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	<title>My geek blog - Brian McQuay &#187; Reviews</title>
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	<link>http://www.brianmcquay.com</link>
	<description>My Ruby on Rails experience, web development tips including SEO, and contributions to open source projects</description>
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		<title>Can&#8217;t see the forest for the trees &#8211; Robots, AI, and the Internet</title>
		<link>http://www.brianmcquay.com/cant-see-the-forest-for-the-trees-robots-ai-and-the-internet/442</link>
		<comments>http://www.brianmcquay.com/cant-see-the-forest-for-the-trees-robots-ai-and-the-internet/442#comments</comments>
		<pubDate>Wed, 04 Jan 2012 00:37:53 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Reviews]]></category>
		<category><![CDATA[Robotics]]></category>
		<category><![CDATA[ai]]></category>
		<category><![CDATA[emergence]]></category>
		<category><![CDATA[internet]]></category>
		<category><![CDATA[robotics]]></category>

		<guid isPermaLink="false">http://www.brianmcquay.com/?p=442</guid>
		<description><![CDATA[I&#8217;d like to share some ideas I&#8217;ve had today about robotics, ai, and the internet. First lets discuss robotics. There has been much work in the field of robotics to create bi-pedal humanoid like machines. Much, if not most, of the work pursues what I consider the ideal: being as human as possible. When you [...]]]></description>
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		<slash:comments>2</slash:comments>
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		<item>
		<title>Playing the Devil&#8217;s Advocate: An Argument for SOPA</title>
		<link>http://www.brianmcquay.com/playing-the-devils-advocate-an-argument-for-sopa/433</link>
		<comments>http://www.brianmcquay.com/playing-the-devils-advocate-an-argument-for-sopa/433#comments</comments>
		<pubDate>Mon, 26 Dec 2011 23:48:22 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Reviews]]></category>
		<category><![CDATA[dns]]></category>
		<category><![CDATA[internet legislation]]></category>
		<category><![CDATA[sopa]]></category>

		<guid isPermaLink="false">http://www.brianmcquay.com/?p=433</guid>
		<description><![CDATA[There&#8217;s much ado online these past few weeks promoting the demise of the SOPA act. Many smart people are arguing it will destroy the internet as we know it. That may be true but that may also be exactly what we need. We&#8217;ve long known about the problems with the current DNS system from its [...]]]></description>
		<wfw:commentRss>http://www.brianmcquay.com/playing-the-devils-advocate-an-argument-for-sopa/433/feed</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Review of &#8220;How to Grow a Mind: Statistics, Structure and Abstraction&#8221;</title>
		<link>http://www.brianmcquay.com/review-of-how-to-grow-a-mind-statistics-structure-and-abstraction/415</link>
		<comments>http://www.brianmcquay.com/review-of-how-to-grow-a-mind-statistics-structure-and-abstraction/415#comments</comments>
		<pubDate>Tue, 19 Jul 2011 18:23:28 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[Reviews]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[data structures]]></category>
		<category><![CDATA[knowledge representation]]></category>
		<category><![CDATA[machine learning]]></category>

		<guid isPermaLink="false">http://www.brianmcquay.com/?p=415</guid>
		<description><![CDATA[This was an amazing lecture by Josh Tenenbaum. He presents motivation for determining the type of data structure for representing knowledge without knowing how the knowledge is best organized a priori. He presents the ways children learn and how they organize knowledge at an early age starting at a very simple structure and building up [...]]]></description>
		<wfw:commentRss>http://www.brianmcquay.com/review-of-how-to-grow-a-mind-statistics-structure-and-abstraction/415/feed</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Review of &#8220;Latent Factor Models for Relational Arrays and Network Data&#8221;</title>
		<link>http://www.brianmcquay.com/review-of-latent-factor-models-for-relational-arrays-and-network-data/410</link>
		<comments>http://www.brianmcquay.com/review-of-latent-factor-models-for-relational-arrays-and-network-data/410#comments</comments>
		<pubDate>Tue, 19 Jul 2011 16:36:14 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Modeling]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[Reviews]]></category>
		<category><![CDATA[latent class model]]></category>
		<category><![CDATA[latent distance model]]></category>
		<category><![CDATA[latent factor model]]></category>
		<category><![CDATA[online lecture]]></category>
		<category><![CDATA[social network data]]></category>

