<story><title>Machine learning is teaching us the secret to teaching</title><url>http://nautil.us/issue/6/secret-codes/teaching-me-softly</url><text></text></story><parent_chain></parent_chain><comment><author>vanderZwan</author><text>Although the article makes no mention of them, George Lakoff and Mark Johnson&amp;#x27;s research on conceptual metaphor[0] seems to be intricately tied to this, somehow. I recently read two books by them: &lt;i&gt;Metaphors We Live By&lt;/i&gt; (see Peter Norvig&amp;#x27;s description here[1]), and &lt;i&gt;Philosophy In The Flesh&lt;/i&gt;[2] and found the ideas in them on how humans use metaphors to make sese of things very interesting and intuitive. It actually left me wondering when and how AI would use these insights.&lt;p&gt;[0] &lt;a href=&quot;http://en.wikipedia.org/wiki/Conceptual_metaphor&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Conceptual_metaphor&lt;/a&gt;&lt;p&gt;[1] &lt;a href=&quot;http://norvig.com/mwlb.html&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;norvig.com&amp;#x2F;mwlb.html&lt;/a&gt; - because it stays focused on language it manages to make a clear and convincing case&lt;p&gt;[2] &lt;a href=&quot;http://www.nytimes.com/books/first/l/lakoff-philosophy.html&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.nytimes.com&amp;#x2F;books&amp;#x2F;first&amp;#x2F;l&amp;#x2F;lakoff-philosophy.html&lt;/a&gt; - by comparison a flawed but nonetheless good read. I agree with the critiques laid out this review: &lt;a href=&quot;http://lesswrong.com/lw/871/review_of_lakoff_johnson_philosophy_in_the_flesh/&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;lesswrong.com&amp;#x2F;lw&amp;#x2F;871&amp;#x2F;review_of_lakoff_johnson_philoso...&lt;/a&gt;</text></comment>
<story><title>Machine learning is teaching us the secret to teaching</title><url>http://nautil.us/issue/6/secret-codes/teaching-me-softly</url><text></text></story><parent_chain></parent_chain><comment><author>skybrian</author><text>I found some slides [1] explaining how this works.&lt;p&gt;A less poetic example of privileged information: if you&amp;#x27;re training on time-series information, you can include events from the future in the training examples, even though they won&amp;#x27;t be available while making predictions in production.&lt;p&gt;Apparently this helps the machine learning algorithm find the outlying data points when the data isn&amp;#x27;t linearly separable.&lt;p&gt;[1] &lt;a href=&quot;http://web.mit.edu/zoya/www/SVM+.pdf&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;web.mit.edu&amp;#x2F;zoya&amp;#x2F;www&amp;#x2F;SVM+.pdf&lt;/a&gt;</text></comment>