<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
			xmlns:itunes="http://www.itunes.com/dtds/podcast-1.0.dtd"
		>

<channel>
	<title>Gigaom Search &#187; Technologies and Products &#187; artificial intelligence</title>
	<atom:link href="http://search.gigaom.com/technology/artificial-intelligence/feed/" rel="self" type="application/rss+xml" />
	<link>http://search.gigaom.com</link>
	<description></description>
	<lastBuildDate>Wed, 11 Mar 2015 11:51:00 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>hourly</sy:updatePeriod>
	<sy:updateFrequency>1</sy:updateFrequency>
	<generator>http://wordpress.org/?v=4.1</generator>
	<item>
		<title>How PayPal uses deep learning and detective work to fight fraud</title>
		<link>http://gigaom.com/2015/03/06/how-paypal-uses-deep-learning-and-detective-work-to-fight-fraud/</link>
		<comments>http://gigaom.com/2015/03/06/how-paypal-uses-deep-learning-and-detective-work-to-fight-fraud/#comments</comments>
		<pubDate>Fri, 06 Mar 2015 20:32:34 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[fraud]]></category>
		<category><![CDATA[Internet fraud]]></category>
		<category><![CDATA[machine-learning]]></category>
		<category><![CDATA[online payment platform]]></category>
		<category><![CDATA[Pattern recognition]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:FB]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:GOOG]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:MSFT]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:FB]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:GOOG]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:MSFT]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=919400</guid>
		<description><![CDATA[Hui Wang has seen the nature of online fraud change a lot in the 11 years she's been at PayPal. In fact, a continuous evolution of methods is kind of the nature of&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/03/06/how-paypal-uses-deep-learning-and-detective-work-to-fight-fraud/feed/</wfw:commentRss>
		<slash:comments>2</slash:comments>
		</item>
		<item>
		<title>IBM acquires deep learning startup AlchemyAPI</title>
		<link>http://gigaom.com/2015/03/04/ibm-acquires-deep-learning-startup-alchemyapi/</link>
		<comments>http://gigaom.com/2015/03/04/ibm-acquires-deep-learning-startup-alchemyapi/#comments</comments>
		<pubDate>Wed, 04 Mar 2015 16:15:56 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[ibm-watson]]></category>
		<category><![CDATA[machine-learning]]></category>
		<category><![CDATA[Watson]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:IBM]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=918762</guid>
		<description><![CDATA[So much for AlchemyAPI CEO Elliot Turner's statement that his company is not for sale. IBM has bought the Denver-based deep learning startup that delivers a wide variety of text analysis&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/03/04/ibm-acquires-deep-learning-startup-alchemyapi/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>A look at Zeroth, Qualcomm’s effort to put AI in your smartphone</title>
		<link>http://gigaom.com/2015/03/03/a-look-at-zeroth-qualcomms-effort-to-put-ai-in-your-smartphone/</link>
		<comments>http://gigaom.com/2015/03/03/a-look-at-zeroth-qualcomms-effort-to-put-ai-in-your-smartphone/#comments</comments>
		<pubDate>Tue, 03 Mar 2015 15:08:09 +0000</pubDate>
		<dc:creator><![CDATA[Kevin Fitchard]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[machine-learning]]></category>
		<category><![CDATA[Mobile World Congress]]></category>
		<category><![CDATA[Mobile World Congress (MWC)]]></category>
		<category><![CDATA[MWC]]></category>
		<category><![CDATA[MWC 2015]]></category>
		<category><![CDATA[neural networks]]></category>
		<category><![CDATA[Structure Data 2015]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:QCOM]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:QCOM]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=918382</guid>
		<description><![CDATA[What if your smartphone camera were smart enough to identify that the plate of clams and black beans appearing in its lens was actually food? What if it then automatically could&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/03/03/a-look-at-zeroth-qualcomms-effort-to-put-ai-in-your-smartphone/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Google, Stanford say big data is key to deep learning for drug discovery</title>
		<link>http://gigaom.com/2015/03/02/google-stanford-say-big-data-is-key-to-deep-learning-for-drug-discovery/</link>
		<comments>http://gigaom.com/2015/03/02/google-stanford-say-big-data-is-key-to-deep-learning-for-drug-discovery/#comments</comments>
		<pubDate>Mon, 02 Mar 2015 20:22:11 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[drug discovery]]></category>
		<category><![CDATA[machine-learning]]></category>
		<category><![CDATA[medical data]]></category>
		<category><![CDATA[pharmaceutical industry]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:GOOG]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:GOOG]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:IBM]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=918172</guid>
		<description><![CDATA[A team of researchers from Stanford University and Google have released a paper highlighting a deep learning approach they say shows promise in the field of drug discovery. What they found, essentially, is that&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/03/02/google-stanford-say-big-data-is-key-to-deep-learning-for-drug-discovery/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<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>
		<wfw:commentRss>http://gigaom.com/2015/03/02/you-cant-program-intelligent-robots-but-you-can-train-them/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<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>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:MSFT]]></category>
		
