. In the last few months we&amp;#x27;ve seen pretty definitive evidence that transformers can recombine concepts ([1], [2]) and do simple logical inference using contextual information ([3], &amp;quot;make the score font color visible&amp;quot;). I see no reason that this technology couldn&amp;#x27;t smoothly scale into human-level intelligence, yet lots of people seem to think it&amp;#x27;ll require a step change or is impossible.&lt;p&gt;That being said, robust systematic generalization is still a hard problem. But &amp;quot;achieve symbol grounding through tons of multimodal data&amp;quot; is looking more and more like the answer.&lt;p&gt;[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;openai.com&amp;#x2F;blog&amp;#x2F;dall-e&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;openai.com&amp;#x2F;blog&amp;#x2F;dall-e&amp;#x2F;&lt;/a&gt;
[2] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;distill.pub&amp;#x2F;2021&amp;#x2F;multimodal-neurons&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;distill.pub&amp;#x2F;2021&amp;#x2F;multimodal-neurons&amp;#x2F;&lt;/a&gt;
[3] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;openai.com&amp;#x2F;blog&amp;#x2F;openai-codex&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;openai.com&amp;#x2F;blog&amp;#x2F;openai-codex&amp;#x2F;&lt;/a&gt;</text></item></parent_chain></comment><story><title>OpenAI Codex</title><url>https://openai.com/blog/openai-codex/</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>beering</author><text>&amp;gt; In my mind, understanding a thing means you can justify an answer.&lt;p&gt;What if the language model can generate a step-by-step explanation in the form of text? [0]&lt;p&gt;There&amp;#x27;s no guarantee that the reasoning was used to come up with the answer in the first place, and no proof that the reasoning isn&amp;#x27;t just the product of &amp;quot;a really fancy markov chain generator&amp;quot;, but would you accept it?&lt;p&gt;We&amp;#x27;re really walking into Searle&amp;#x27;s Chinese Room at this point.&lt;p&gt;[0] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;nitter.hu&amp;#x2F;kleptid&amp;#x2F;status&amp;#x2F;1284069270603866113#m&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;nitter.hu&amp;#x2F;kleptid&amp;#x2F;status&amp;#x2F;1284069270603866113#m&lt;/a&gt;</text><parent_chain><item><author>6gvONxR4sf7o</author><text>&amp;gt; The &amp;quot;language models don&amp;#x27;t really understand anything&amp;quot; corner is getting smaller and smaller.&lt;p&gt;In my mind, understanding a thing means you can justify an answer. Like a student showing their work and being able to defend it. An answer with a proof understands the answer with respect to the proof it provides. E.g. to understand an answer with regards to first order logic, it&amp;#x27;ll have to be able to defend a logical deduction of that answer.&lt;p&gt;These models still can&amp;#x27;t justify their answers very well, so I&amp;#x27;d say they&amp;#x27;re accurate but only understand with respect to a fairly dumb proof system (e.g. they can select relevant passages or just appeal to overall accuracy statistics). They&amp;#x27;re still far from being able to justify answers in the various ways we do, which I&amp;#x27;d say means that by definition that they still don&amp;#x27;t understand with regards to the &amp;quot;proof systems&amp;quot; that we understand things with regards to.&lt;p&gt;Maybe the next step will require increasingly interesting justification systems.</text></item><item><author>maxwells-daemon</author><text>The &amp;quot;language models don&amp;#x27;t really understand anything&amp;quot; corner is getting smaller and smaller. In the last few months we&amp;#x27;ve seen pretty definitive evidence that transformers can recombine concepts ([1], [2]) and do simple logical inference using contextual information ([3], &amp;quot;make the score font color visible&amp;quot;). I see no reason that this technology couldn&amp;#x27;t smoothly scale into human-level intelligence, yet lots of people seem to think it&amp;#x27;ll require a step change or is impossible.&lt;p&gt;That being said, robust systematic generalization is still a hard problem. But &amp;quot;achieve symbol grounding through tons of multimodal data&amp;quot; is looking more and more like the answer.&lt;p&gt;[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;openai.com&amp;#x2F;blog&amp;#x2F;dall-e&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;openai.com&amp;#x2F;blog&amp;#x2F;dall-e&amp;#x2F;&lt;/a&gt;
[2] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;distill.pub&amp;#x2F;2021&amp;#x2F;multimodal-neurons&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;distill.pub&amp;#x2F;2021&amp;#x2F;multimodal-neurons&amp;#x2F;&lt;/a&gt;
[3] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;openai.com&amp;#x2F;blog&amp;#x2F;openai-codex&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;openai.com&amp;#x2F;blog&amp;#x2F;openai-codex&amp;#x2F;&lt;/a&gt;</text></item></parent_chain></comment><story><title>OpenAI Codex</title><url>https://openai.com/blog/openai-codex/</url></story>