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	<title>Gigaom Search &#187; object recognition</title>
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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>
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		<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>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>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>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>
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		<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>
		</item>
		<item>
		<title>Baidu built a supercomputer for deep learning</title>
		<link>https://gigaom.com/2015/01/14/baidu-has-built-a-supercomputer-for-deep-learning/</link>
		<comments>https://gigaom.com/2015/01/14/baidu-has-built-a-supercomputer-for-deep-learning/#comments</comments>
		<pubDate>Thu, 15 Jan 2015 01:25:21 +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[object recognition]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=906667</guid>
		<description><![CDATA[Chinese search engine company Baidu says it has built the world's most-accurate computer vision system, dubbed Deep Image, which runs on a supercomputer optimized for deep learning algorithms. Baidu claims a 5.98&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2015/01/14/baidu-has-built-a-supercomputer-for-deep-learning/feed/</wfw:commentRss>
		<slash:comments>2</slash:comments>
		</item>
		<item>
		<title>Machine learning will eventually solve your JPEG problem</title>
		<link>https://gigaom.com/2015/01/07/one-step-at-a-time-machine-learning-will-solve-your-jpeg-problem/</link>
		<comments>https://gigaom.com/2015/01/07/one-step-at-a-time-machine-learning-will-solve-your-jpeg-problem/#comments</comments>
		<pubDate>Wed, 07 Jan 2015 21:31:32 +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[photos]]></category>
		<category><![CDATA[smartphone camera]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:GOOG]]></category>
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		<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:DIS]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=904921</guid>
		<description><![CDATA[I take a lot of photos on my smartphone. So many, in fact, that my wife calls me Cellphone Ansel Adams. I can't imagine how many more digital photos we'd have cluttering&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2015/01/07/one-step-at-a-time-machine-learning-will-solve-your-jpeg-problem/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>AI is coming to IoT, and not all the brains will be in the cloud</title>
		<link>https://gigaom.com/2015/01/06/ai-is-coming-to-iot-and-not-all-the-brains-will-be-in-the-cloud/</link>
		<comments>https://gigaom.com/2015/01/06/ai-is-coming-to-iot-and-not-all-the-brains-will-be-in-the-cloud/#comments</comments>
		<pubDate>Tue, 06 Jan 2015 15:14:14 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[brain-like chips]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[object recognition]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:GOOG]]></category>
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		<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:IBM]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=904196</guid>
		<description><![CDATA[Smart devices, appliances and the internet of things are dominating International CES this week, but we're probably just getting a small taste of what's to come -- not only in quantity, but&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2015/01/06/ai-is-coming-to-iot-and-not-all-the-brains-will-be-in-the-cloud/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
		</item>
		<item>
		<title>A startup wants to quantify video content using computer vision</title>
		<link>https://gigaom.com/2014/12/29/a-startup-wants-to-quantify-video-content-using-computer-vision/</link>
		<comments>https://gigaom.com/2014/12/29/a-startup-wants-to-quantify-video-content-using-computer-vision/#comments</comments>
		<pubDate>Mon, 29 Dec 2014 19:37:05 +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[video data]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=903025</guid>
		<description><![CDATA[Computer vision has seen some major advances over the past couple of years, and a New York-based startup called Dextro wants to take the field to a new level by making it easier&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2014/12/29/a-startup-wants-to-quantify-video-content-using-computer-vision/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
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