		<guid isPermaLink="false">http://www.brianmcquay.com/?p=410</guid>
		<description><![CDATA[This was an excellent video lecture on latent factor models by Peter Hoff. The speaker goes over various other models that may fit social network data. He presents a comparison of the models to a sample data set of a highschool. The comparisons show that the latent factor model is able to fit characteristics of [...]]]></description>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Review of &#8220;On Three-Layer Architectures&#8221;</title>
		<link>http://www.brianmcquay.com/review-of-on-three-layer-architectures/395</link>
		<comments>http://www.brianmcquay.com/review-of-on-three-layer-architectures/395#comments</comments>
		<pubDate>Tue, 19 Jul 2011 01:43:21 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Research]]></category>
		<category><![CDATA[Reviews]]></category>
		<category><![CDATA[Robotics]]></category>
		<category><![CDATA[3T]]></category>
		<category><![CDATA[ATLANTIS]]></category>
		<category><![CDATA[control systems]]></category>
		<category><![CDATA[DARPA Grand Challenge]]></category>
		<category><![CDATA[RAPs]]></category>
		<category><![CDATA[sense plan act]]></category>
		<category><![CDATA[SPA]]></category>
		<category><![CDATA[SSS]]></category>
		<category><![CDATA[subsumption]]></category>
		<category><![CDATA[three-layered architecture]]></category>

		<guid isPermaLink="false">http://www.brianmcquay.com/?p=395</guid>
		<description><![CDATA[The author goes into a detailed history of robotics control systems and in particular how &#8220;Subsumption&#8221;, or SSS, revolutionized the field which was previously dominated by sense-plan-act control systems (SPA). Next the point out the major flaw in Subsumption was the complexity at higher layers. Higher layers depended on lower layers and therefore were increasingly [...]]]></description>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Review of &#8220;Winning the DARPA Grand Challenge with an AI Robot&#8221;</title>
		<link>http://www.brianmcquay.com/review-of-winning-the-darpa-grand-challenge-with-an-ai-robot/389</link>
		<comments>http://www.brianmcquay.com/review-of-winning-the-darpa-grand-challenge-with-an-ai-robot/389#comments</comments>
		<pubDate>Mon, 18 Jul 2011 17:47:54 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[Reviews]]></category>
		<category><![CDATA[Robotics]]></category>
		<category><![CDATA[DARPA]]></category>
		<category><![CDATA[DARPA Grand Challenge]]></category>
		<category><![CDATA[Markov model]]></category>
		<category><![CDATA[three-layered architecture]]></category>
		<category><![CDATA[UKF]]></category>
		<category><![CDATA[unscented Kalman filter]]></category>

		<guid isPermaLink="false">http://www.brianmcquay.com/?p=389</guid>
		<description><![CDATA[This paper was detailing the approach taken by the winning team of the DARPA Grand Challenge, a robotics competition to build an autonomous vehicle to drive over 100 miles across desert terrain. The architecture used in was of interest to me. Specifically the &#8220;three-layered architecture&#8221; which &#8220;pipelines data through a series of layers, transforming sensor [...]]]></description>
		<wfw:commentRss>http://www.brianmcquay.com/review-of-winning-the-darpa-grand-challenge-with-an-ai-robot/389/feed</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Review of &#8220;Matrix Factorization Techniques for Recommender Systems&#8221;</title>
		<link>http://www.brianmcquay.com/review-of-matrix-factorization-techniques-for-recommender-systems/384</link>
		<comments>http://www.brianmcquay.com/review-of-matrix-factorization-techniques-for-recommender-systems/384#comments</comments>
		<pubDate>Sun, 17 Jul 2011 21:30:31 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[Reviews]]></category>
		<category><![CDATA[latent factor model]]></category>
		<category><![CDATA[latent factor modeling]]></category>
		<category><![CDATA[matrix factorization]]></category>
		<category><![CDATA[recommender system]]></category>