		<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>
		</item>
		<item>
		<title>Remember when machine learning was hard? That&#8217;s about to change</title>
		<link>http://gigaom.com/2015/02/21/remember-when-machine-learning-was-hard-thats-about-to-change/</link>
		<comments>http://gigaom.com/2015/02/21/remember-when-machine-learning-was-hard-thats-about-to-change/#comments</comments>
		<pubDate>Sat, 21 Feb 2015 17:56:40 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Structure Data 2015]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NASDAQ:MSFT]]></category>
		<category domain="http://search.gigaom.com/stock/"><![CDATA[NSDQ:MSFT]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=916311</guid>
		<description><![CDATA[A few years ago, there was a shift in the world of machine learning. Companies, such as Skytree and Context Relevant, began popping up, promising to make it easier for companies&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/21/remember-when-machine-learning-was-hard-thats-about-to-change/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
<enclosure url="http://traffic.libsyn.com/gigaom/021815_01-audio.mp3" length="41459419" type="audio/mpeg" />
		</item>
		<item>
		<title>The 4 things (at least) you&#8217;ll learn about at Structure Data</title>
		<link>http://gigaom.com/2015/02/19/the-4-things-at-least-youll-learn-about-at-structure-data/</link>
		<comments>http://gigaom.com/2015/02/19/the-4-things-at-least-youll-learn-about-at-structure-data/#comments</comments>
		<pubDate>Thu, 19 Feb 2015 17:34:45 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[data science]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Structure Data 2015]]></category>

		<guid isPermaLink="false">http://gigaom.com/?p=915784</guid>
		<description><![CDATA[Gigaom's Structure Data conference is less than a month away, kicking off March 18 in New York. There are a lot of reasons to attend -- great location, great networking, free drinks --&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/19/the-4-things-at-least-youll-learn-about-at-structure-data/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
		</item>
		<item>
		<title>Watson-powered toy blows past Kickstarter goal in a day</title>
		<link>http://gigaom.com/2015/02/17/watson-powered-toy-blows-past-kickstarter-goal-in-a-day/</link>
		<comments>http://gigaom.com/2015/02/17/watson-powered-toy-blows-past-kickstarter-goal-in-a-day/#comments</comments>
		<pubDate>Tue, 17 Feb 2015 19:46:39 +0000</pubDate>
		<dc:creator><![CDATA[Derrick Harris]]></dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[ibm-watson]]></category>
		<category><![CDATA[Structure Data 2015]]></category>
		<category><![CDATA[voice-interface]]></category>
<category domain="http://search.gigaom.com/stock/"><![CDATA[NYSE:IBM]]></category>
		
		<guid isPermaLink="false">http://gigaom.com/?p=915294</guid>
		<description><![CDATA[First it was Jeopardy!, then it was cancer, e-commerce and cooking. Now, IBM's Watson artificial intelligence system is powering a line of connected toys. And it looks as if people are&#8230;]]></description>
		<wfw:commentRss>http://gigaom.com/2015/02/17/watson-powered-toy-blows-past-kickstarter-goal-in-a-day/feed/</wfw:commentRss>
		<slash:comments>4</slash:comments>
		</item>
		<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>
<enclosure url="http://traffic.libsyn.com/gigaom/021215_01-AudioMp3.mp3" length="45749770" type="audio/mpeg" />
		</item>
	</channel>
</rss>