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Is Facebook Moving Into A.I.?

December 6, 2016 by  
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Facebook Inc is developing a way to automatically flag offensive material in live video streams, building on a growing effort to use artificial intelligence to monitor content, said Joaquin Candela, the company’s director of applied machine learning.

The social media company has been embroiled in a number of content moderation controversies this year, from facing international outcry after removing an iconic Vietnam War photo due to nudity, to allowing the spread of fake news on its site.

Facebook has historically relied mostly on users to report offensive posts, which are then checked by Facebook employees against company “community standards.” Decisions on especially thorny content issues that might require policy changes are made by top executives at the company.

Candela told reporters that Facebook increasingly was using artificial intelligence to find offensive material. It is “an algorithm that detects nudity, violence, or any of the things that are not according to our policies,” he said.

The company already had been working on using automation to flag extremist video content, as Reuters reported in June.

Now the automated system also is being tested on Facebook Live, the streaming video service for users to broadcast live video.

Using artificial intelligence to flag live video is still at the research stage, and has two challenges, Candela said. “One, your computer vision algorithm has to be fast, and I think we can push there, and the other one is you need to prioritize things in the right way so that a human looks at it, an expert who understands our policies, and takes it down.”

Facebook said it also uses automation to process the tens of millions of reports it gets each week, to recognize duplicate reports and route the flagged content to reviewers with the appropriate subject matter expertise.

Chief Executive Officer Mark Zuckerberg in November said Facebook would turn to automation as part of a plan to identify fake news. Ahead of the Nov. 8 U.S. election, Facebook users saw fake news reports erroneously alleging that Pope Francis endorsed Donald Trump and that a federal agent who had been investigating Democratic candidate Hillary Clinton was found dead.

However, determining whether a particular comment is hateful or bullying, for example, requires context, the company said.

Source-http://www.thegurureview.net/aroundnet-category/facebook-developing-artificial-intelligence-to-patrol-live-videos.html

Intel To Acquire Deep Learning Company Nervana

August 19, 2016 by  
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Intel is acquiring deep-learning startup Nervana Systems in a deal that could help it make up for lost ground in the increasingly hot area of artificial intelligence.

Founded in 2014, California-based Nervana offers a hosted platform for deep learning that’s optimized “from algorithms down to silicon” to solve machine-learning problems, the startup says.

Businesses can use its Nervana cloud service to build and deploy applications that make use of deep learning, a branch of AI used for tasks like image recognition and uncovering patterns in large amounts of data.

Also of interest to Intel, Nervana is developing a specialty processor, known as an ASIC, that’s custom built for deep learning.

Financial terms of the deal were not disclosed, but one estimate put the value above $350 million.

“We will apply Nervana’s software expertise to further optimize the Intel Math Kernel Library and its integration into industry standard frameworks,” Diane Bryant, head of Intel’s Data Center Group, said in a blog post. Nervana’s expertise “will advance Intel’s AI portfolio and enhance the deep-learning performance and TCO of our Intel Xeon and Intel Xeon Phi processors.”

Though Intel also acquired AI firm Saffron late last year, the Nervana acquisition “clearly defines the start of Intel’s AI portfolio,” said Paul Teich, principal analyst with Tirias Research.

“Intel has been chasing high-performance computing very effectively, but their hardware-design teams missed the convolutional neural network transition a few years ago,” Teich said. CNNs are what’s fueling the current surge in artificial intelligence, deep learning and machine learning.

As part of Intel, Nervana will continue to operate out of its San Diego headquarters, cofounder and CEO Naveen Rao said in a blog post.

The startup’s 48-person team will join Intel’s Data Center Group after the deal’s close, which is expected “very soon,” Intel said.

Source- http://www.thegurureview.net/aroundnet-category/intel-to-acquire-deep-learning-company-nervana.html

Interest Grows In Collaborative Robots

July 5, 2016 by  
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Robots that work as assistants in unison with people are set to upend the world of industrial robotics by putting automation within reach of many small and medium-sized companies for the first time, according to industry experts.