		<guid isPermaLink="false">http://www.brianmcquay.com/?p=384</guid>
		<description><![CDATA[The authors present their research on matrix factorization with respect to their winning entry in the Netflix Prize for a recommender system. One point of interest or clarification the authors made on my understanding of matrix factorization was that extrapolating characteristics from patterns within a dataset is called &#8220;latent factor modeling&#8221;. Latent factor modeling is [...]]]></description>
		<wfw:commentRss>http://www.brianmcquay.com/review-of-matrix-factorization-techniques-for-recommender-systems/384/feed</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Review of &#8220;All Together Now:  A Perspective on the NETFLIX PRIZE&#8221;</title>
		<link>http://www.brianmcquay.com/review-of-all-together-now-a-perspective-on-the-netflix-prize/377</link>
		<comments>http://www.brianmcquay.com/review-of-all-together-now-a-perspective-on-the-netflix-prize/377#comments</comments>
		<pubDate>Sun, 17 Jul 2011 18:47:32 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Reviews]]></category>
		<category><![CDATA[crowdsourcing]]></category>
		<category><![CDATA[gradient decent]]></category>
		<category><![CDATA[matrix factorization]]></category>
		<category><![CDATA[nearest neighbor]]></category>
		<category><![CDATA[temporal drift]]></category>

		<guid isPermaLink="false">http://www.brianmcquay.com/?p=377</guid>
		<description><![CDATA[In 2006, Netflix announced a 1 million dollar prize for an algorithm to recommend movies to users that they would be interested in that could beat their current algorithm by at least 10%. This paper reviews the methodology taken by the winning team to achieve victory after 3 years of work. The winning solution was [...]]]></description>
		<wfw:commentRss>http://www.brianmcquay.com/review-of-all-together-now-a-perspective-on-the-netflix-prize/377/feed</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Review of &#8220;Introduction to the special issue on empirical evaluations  in reinforcement learning&#8221;</title>
		<link>http://www.brianmcquay.com/review-of-introduction-to-the-special-issue-on-empirical-evaluations-in-reinforcement-learning/372</link>
		<comments>http://www.brianmcquay.com/review-of-introduction-to-the-special-issue-on-empirical-evaluations-in-reinforcement-learning/372#comments</comments>
		<pubDate>Sun, 17 Jul 2011 16:02:29 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Reviews]]></category>
		<category><![CDATA[empirical]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[research]]></category>

		<guid isPermaLink="false">http://www.brianmcquay.com/?p=372</guid>
		<description><![CDATA[This is a brief review the introduction article for the Machine Learning Journal &#8220;Special Issue on Empirical Evaluations in Reinforcement Learning&#8221;, Volume 84, Numbers 1-2 / July 2011. The article does a good job at tying together seemingly disparate empirical research papers on machine learning. It presents a few past empirical studies and provides brief [...]]]></description>
		<wfw:commentRss>http://www.brianmcquay.com/review-of-introduction-to-the-special-issue-on-empirical-evaluations-in-reinforcement-learning/372/feed</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Review of &#8220;The Development of Brain-Machine Interface Neuroprosthetic Devices&#8221;</title>
		<link>http://www.brianmcquay.com/review-of-the-development-of-brain-machine-interface-neuroprosthetic-devices/340</link>
		<comments>http://www.brianmcquay.com/review-of-the-development-of-brain-machine-interface-neuroprosthetic-devices/340#comments</comments>
		<pubDate>Mon, 18 Oct 2010 01:08:32 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Neuroscience]]></category>
		<category><![CDATA[Reviews]]></category>
		<category><![CDATA[bmi]]></category>
		<category><![CDATA[brain-machine interface]]></category>
		<category><![CDATA[ECoG]]></category>
		<category><![CDATA[EEG]]></category>
		<category><![CDATA[LFP]]></category>
		<category><![CDATA[neuron recording]]></category>
		<category><![CDATA[neuroprosthetic feedback]]></category>
		<category><![CDATA[neuroprosthetics]]></category>

		<guid isPermaLink="false">http://www.brianmcquay.com/?p=340</guid>
		<description><![CDATA[Summary: This paper presents a review of current brain-machine interface research with respect to how such BMI devices would be used for neuroprosthetic devices. The authors present a high level overview of the current research at each step for a neuroprothestic device to be realized. There are currently 4 main techniques for reading data from [...]]]></description>
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		<slash:comments>0</slash:comments>
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