<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>kqr</author><text>I took the opportunity of seasonal proximity to family and tried this question on a small selection of 6--8 year olds, and I received one correct response (accompanied by proudly showing the correct application of long multiplication on a big piece of paper) along with two &amp;quot;I don&amp;#x27;t know, what is it?&amp;quot;&lt;p&gt;None invented their own algorithm and confidently claimed it to be the way.</text><parent_chain><item><author>johnfn</author><text>I don&amp;#x27;t think I buy this argument. ChatGPT seems to understand how to reason about a large multiplication the same that a 6 or 7 year old might, and I would expect a 6 or 7 year old to make similarly large errors. No one claims that 6 or 7 year olds are unable to reason.</text></item><item><author>ookdatnog</author><text>Reasoning is meaningfully distinct from what the models do now. You can ask ChatGPT to multiply two smallish natural numbers and often it will give the correct answer. Now look what happens if you ask it to multiply two very large numbers:&lt;p&gt;&amp;gt; To multiply two large numbers, such as 3146746135164684 times 468473136468433, we can use the traditional multiplication algorithm. This algorithm involves breaking the numbers down into smaller groups of digits, multiplying each group by the corresponding group of digits in the other number, and then adding up the products to find the final result.&lt;p&gt;&amp;gt; Here is an example of how to multiply 3146746135164684 times 468473136468433 using the traditional multiplication algorithm:&lt;p&gt;&amp;gt;    Break the numbers down into groups of digits. For example, we could group the digits of 3146746135164684 as 314, 6746, 135, 1646, 84 and group the digits of 468473136468433 as 4684, 7313, 6468, 433.&lt;p&gt;&amp;gt;    Multipply each group of digits by the corresponding group of digits in the other number. For example, we would multiply 314 times 4684 to get 1470196, 6746 times 7313 to get 49470158, 135 times 6468 to get 873980, 1646 times 433 to get 708338, and 84 times 1 to get 84.&lt;p&gt;&amp;gt;    Add up the products to find the final result. In this case, the final result would be 1470196 + 49470158 + 873980 + 708338 + 84 = 146845556.&lt;p&gt;&amp;gt; Therefore, the result of multiplying 3146746135164684 times 468473136468433 using the traditional multiplication algorithm is 146845556.&lt;p&gt;It&amp;#x27;s not just that the answer is wrong, is that it&amp;#x27;s complete nonsense.&lt;p&gt;Reasoning is a style of thinking that scales. You may be more likely to get the wrong answer in a very long chain of reasoning because at every step you have a nonzero chance of making a mistake, but the mistake is identifiable and explainable. That&amp;#x27;s why teachers ask you to show your work. Even if you get the answer wrong, they can see at a glance whether you understand the material or not. We can see at a glance that ChatGPT does not understand multiplication.</text></item><item><author>johnfn</author><text>I&amp;#x27;m not the first to say it, but the distinction over whether models do any &amp;quot;actual reasoning&amp;quot; or not seems moot to me. Whether or not they do reasoning, they answer questions with a decent degree of accuracy, and that degree of accuracy is only going up as we feed the models more data. Whether or not they &amp;quot;do actual reasoning&amp;quot; simply won&amp;#x27;t matter.&lt;p&gt;They&amp;#x27;re already superhuman in some regards; I don&amp;#x27;t think that I could have coded up the solution to that problem in 5 seconds. :)</text></item><item><author>trynewideas</author><text>This is a great review but it still misses what seems like the point to me: these models don&amp;#x27;t do any actual reasoning. They&amp;#x27;re doing the same thing that DALL-E &lt;i&gt;etc.&lt;/i&gt; does with images: using a superhuman store of potential outcomes to mimic an outcome that the person entering the prompt would then click a thumbs-up icon on in a training model.&lt;p&gt;Asking why the model doesn&amp;#x27;t explain how the code it generated works is like asking a child who just said their first curse word what it means. The model and child alike don&amp;#x27;t know or care, they just know how people react to it.