Tuesday, November 10, 2015

Google Just Open Sourced TensorFlow, Its Artificial Intelligence Engine

TECH PUNDIT TIM O’Reilly had just tried the new Google Photos app, and he was amazed by the depth of its artificial intelligence.

O’Reilly was standing a few feet from Google CEO and co-founder Larry Page this past May, at a small cocktail reception for the press at the annual Google I/O conference—the centerpiece of the company’s year. Google had unveiled its personal photos app earlier in the day, andO’Reilly marveled that if he typed something like “gravestone” into the search box, the app could find a photo of his uncle’s grave, taken so long ago.

The app uses an increasingly powerful form of artificial intelligence called deep learning. By analyzing thousands of photos of gravestones, this AI technology can learn to identify a gravestone it has never seen before. The same goes for cats and dogs, trees and clouds, flowers and food.

The Google Photos search engine isn’t perfect. But its accuracy is enormously impressive—so impressive that O’Reilly couldn’t understand why Google didn’t sell access to its AI engine via the Internet, cloud-computing style, letting others drive their apps with the same machine learning. That could be Google’s real money-maker, he said. After all, Google also uses this AI engine to recognize spoken words, translate from one language to another, improve Internet search results, and more. The rest of the world could turn this tech towards so many other tasks, from ad targeting to computer security.

Well, this morning, Google took O’Reilly’s idea further than even he expected. It’s not selling access to its deep learning engine. It’s open sourcing that engine, freely sharing the underlying code with the world at large. This software is called TensorFlow, and in literally giving the technology away, Google believes it can accelerate the evolution of AI. Through open source, outsiders can help improve on Google’s technology and, yes, return these improvements back to Google.

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IBM brings Watson's cognitive computing to sports

IBM took cognitive computing into the sports world today with a trio of partnerships under which cognitive applications-powered by Watson will help prevent concussions, change the nature of training in golf and transform fans' game-day experiences. The partnerships with Triax Technologies, Spare5 and 113 Industries will use the power of cognitive computing in different ways.

"Cognitive is a new form of computing that represents a seismic shift in technology," Lauri Saft, vice president, IBM Watson Ecosystem, said in a statement today. "We've moved beyond systems that are programmed — the technologies most of us use today — to systems that understand, reason and learn. These latest partnerships exemplify the entrepreneurial nature of our Watson ecosystem. Like so many other industries, sports is awash in data, and cognitive computing allows IBM's partners like Triax Technologies, 113 Industries and Spare5 to apply deeper insights to all of that information to improve athlete performance and redefine the fan experience."

Reducing concussions

Triax Technologies develops and manufactures products to ensure the health and safety of athletes. Its new Triax Smart Impact Monitor (SIM) is a wearable sensor that can be embedded in headbands or skullcaps to track the force and frequency of head impacts. The company says (SIM) empowers parents, coaches and athletic trainers with the tools to improve player safety and refine technique in real-time. Using Watson language service, the device can factor in more diverse data sources to analyze sentiment and infer cognitive and social characteristics.

It's in the hole ...

Watson's deep learning, natural language and vision capabilities are powering Watson Golf Pro from Spare 5. The cognitive app is a personal caddy that amateur players can consult while at the driving range or on the course. It's been trained with a corpus of knowledge from contracted golf professionals on mechanics and drills. By "seeing" a golfer's swing, the app can provide feedback for improving that swing.

Keeping the fan engaged (and spending)

113 Industries is bringing Watson to hockey. It's working with the Pittsburgh Penguins to transform the fan game-day experience with 113 Industries' "Pi" service embedded with Watson natural language and cognitive capabilities. This allows the Penguins to analyze large volumes of fan-based data to develop specialized offers and services for fans at the CONSOL Energy Center. This includes concessions to merchandise and pre-/post-game entertainment.

Tuesday, November 3, 2015

Artificial Intelligence Outperforms Human Data Scientists

Artificial intelligence may be poised to ease the shortage of data scientists who build models that explain and predict patterns in the ocean of “Big Data” representing today’s world. An MIT startup’s computer software has proved capable of building better predictive models than the majority of human researchers it competed against in several recent data science contests. 

Until now, well-paid data scientists have relied on their human intuition to create and test computer models that can explain and predict patterns in data. But MIT’s “Data Science Machine” software represents a fully automated process capable of building such predictive computer models by identifying relevant features in raw data. Such a tool could make human data scientists even more effective by allowing them to build and test such predictive models in far less time. But it might also help more individuals and companies harness the power of Big Data without the aid of trained data scientists.

