Tag Archives: IBM

Roundup 10/31/2016

Top machine learning (ML) and deep learning (DL) stories from last week. Plus some good reads, and special Halloween content. The featured image comes from MIT’s Nightmare Machine. Horrifying AI/ML/DL — Aussie bots that hunt down and kill small animals. — Bots with knives that cut flesh. — Machines that hunt and kill humans. — IBM Watson engines that profile you and sell

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Machine Learning Roundup: 10/6/2016

Machine learning (ML) and deep learning (DL) content from the past 24 hours, plus a few older items. Good Reads Gale Morrison’s survey of neural computing in Semiconductor Engineering is an absolute must read. She summarizes intellectual and technical developments in the field, market drivers and briefly discusses what works. Companies mentioned: Amazon, Baidu, Google, Huawei, Intel, Nervana, NVIDIA, and

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Machine Learning Roundup (September 30, 2016)

Machine learning (ML) and deep learning (DL) content from the past 24 hours. Public Beta for Google Cloud Machine Learning Google releases Cloud Machine Learning services from private beta to public beta, announces additional customer support plans and a certification program. In VentureBeat, Jordan Novet reports on Google’s brand launch for Google Cloud. Stephanie Condon reports that Google seeks to

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Machine Learning Roundup (September 29, 2016)

Machine learning (ML) and deep learning (DL) content from the past 24 hours. ICYMI. Issues On the Electronic Frontier Foundation’s Deeplinks blog, Gennie Gebhart and Erica Portnoy ponder privacy issues raised by facial recognition technology.In Fortune, Jeff John Roberts enumerates what companies get wrong about ML. Also in Fortune, Roger Parloff explains why DL is suddenly changing your life. Opinions

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Big Analytics Roundup (September 19, 2016)

Many thanks to Australia’s Dez Blanchfield for his contributions to this roundup. We set out to create a special “Australia/APAC” edition; however, most of the stories have a global interest: chips are chips and deep learning is deep learning wherever you live. We did find this story, profiling a Tasmanian oyster farm that uses Microsoft’s IoT hub. Well, that’s embarrassing. MapR’s

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Big Analytics Roundup (August 22, 2016)

MIT Technology Review reports that Chicago’s experiment in predictive policing isn’t working. Data scientists developed a list of a few hundred people likely to commit a shooting; police, however, ignored the predictions, primarily because nobody told them what to do with individuals on the list. The report illustrates a fundamental truth about data science: no amount of insight matters unless your

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