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Archive for the ‘machine learning’ category

Aug 24, 2016

The iBrain is Here And it’s Already Inside Your Phone — By Steven Level | Backchannel

Posted by in categories: business, machine learning, robotics/AI

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“An exclusive inside look at how artificial intelligence and machine learning work at Apple”

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Aug 5, 2016

This startup uses machine learning and satellite imagery to predict crop yields — By Alex Brokaw | The Verge

Posted by in categories: big data, business, machine learning, satellites, space

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“Instead, Descartes relies on 4 petabytes of satellite imaging data and a machine learning algorithm to figure out how healthy the corn crop is from space.”

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Jul 22, 2016

Google Sprints Ahead in AI Building Blocks, Leaving Rivals Wary — By Jack Clark | Bloomberg

Posted by in categories: machine learning, robotics/AI

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“There’s a high-stakes race under way in Silicon Valley to develop software that makes it easy to weave artificial intelligence technology into almost everything, and Google has sprinted into the lead.”

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Jul 5, 2016

When Humanity Meets A.I. | a16z Podcast

Posted by in categories: disruptive technology, education, ethics, machine learning, robotics/AI

Podcast with “Andreessen Horowitz Distinguished Visiting Professor of Computer Science … Fei-Fei Li [who publishes under Li Fei-Fei], associate professor at Stanford University.”

May 18, 2016

May 18th-20th Google I/O Developers Conference Live Feed

Posted by in categories: machine learning, virtual reality

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Today’s conference emphasizes virtual reality and machine learning.

Live Feed

May 12, 2016

Recommendation Engines Yielding Stronger Predictions into Our Wants and Needs

Posted by in categories: computing, disruptive technology, economics, information science, innovation, internet, machine learning, software

If you’ve ever seen a “recommended item” on eBay or Amazon that was just what you were looking for (or maybe didn’t know you were looking for), it’s likely the suggestion was powered by a recommendation engine. In a recent interview, Co-founder of machine learning startup Delvv, Inc., Raefer Gabriel, said these applications for recommendation engines and collaborative filtering algorithms are just the beginning of a powerful and broad-reaching technology.

Raefer Gabriel, Delvv, Inc.

Raefer Gabriel, Delvv, Inc.

Gabriel noted that content discovery on services like Netflix, Pandora, and Spotify are most familiar to people because of the way they seem to “speak” to one’s preferences in movies, games, and music. Their relatively narrow focus of entertainment is a common thread that has made them successful as constrained domains. The challenge lies in developing recommendation engines for unbounded domains, like the internet, where there is more or less unlimited information.

“Some of the more unbounded domains, like web content, have struggled a little bit more to make good use of the technology that’s out there. Because there is so much unbounded information, it is hard to represent well, and to match well with other kinds of things people are considering,” Gabriel said. “Most of the collaborative filtering algorithms are built around some kind of matrix factorization technique and they definitely tend to work better if you bound the domain.”

Continue reading “Recommendation Engines Yielding Stronger Predictions into Our Wants and Needs” »

Mar 30, 2016

Silicon Valley Looks to Artificial Intelligence for the Next Big Thing — By Quentin Hardy | The New York Times

Posted by in categories: business, machine learning, robotics/AI

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” “There is going to be a boom for design companies, because there’s going to be so much information people have to work through quickly,” said Diane B. Greene, the head of Google Compute Engine, one of the companies hoping to steer an A.I. boom. “Just teaching companies how to use A.I. will be a big business.” ”

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Jan 19, 2016

Connecting The Dots to Get the Big Picture with Artificial Intelligence

Posted by in categories: big data, disruptive technology, economics, information science, machine learning

Ask the average passerby on the street to describe artificial intelligence and you’re apt to get answers like C-3PO and Apple’s Siri. But for those who follow AI developments on a regular basis and swim just below the surface of the broad field , the idea that the foreseeable AI future might be driven more by Big Data rather than big discoveries is probably not a huge surprise. In a recent interview with Data Scientist and Entrepreneur Eyal Amir, we discussed how companies are using AI to connect the dots between data and innovation.

Image credit: Startup Leadership Program Chicago

Image credit: Startup Leadership Program Chicago

According to Amir, the ability to make connections between big data together has quietly become a strong force in a number of industries. In advertising for example, companies can now tease apart data to discern the basics of who you are, what you’re doing, and where you’re going, and tailor ads to you based on that information.

“What we need to understand is that, most of the time, the data is not actually available out there in the way we think that it is. So, for example I don’t know if a user is a man or woman. I don’t know what amounts of money she’s making every year. I don’t know where she’s working,” said Eyal. “There are a bunch of pieces of data out there, but they are all suggestive. (But) we can connect the dots and say, ‘she’s likely working in banking based on her contacts and friends.’ It’s big machines that are crunching this.”

Continue reading “Connecting The Dots to Get the Big Picture with Artificial Intelligence” »

Jan 7, 2016

Apple Buys Artificial-Intelligence Startup Emollient — By Rolfe Winkler, et al | The Wall Street Journal

Posted by in categories: computing, electronics, machine learning, mobile phones, robotics/AI, software

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“Apple Inc. has purchased Emotient Inc., a startup that uses artificial-intelligence technology to read people’s emotions by analyzing facial expressions.”

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Nov 25, 2015

‘Go’ Is the Game Machines Can’t Beat. Google’s Artificial Intelligence Whiz Hints That His Will — By Mark Bergen | Re/code

Posted by in categories: business, computing, innovation, machine learning, neuroscience, robotics/AI

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“When the world’s smartest researchers train computers to become smarter, they like to use games. Go, the two-player board game born in China more than two millennia ago, remains the nut that machines still can’t crack.”

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