“I think the biggest potential is for increasing the pool of people who are capable of doing data science,” Max Kanter, a data scientist at MIT’s Computer Science and AI Lab and co-creator of the Data Science Machine software, told IEEE Spectrum. “If you look at the growth in demand for people with data science abilities, it’s far outpacing the number of people who have those skills.”

The Data Science Machine can automatically create accurate predictive models based on raw datasets within two to 12 hours; a team of human data scientists may require months. A paper on the Data Science Machine will be presented this week at the IEEE International Conference on Data Science and Advanced Analytics being held in Paris from 19–21 Oct.

Trained data scientists, who typically draw salaries above $100,000 on average, remain a coveted but scarce resource for companies as diverse as Facebook and Walmart. In 2011, the McKinsey Global Institute estimated that the United States alone might face a shortage of 140,000 to 190,000 people with the analytical skills necessary for data science. A 2012 issue of the Harvard Business Review declared data scientist as the sexiest job of the 21st century.

The reason for such high demand for data scientists comes from Big Data’s revolutionary promise of tapping into vast collections of data—whether it’s the online behavior of social media users, the movements of financial markets worth trillions of dollars, or the billions of celestial objects spotted by telescopes—to explain and predict patterns in the huge datasets. Such models could help companies predict the future behavior of individual customers or aid astronomers in automatically identifying an object in the starry nighttime sky.

But how do you transform a sea of raw data into information that can help businesses or researchers identify and predict patterns? Human data scientists usually have to spend weeks or months working on their predictive computer algorithms. First, they sift through the raw data to identify key variables that could help predict the behavior of related observations over time. Then they must continuously test and refine those variables in a series of computer models that often use machine learning techniques.

Such a time-consuming part of the data scientists’ job description inspired Kanter, an MIT grad student at the time, and Kalyan Veeramachaneni, a research scientist at MIT’s Computer Science and AI Lab who acted as Kanter’s master’s thesis advisor, to try creating a computer program that could automate the biggest bottlenecks in data science.

Previous computer software programs aimed at solving such data science problems have tended to be one dimensional, focusing on problems particular to specific industries or fields. But Kanter and Veeramachaneni wanted their Data Science Machine software to be capable of tackling any general data science problem. Veeramachaneni in particular drew on his experience of seeing similar connections among the many industry data science problems he had worked on during his time at MIT. 

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Hilary Mason: Use data science and machine intelligence to build a better future

An algorithm can creatively reimagine the Mona Lisa.

Now what?

In the opening keynote of the Grace Hopper Women in Computing Conference 2015 in Houston, Texas, Fast Forward Labs CEO Hilary Mason talked about the burgeoning world of data science and machine intelligence, and several of the considerations for how they will affect the future.

But first, in a subtle nod to the #ILookLikeAnEngineer movement, Mason introduced herself like this: "I'm a computer scientist, a data scientist, a software engineer, I'm also a CEO and I look like all of those things."

And then she dove into machine intelligence.

"Machines are starting to do things that we might have thought were more in the creative domain of humans," she said, showing several computer-generated takes on the classic Da Vinci painting. Or, she also pointed out some of her favorite data-based apps that have already changed the ways that users function, like Google Maps, Foursquare, or Dark Sky.

Mason outlined reasons why data science and machine learning are having a moment: we have the computing power, we know what to do with data when we have it, and, we're getting access to more and more of it.

Looking at her own history with data, Mason described a moment she and a co-worker had while she was working at Bitly as chief scientist. They were making changes to a Hadoop cluster they had. In order to test a job, they decided to find out what the cutest animal on the internet was.

"We had just used hours of compute time and a petabyte of data to answer the most frivolous question," she said. That ability, though, to "play" with data is important. Mason also referenced a Kickstarter for a LED light up "disco dog" suite — it's a smart phone-controlled vest for your dog.

"When you start to see the ridiculous things occurring, you know something interesting is happening because that means the technology is something we all can use," Mason said.

But, in building new things, even silly things it's important to remember unintended and unforeseen consequences. For example, in 1999, Sony was building and selling a toy robotic dog called Aibo. Recently, though, they stopped supporting them, so if someone happened to still be using their robodog and it malfunctioned, there was no reviving it. And that was actually more common than one would think, leading to funerals for those longtime robotic pets by bereaved owners.

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