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	<title>Gigaom Search &#187; Technologies and Products &#187; computer vision</title>
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		<title>Why you can&#8217;t program intelligent robots, but you can train them</title>
		<link>http://gigaom.com/2015/03/02/you-cant-program-intelligent-robots-but-you-can-train-them/</link>
		<comments>http://gigaom.com/2015/03/02/you-cant-program-intelligent-robots-but-you-can-train-them/#comments</comments>
		<pubDate>Mon, 02 Mar 2015 16:10:20 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[machine-learning]]></category>
		<category><![CDATA[Structure Data 2015]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=916526</guid>
		<description><![CDATA[If it feels like we're in the midst of robot renaissance right now, perhaps it's because we are. There is a new crop of robots under development that we'll soon be&#8230;]]></description>
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		<slash:comments>0</slash:comments>
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		<title>Microsoft is building fast, low-power neural networks with FPGAs</title>
		<link>http://gigaom.com/2015/02/23/microsoft-is-building-fast-low-power-neural-networks-with-fpgas/</link>
		<comments>http://gigaom.com/2015/02/23/microsoft-is-building-fast-low-power-neural-networks-with-fpgas/#comments</comments>
		<pubDate>Mon, 23 Feb 2015 17:28:12 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Altera]]></category>
		<category><![CDATA[FPGAs]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Microsoft Research]]></category>
		<category><![CDATA[neural networks]]></category>
		<category><![CDATA[object recognition]]></category>
		<category><![CDATA[processor architecture]]></category>
		<category><![CDATA[web scale]]></category>
		<category><![CDATA[webscale infrastructure]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:MSFT]]></category>
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		<guid isPermaLink="false">http://gigaom.com/?p=916437</guid>
		<description><![CDATA[Microsoft on Monday released a white paper explaining a current effort to run convolutional neural networks -- the deep learning technique responsible for record-setting computer vision algorithms -- on FPGAs rather&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/23/microsoft-is-building-fast-low-power-neural-networks-with-fpgas/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
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		<item>
		<title>Why deep learning is at least inspired by biology, if not the brain</title>
		<link>http://gigaom.com/2015/02/14/why-deep-learning-is-at-least-inspired-by-biology-if-not-the-brain/</link>
		<comments>http://gigaom.com/2015/02/14/why-deep-learning-is-at-least-inspired-by-biology-if-not-the-brain/#comments</comments>
		<pubDate>Sat, 14 Feb 2015 18:58:13 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Computational neuroscience]]></category>
		<category><![CDATA[Enlitic]]></category>
		<category><![CDATA[health care]]></category>
		<category><![CDATA[healthcare]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[neuroscience]]></category>
		<category><![CDATA[object recognition]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=915084</guid>
		<description><![CDATA[As deep learning continues gathering steam among researchers, entrepreneurs and the press, there's a loud-and-getting-louder debate about whether its algorithms actually operate like the human brain does. The comparison might not make much&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/14/why-deep-learning-is-at-least-inspired-by-biology-if-not-the-brain/feed/</wfw:commentRss>
		<slash:comments>4</slash:comments>
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		<title>Microsoft says its new computer vision system can outperform humans</title>
		<link>http://gigaom.com/2015/02/13/microsoft-says-its-new-computer-vision-system-can-outperform-humans/</link>
		<comments>http://gigaom.com/2015/02/13/microsoft-says-its-new-computer-vision-system-can-outperform-humans/#comments</comments>
		<pubDate>Fri, 13 Feb 2015 19:17:35 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[image recognition]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Microsoft Research]]></category>
		<category><![CDATA[object recognition]]></category>
		<category><![CDATA[Structure Data 2015]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:GOOG]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:MSFT]]></category>
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		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:MSFT]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=914914</guid>
		<description><![CDATA[Microsoft researchers claim in a recently published paper that they have developed the first computer system capable of outperforming humans on a popular benchmark. While it's estimated that humans can classify images in&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/13/microsoft-says-its-new-computer-vision-system-can-outperform-humans/feed/</wfw:commentRss>
		<slash:comments>6</slash:comments>
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		<title>Industrial IoT startup Sight Machine raises $5M, expands to robots</title>
		<link>https://gigaom.com/2015/02/09/industrial-iot-startup-sight-machine-raises-5m-expands-to-robots/</link>
		<comments>https://gigaom.com/2015/02/09/industrial-iot-startup-sight-machine-raises-5m-expands-to-robots/#comments</comments>
		<pubDate>Mon, 09 Feb 2015 17:55:37 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Industrial internet]]></category>
		<category><![CDATA[sensor data]]></category>
