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Archive for the ‘machine learning’ category: Page 2

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.”

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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.”

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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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Nov 17, 2015

Can Artificial Intelligence Be Taught?

Posted by in categories: bioengineering, evolution, machine learning, robotics/AI, science

In spite of the popular perception of the state of artificial intelligence, technology has yet to create a robot with the same instincts and adaptability as a human. While humans are born with some natural instincts that have evolved over millions of years, Neuroscientist and Artificial Intelligence Expert Dr. Danko Nikolic believes these same tendencies can be instilled in a robot.

“Our biological children are born with a set of knowledge. They know where to learn, they know where to pay attention. Robots simply can not do that,” Nikolic said. “The problem is you can not program it. There’s a trick we can use called AI Kindergarten. Then we can basically interact with this robot kind of like we do with children in kindergarten, but then make robots learn one level lower, at the level of something called machine genome.”

Programming that machine genome would require all of the innate human knowledge that’s evolved over thousands of years, Nikolic said. Lacking that ability, he said researchers are starting from scratch. While this form of artificial intelligence is still in its embryonic state, it does have some evolutionary advantages that humans didn’t have.

“By using AI Kindergarten, we don’t have to repeat the evolution exactly the way evolution has done it,” Nikolic said. “This experiment has been done already and the knowledge is already stored in our genes, so we can accelerate tremendously. We can skip millions of failed experiments where evolution has failed already.”

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Sep 28, 2015

Intelligent machines: Making AI work in the real world — By Eric Schmidt | BBC News

Posted by in categories: big data, computing, innovation, machine learning, robotics/AI, software

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“As part of the BBC’s Intelligent Machines season, Google’s Eric Schmidt has penned an exclusive article on how he sees artificial intelligence developing, why it is experiencing such a renaissance and where it will go next.”

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Sep 8, 2015

Finding Artificial Intelligence Through Storytelling — An Interview with Dr. Roger Schank

Posted by in categories: machine learning, robotics/AI

The media is all-abuzz with tales of Artificial Intelligence (AI). The provocative two-letter symbol conjures up images of invading autonomous robot drones and Terminator-like machines wreaking havoc on mankind. Then there’s the pervading presence of deep learning and big data, also referred to as artificial intelligence. This might leave some of us wondering, is artificial intelligence one or all of these things?

In that sense, AI leaves a bit of an ambiguous trail – there does not seem to be a clear definition, even amongst scientists and researchers in the field. There are certainly many different branches of AI. I asked Dr. Roger Schank, Professor Emeritus at Northwestern University, for a more clear definition; he told me that artificial intelligence is not big data and deep learning algorithms, at least not in the pure sense of the definition.

Roger emphasizes that intelligence has everything to do with the intersection of learning and interaction and memory. “I will tell you the number one thing people do, it’s pretty obvious – they talk to each other. Guess how hard that is? That is phenomenally hard, that is the subsection of AI called natural language processing, the part that I worked on my whole life, and I understand how far away we are from that.”

Take a “simple” AI concept, such as how to create a computer that plays chess, to better understand the challenge. There are, more or less, two approaches to creating an intelligent machine that can play chess like a champion. The first approach requires programming the computer to predict thousands of moves ahead of time, while the second approach involves building a computer system that tries to imitate a grand master. In the historical pursuit of how to create an artificially intelligent entity, a vast majority of scientists chose the first option of programming based on prediction.

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Aug 30, 2015

AI Dangerous for Economics? The Other Threat Flying Under Radars

Posted by in categories: economics, machine learning, security

Dr. Nils J. Nilsson spent almost a lifetime in the field of Artificial Intelligence (AI) before writing and publishing his book, The Quest for Artificial Intelligence (2009). I recently had the opportunity to speak with the former Stanford computer science professor, now retired at the age of 82, and reflect on the earlier accomplishments that have led to some of the current trends in AI, as well as the serious economic and security considerations that need to be made about AI as society moves ahead in the coming decades.

The Early AI that Powers Today’s Trends

One key contribution of early AI developments included rules-based expert systems, such as MYCIN, which was developed in the mid-1970s by Ted Shortliffe and colleagues at Stanford University. The information built into the diagnostic system was gleaned from medical diagnosticians, and the system would then ask questions based on that information. A person could then type in answers about a patient’s tests, symptoms, etc., and the program would then attempt to diagnose diseases and prescribe therapy.

“Bringing us more up to the future was the occurrence of huge databases (in the 1990s) — sometimes called big data — and the ability of computers to mine that data and find information and make inferences,” remarks Nils. This made possible the new work on face recognition, speech recognition, and language translation. “AI really had what might be called a take off at this time.” Both of these technologies also feed into the launch of IBM’s Watson Healthcare, which combines advanced rules-based systems with big data capabilities and promises to give healthcare providers access to powerful tools in a cloud-based data sharing hub.

Work in neural networks, another catalyst, went through two phases, an earlier phase in the 1950s and 1960s and a latter phase in the 1980s and 1990s. “The second phase (of neural networks) allowed…people to make changes in the connected strength in those networks and multiple layers, and this allowed neural networks that can steer and drive automobiles.” More primitive networks led to the cutting-edge work being done by today’s scientists in the self-driven automobile industry via companies like Tesla and Google.

Continue reading “AI Dangerous for Economics? The Other Threat Flying Under Radars” »

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