</text></item></parent_chain></comment><story><title>Peter Norvig critically reviews AlphaCode’s code quality</title><url>https://github.com/norvig/pytudes/blob/main/ipynb/AlphaCode.ipynb</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>fossuser</author><text>Yeah, in the original gpt-3 paper one of the more interesting bits was that it made similar off by one errors a human would make when doing arithmetic (and they controlled for memorized test data).</text><parent_chain><item><author>johnfn</author><text>I don&amp;#x27;t think I buy this argument. ChatGPT seems to understand how to reason about a large multiplication the same that a 6 or 7 year old might, and I would expect a 6 or 7 year old to make similarly large errors. No one claims that 6 or 7 year olds are unable to reason.</text></item><item><author>ookdatnog</author><text>Reasoning is meaningfully distinct from what the models do now. You can ask ChatGPT to multiply two smallish natural numbers and often it will give the correct answer. Now look what happens if you ask it to multiply two very large numbers:&lt;p&gt;&amp;gt; To multiply two large numbers, such as 3146746135164684 times 468473136468433, we can use the traditional multiplication algorithm. This algorithm involves breaking the numbers down into smaller groups of digits, multiplying each group by the corresponding group of digits in the other number, and then adding up the products to find the final result.&lt;p&gt;&amp;gt; Here is an example of how to multiply 3146746135164684 times 468473136468433 using the traditional multiplication algorithm:&lt;p&gt;&amp;gt;    Break the numbers down into groups of digits. For example, we could group the digits of 3146746135164684 as 314, 6746, 135, 1646, 84 and group the digits of 468473136468433 as 4684, 7313, 6468, 433.&lt;p&gt;&amp;gt;    Multipply each group of digits by the corresponding group of digits in the other number. For example, we would multiply 314 times 4684 to get 1470196, 6746 times 7313 to get 49470158, 135 times 6468 to get 873980, 1646 times 433 to get 708338, and 84 times 1 to get 84.&lt;p&gt;&amp;gt;    Add up the products to find the final result. In this case, the final result would be 1470196 + 49470158 + 873980 + 708338 + 84 = 146845556.&lt;p&gt;&amp;gt; Therefore, the result of multiplying 3146746135164684 times 468473136468433 using the traditional multiplication algorithm is 146845556.&lt;p&gt;It&amp;#x27;s not just that the answer is wrong, is that it&amp;#x27;s complete nonsense.&lt;p&gt;Reasoning is a style of thinking that scales. You may be more likely to get the wrong answer in a very long chain of reasoning because at every step you have a nonzero chance of making a mistake, but the mistake is identifiable and explainable. That&amp;#x27;s why teachers ask you to show your work. Even if you get the answer wrong, they can see at a glance whether you understand the material or not. We can see at a glance that ChatGPT does not understand multiplication.</text></item><item><author>johnfn</author><text>I&amp;#x27;m not the first to say it, but the distinction over whether models do any &amp;quot;actual reasoning&amp;quot; or not seems moot to me. Whether or not they do reasoning, they answer questions with a decent degree of accuracy, and that degree of accuracy is only going up as we feed the models more data. Whether or not they &amp;quot;do actual reasoning&amp;quot; simply won&amp;#x27;t matter.&lt;p&gt;They&amp;#x27;re already superhuman in some regards; I don&amp;#x27;t think that I could have coded up the solution to that problem in 5 seconds. :)</text></item><item><author>trynewideas</author><text>This is a great review but it still misses what seems like the point to me: these models don&amp;#x27;t do any actual reasoning. They&amp;#x27;re doing the same thing that DALL-E &lt;i&gt;etc.&lt;/i&gt; does with images: using a superhuman store of potential outcomes to mimic an outcome that the person entering the prompt would then click a thumbs-up icon on in a training model.&lt;p&gt;Asking why the model doesn&amp;#x27;t explain how the code it generated works is like asking a child who just said their first curse word what it means. The model and child alike don&amp;#x27;t know or care, they just know how people react to it.</text></item></parent_chain></comment><story><title>Peter Norvig critically reviews AlphaCode’s code quality</title><url>https://github.com/norvig/pytudes/blob/main/ipynb/AlphaCode.ipynb</url></story>