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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Roadmap towards Machine Intelligence · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1511.08130/</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/1511.08130/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Roadmap towards Machine Intelligence — mentioned by sapphireblue</title><link>https://news.ycombinator.com/item?id=12197681</link><guid isPermaLink="false">urn:hackerpapers:mention:1511.08130:12197681</guid><pubDate>Sun, 31 Jul 2016 16:55:11 GMT</pubDate><description>&lt;p&gt;The author proposes a lot of vague ideas in this article (for example &amp;quot;I believe one of the biggest problems is the use of Error Propagation and Gradient Descent&amp;quot;) without references or any solid proofs why they are necessary to solve the proposed program (Automate programming using ML?). In fact there is already a lot of solid work just on this subject: * Learning algorithms from examples http://arxiv.org/abs/1511.07275 https://arxiv.org/abs/1410.5401 * Generating source code from natural language description http://arxiv.org/abs/1510.07211 * And, the most closest work to what author…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1511.08130/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>A Roadmap towards Machine Intelligence — mentioned by mtrn</title><link>https://news.ycombinator.com/item?id=10784809</link><guid isPermaLink="false">urn:hackerpapers:mention:1511.08130:10784809</guid><pubDate>Wed, 23 Dec 2015 18:36:09 GMT</pubDate><description>&lt;p&gt;* Program design in the UNIX environment (1984): http://harmful.cat-v.org/cat-v/unix_prog_design.pdf Glipse into unix essentials. Do one thing well on an example so you&amp;#x27;ll never forget it again. Doing less requires more care and attention to detail. * From Frequency to Meaning (2010): https://www.jair.org/media/2934/live-2934-4846-jair.pdf A nice summary on vector space models along with three basic matrix layouts: term-document, word-context, pair-pattern and the resulting applications and algorithms. * A Roadmap towards Machine Intelligence (2015): http://arxiv.org/pdf/1511.08130v1.pdf…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1511.08130/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>A Roadmap towards Machine Intelligence — HN submission</title><link>https://news.ycombinator.com/item?id=10634208</link><guid isPermaLink="false">urn:hackerpapers:submission:1511.08130:10634208</guid><pubDate>Thu, 26 Nov 2015 18:57:33 GMT</pubDate><description>&lt;p&gt;A Roadmap Towards Machine Intelligence&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;59 points; 29 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1511.08130/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
