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	<title>Gigaom Search &#187; Companies &#187; Baidu</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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		<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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		<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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		<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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		<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>
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		<title>Baidu claims deep learning breakthrough with Deep Speech</title>
		<link>https://gigaom.com/2014/12/18/baidu-claims-deep-learning-breakthrough-with-deep-speech/</link>
		<comments>https://gigaom.com/2014/12/18/baidu-claims-deep-learning-breakthrough-with-deep-speech/#comments</comments>
		<pubDate>Thu, 18 Dec 2014 14:33:41 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[machine learning]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:FB]]></category>
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		<guid isPermaLink="false">http://gigaom.com/?p=901378</guid>
		<description><![CDATA[Chinese search engine giant Baidu says it has developed a speech recognition system, called Deep Speech, the likes of which has never been seen, especially in noisy environments. In restaurant settings&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2014/12/18/baidu-claims-deep-learning-breakthrough-with-deep-speech/feed/</wfw:commentRss>
		<slash:comments>5</slash:comments>
		</item>
		<item>
		<title>Uber&#8217;s first ever Global Head of Safety hints at improvements</title>
		<link>https://gigaom.com/2014/12/17/uber-hires-first-ever-global-head-of-safety/</link>
		<comments>https://gigaom.com/2014/12/17/uber-hires-first-ever-global-head-of-safety/#comments</comments>
		<pubDate>Wed, 17 Dec 2014 20:05:05 +0000</pubDate>
		<dc:creator><![CDATA[Carmel DeAmicis]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[admit]]></category>
		<category><![CDATA[background checks]]></category>
		<category><![CDATA[better]]></category>
		<category><![CDATA[expand]]></category>
		<category><![CDATA[global head of safety]]></category>
		<category><![CDATA[Improve]]></category>
		<category><![CDATA[investigate]]></category>
		<category><![CDATA[procedures]]></category>
		<category><![CDATA[SAFE]]></category>
		<category><![CDATA[Safety]]></category>
		<category><![CDATA[taxis]]></category>
		<category><![CDATA[Vetting]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:GOOG]]></category>
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		<guid isPermaLink="false">http://gigaom.com/?p=901133</guid>
		<description><![CDATA[Uber riders received an email Wednesday from the company's new "Head of Global Safety," Philip Cardenas. Cardenas introduced himself as a new hire whose team is reviewing Uber's safety practices around the world&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2014/12/17/uber-hires-first-ever-global-head-of-safety/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>How Xiaomi could generate real money in emerging markets</title>
		<link>http://research.gigaom.com/2014/12/how-xiaomi-could-generate-real-money-in-emerging-markets/</link>
		<comments>http://research.gigaom.com/2014/12/how-xiaomi-could-generate-real-money-in-emerging-markets/#comments</comments>
		<pubDate>Wed, 17 Dec 2014 14:00:48 +0000</pubDate>
		<dc:creator><![CDATA[Colin Gibbs]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:AAPL]]></category>
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		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:GOOG]]></category>
		
		<guid isPermaLink="false">http://research.gigaom.com/?p=243034</guid>
		<description><![CDATA[Xiaomi has suddenly emerged as one of the world's largest smartphone producers, but those phones generate razor-thin margins. The company's ever-increasing footprint may give it a chance to build lucrative app&#8230;]]></description>
		<wfw:commentRss>http://research.gigaom.com/2014/12/how-xiaomi-could-generate-real-money-in-emerging-markets/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Nokia Here data to power Baidu&#8217;s non-Chinese maps</title>
		<link>https://gigaom.com/2014/12/15/nokia-here-data-to-power-baidus-non-chinese-maps/</link>
		<comments>https://gigaom.com/2014/12/15/nokia-here-data-to-power-baidus-non-chinese-maps/#comments</comments>
		<pubDate>Mon, 15 Dec 2014 11:04:27 +0000</pubDate>
		<dc:creator><![CDATA[David Meyer]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[energy-efficient light bulbs]]></category>
		<category><![CDATA[geolocation]]></category>
		<category><![CDATA[Nokia HERE]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:MSFT]]></category>
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		<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:NOK]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=900389</guid>
		<description><![CDATA[The Chinese web giant Baidu has decided on Nokia as its mapping partner for services outside of China. 's Here platform is one of the key remaining businesses of the Finnish&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2014/12/15/nokia-here-data-to-power-baidus-non-chinese-maps/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
		</item>
		<item>
		<title>Uber gets investment from Baidu to aid China push, report claims</title>
		<link>https://gigaom.com/2014/12/12/uber-gets-investment-from-baidu-to-aid-china-push-report-claims/</link>
		<comments>https://gigaom.com/2014/12/12/uber-gets-investment-from-baidu-to-aid-china-push-report-claims/#comments</comments>
		<pubDate>Fri, 12 Dec 2014 09:09:41 +0000</pubDate>
		<dc:creator><![CDATA[David Meyer]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[China]]></category>
		<category><![CDATA[investment]]></category>
		<category><![CDATA[taxi]]></category>
		<category><![CDATA[WeChat]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=900080</guid>
		<description><![CDATA[The Chinese web giant Baidu will buy a stake in Uber worth up to $600 million, according to sources quoted by Bloomberg. The ride-booking company, which raised $1.2 billion earlier this&#8230;]]></description>
		<wfw:commentRss>https://gigaom.com/2014/12/12/uber-gets-investment-from-baidu-to-aid-china-push-report-claims/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>It doesn&#8217;t matter if deep learning mimics the brain or Watson is cognitive. It matters if they work</title>
		<link>https://gigaom.com/2014/10/29/it-doesnt-matter-if-deep-learning-mimics-the-brain-or-watson-is-cognitive-it-matters-if-they-work/</link>
		<comments>https://gigaom.com/2014/10/29/it-doesnt-matter-if-deep-learning-mimics-the-brain-or-watson-is-cognitive-it-matters-if-they-work/#comments</comments>
		<pubDate>Wed, 29 Oct 2014 15:33:56 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Michael Jordan]]></category>
		<category><![CDATA[open source]]></category>
		<category><![CDATA[text analysis]]></category>
		<category><![CDATA[Watson]]></category>
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		<guid isPermaLink="false">http://gigaom.com/?p=884228</guid>
		<description><![CDATA[Recent comments by machine learning experts have caused a stir, but debate over the novelty or architecture of deep learning might be best left in academia . As AI techniques make&#8230;]]></description>
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		<slash:comments>3</slash:comments>
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