<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>mark_l_watson</author><text>I work in the field of deep learning but in the 1980s and 1990s I used Common Lisp and worked on symbolic AI projects.&lt;p&gt;For several years, my gut instinct has been that the two technologies should be combined. Since neural nets are basically functions, I think it makes sense to compose functional programs using network models for perception, word and graph embedding, etc.&lt;p&gt;EDIT: I can’t wait to see the published results in May!
EDIT 2: another commenter reelin posted a link to the draft paper &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;openreview.net&amp;#x2F;pdf?id=rJgMlhRctm&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;openreview.net&amp;#x2F;pdf?id=rJgMlhRctm&lt;/a&gt;</text><parent_chain></parent_chain></comment><story><title>Combine statistical and symbolic artificial intelligence techniques</title><url>http://news.mit.edu/2019/teaching-machines-to-reason-about-what-they-see-0402</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>xvilka</author><text>There is an interesting project - DeepProbLog[1], based on the ProbLog[2] (a Prolog dialect with probabilistic reasoning) and Deep Learning combined. I only wish it was Rust, so it would have been safer, faster, and easier to embed in your programs. I have high hopes to the Scryer Prolog[3], and it seems[4] the author think about probabilistic extensions too.&lt;p&gt;[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;bitbucket.org&amp;#x2F;problog&amp;#x2F;deepproblog&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;bitbucket.org&amp;#x2F;problog&amp;#x2F;deepproblog&lt;/a&gt;&lt;p&gt;[2] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;dtai.cs.kuleuven.be&amp;#x2F;problog&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;dtai.cs.kuleuven.be&amp;#x2F;problog&amp;#x2F;&lt;/a&gt;&lt;p&gt;[3] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;mthom&amp;#x2F;scryer-prolog&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;mthom&amp;#x2F;scryer-prolog&lt;/a&gt;&lt;p&gt;[4] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;mthom&amp;#x2F;scryer-prolog&amp;#x2F;issues&amp;#x2F;69&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;mthom&amp;#x2F;scryer-prolog&amp;#x2F;issues&amp;#x2F;69&lt;/a&gt;</text><parent_chain></parent_chain></comment><story><title>Combine statistical and symbolic artificial intelligence techniques</title><url>http://news.mit.edu/2019/teaching-machines-to-reason-about-what-they-see-0402</url></story>