		<category><![CDATA[Sight Machine]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=913537</guid>
		<description><![CDATA[Sight Machine, a startup trying to simplify the collection and analysis of industrial data, has raised a $5 million venture capital round from Mercury Fund, Michigan eLab, Huron River Ventures, Orfin&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2015/02/09/industrial-iot-startup-sight-machine-raises-5m-expands-to-robots/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
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		<title>PhotoTime is a deep learning application for the rest of us</title>
		<link>https://gigaom.com/2015/02/06/phototime-is-a-deep-learning-application-for-the-rest-of-us/</link>
		<comments>https://gigaom.com/2015/02/06/phototime-is-a-deep-learning-application-for-the-rest-of-us/#comments</comments>
		<pubDate>Fri, 06 Feb 2015 20:23:58 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[object recognition]]></category>
		<category><![CDATA[Orbeus]]></category>
		<category><![CDATA[photo tagging]]></category>
		<category><![CDATA[PhotoTime]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=913150</guid>
		<description><![CDATA[A Sunnyvale, California, startup called Orbeus has developed what could be the best application yet for letting everyday consumers benefit from advances in deep learning. It's called PhotoTime and, yes, it's&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2015/02/06/phototime-is-a-deep-learning-application-for-the-rest-of-us/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Indiegogo project CarVi puts a drive camera into older cars</title>
		<link>https://gigaom.com/2015/02/05/indiegogo-project-carvi-puts-a-drive-camera-into-older-cars/</link>
		<comments>https://gigaom.com/2015/02/05/indiegogo-project-carvi-puts-a-drive-camera-into-older-cars/#comments</comments>
		<pubDate>Thu, 05 Feb 2015 20:01:42 +0000</pubDate>
		<dc:creator><![CDATA[Kevin Fitchard]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[driver assistance]]></category>
		<category><![CDATA[seeing cars]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=912873</guid>
		<description><![CDATA[A lot of the new high-end cars hitting the road come with a bevy of sensors designed to assist drivers and in some cases prevent an accident from happening. Rear and&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2015/02/05/indiegogo-project-carvi-puts-a-drive-camera-into-older-cars/feed/</wfw:commentRss>
		<slash:comments>3</slash:comments>
		</item>
		<item>
		<title>TeraDeep wants to bring deep learning to your dumb devices</title>
		<link>https://gigaom.com/2015/02/02/teradeep-wants-to-bring-deep-learning-to-your-dumb-devices/</link>
		<comments>https://gigaom.com/2015/02/02/teradeep-wants-to-bring-deep-learning-to-your-dumb-devices/#comments</comments>
		<pubDate>Mon, 02 Feb 2015 21:00:54 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[FPGAs]]></category>
		<category><![CDATA[hardware]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[microprocessors]]></category>
		<category><![CDATA[object recognition]]></category>
		<category><![CDATA[Structure Data 2015]]></category>
		<category><![CDATA[Structure Data awards]]></category>
		<category><![CDATA[TeraDeep]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=911680</guid>
		<description><![CDATA[Open the closet of any gadget geek or computer nerd, and you're likely to find a lot of skeletons. Stacked deep in a cardboard box or Tupperware tub, there they are:&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2015/02/02/teradeep-wants-to-bring-deep-learning-to-your-dumb-devices/feed/</wfw:commentRss>
		<slash:comments>2</slash:comments>
		</item>
		<item>
		<title>Move over Emeril: Robot learns how to prep food from YouTube</title>
		<link>http://gigaom.com/2015/01/30/move-over-emeril-robot-learns-how-to-prep-food-from-youtube/</link>
		<comments>http://gigaom.com/2015/01/30/move-over-emeril-robot-learns-how-to-prep-food-from-youtube/#comments</comments>
		<pubDate>Fri, 30 Jan 2015 22:47:54 +0000</pubDate>
		<dc:creator><![CDATA[Signe Brewster]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[cooking]]></category>
		<category><![CDATA[machine learning]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=911347</guid>
		<description><![CDATA[These days, you can learn just about anything from YouTube videos -- from how to tie a knot to the best way to open a wine bottle with a shoe. And&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/01/30/move-over-emeril-robot-learns-how-to-prep-food-from-youtube/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>New to deep learning? Here are 4 easy lessons from Google</title>
		<link>http://gigaom.com/2015/01/29/new-to-deep-learning-here-are-4-easy-lessons-from-google/</link>
		<comments>http://gigaom.com/2015/01/29/new-to-deep-learning-here-are-4-easy-lessons-from-google/#comments</comments>
		<pubDate>Fri, 30 Jan 2015 01:26:11 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[object recognition]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:GOOG]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:GOOG]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=911067</guid>
		<description><![CDATA[Google employs some of the world's smartest researchers in deep learning and artificial intelligence, so it's not a bad idea to listen to what they have to say about the space. One&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/01/29/new-to-deep-learning-here-are-4-easy-lessons-from-google/feed/</wfw:commentRss>
		<slash:comments>6</slash:comments>
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