<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	>
<channel>
	<title>Comments on: Pentagon Supercomputer Powers IED-Hunting</title>
	<atom:link href="http://www.captainsjournal.com/2007/11/30/pentagon-supercomputer-powers-ied-hunting/feed/" rel="self" type="application/rss+xml" />
	<link>http://www.captainsjournal.com/2007/11/30/pentagon-supercomputer-powers-ied-hunting/</link>
	<description>News &#38; Commentary on Warfare, Policy and Counterterrorism</description>
	<pubDate>Wed, 20 Aug 2008 12:19:51 +0000</pubDate>
	<generator>http://wordpress.org/?v=2.5</generator>
		<item>
		<title>By: Brian H</title>
		<link>http://www.captainsjournal.com/2007/11/30/pentagon-supercomputer-powers-ied-hunting/#comment-24044</link>
		<dc:creator>Brian H</dc:creator>
		<pubDate>Sun, 02 Dec 2007 09:56:16 +0000</pubDate>
		<guid isPermaLink="false">http://www.captainsjournal.com/2007/11/30/pentagon-supercomputer-powers-ied-hunting/#comment-24044</guid>
		<description>True neural net learning would be more interesting; it is best suited for distinguishing patterns and recognizing form.  Algorithms are human rule sets for logically deriving "correct" answers, and are limited by the verbalization / conceptualization capacities of the composer(s).  Real experts do not follow rules; they tack between significant examples, and only use words and numbers after the fact.  Neural nets do the same; it is not usually possible to track or describe how they reach conclusions, and often the result itself is not readily "boxable".  

I suspect that there are many ideosyncratic practices and patterns amongst IED makers and planters, both from individual characteristics and from instruction/teaching patterns passed on by insurgent highers.  Some may love culverts; others ruts.  Some like dissolved and reset asphalt; some like planting inside brick walls.  Etc.  Trying to make one-ruleset-fits-all "AI" systems from simulated scenarios is an inherently limited strategy.</description>
		<content:encoded><![CDATA[<p>True neural net learning would be more interesting; it is best suited for distinguishing patterns and recognizing form.  Algorithms are human rule sets for logically deriving &#8220;correct&#8221; answers, and are limited by the verbalization / conceptualization capacities of the composer(s).  Real experts do not follow rules; they tack between significant examples, and only use words and numbers after the fact.  Neural nets do the same; it is not usually possible to track or describe how they reach conclusions, and often the result itself is not readily &#8220;boxable&#8221;.  </p>
<p>I suspect that there are many ideosyncratic practices and patterns amongst IED makers and planters, both from individual characteristics and from instruction/teaching patterns passed on by insurgent highers.  Some may love culverts; others ruts.  Some like dissolved and reset asphalt; some like planting inside brick walls.  Etc.  Trying to make one-ruleset-fits-all &#8220;AI&#8221; systems from simulated scenarios is an inherently limited strategy.</p>
]]></content:encoded>
	</item>
</channel>
</rss>