Collaborative robots, or “cobots”, tend to be inexpensive, easy to use and safe to be around. They can easily be adapted to new tasks, making them well-suited to small-batch manufacturing and ever-shortening product cycles.

Cobots can typically lift loads of up 10 kilograms (22 lb) and can be small enough to put on top of a workbench. They can help with repetitive tasks like picking and placing, packaging or gluing and welding.

Some can repeat a task after being guided once through the process by a worker and recording it. The price of a cobot can be as little as $10,000, although typically they cost two to three times that.

The global cobot market is set to grow from $116 million last year to $11.5 billion by 2025, capital goods analysts at Barclays estimate. That would be roughly equal to the size of the entire industrial robotics market today.

“By 2020 it will be a game-changer,” said Stefan Lampa, head of robotics of Germany’s Kuka, during a panel discussion organized by the International Federation of Robotics (IFR) at the Automatica trade fair in Munich.

Growth in industrial robot unit sales slowed to 12 percent last year from 29 percent in 2014, the IFR said on Wednesday, weighed by a sharp fall in top buyer China.

The world’s top industrial robot makers – Japan’s Fanuc and Yaskawa, Swiss ABB and Kuka – all have collaborative robots on the market, although sales are not yet significant for them.

But the market leader and pioneer is Denmark’s Universal Robots, a start-up that sold its first cobot in 2009 and was acquired by U.S. automatic test equipment maker Teradyne for $285 million last year.

Source-http://www.thegurureview.net/aroundnet-category/interest-grows-in-collaborative-robots.html

IBM’s Watson Goes Cybersecurity

May 23, 2016 by  
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IBM Security has announced a new year-long research project through which it will partner with eight universities to help train its Watson artificial intelligence system to tackle cybercrime.

Knowledge about threats is often hidden in unstructured sources such as blogs, research reports and documentation, said Kevin Skapinetz, director of strategy for IBM Security.

“Let’s say tomorrow there’s an article about a new type of malware, then a bunch of follow-up blogs,” Skapinetz explained. “Essentially what we’re doing is training Watson not just to understand that those documents exist, but to add context and make connections between them.”

Over the past year, IBM Security’s own experts have been working to teach Watson the “language of cybersecurity,” he said. That’s been accomplished largely by feeding it thousands of documents annotated to help the system understand what a threat is, what it does and what indicators are related, for example.

“You go through the process of annotating documents not just for nouns and verbs, but also what it all means together,” Skapinetz said. “Then Watson can start making associations.”

Now IBM aims to accelerate the training process. This fall, it will begin working with students at universities including California State Polytechnic University at Pomona, Penn State, MIT, New York University and the University of Maryland at Baltimore County along with Canada’s universities of New Brunswick, Ottawa and Waterloo.

Over the course of a year, the program aims to feed up to 15,000 new documents into Watson every month, including threat intelligence reports, cybercrime strategies, threat databases and materials from IBM’s own X-Force research library. X-Force represents 20 years of security research, including details on 8 million spam and phishing attacks and more than 100,000 documented vulnerabilities.

Watson’s natural language processing capabilities will help it make sense of those reams of unstructured data. Its data-mining techniques will help detect outliers, and its graphical presentation tools will help find connections among related data points in different documents, IBM said.

Ultimately, the result will be a cloud service called Watson for Cyber Security that’s designed to provide insights into emerging threats as well as recommendations on how to stop them.

Source-http://www.thegurureview.net/computing-category/ibms-watson-to-get-schooled-on-cybersecurity.html

Elon Musk Opens Gym For AI Programmers 

May 10, 2016 by  
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Techie entrepreneur Elon Musk has rolled out an open-source training “gym” for artificial-intelligence programmers.

It’s an interesting move for a man who in 2014 said artificial intelligence, or A.I., will pose a threat to the human race.

“I think we should be very careful about artificial intelligence,” Musk said about a year and a half ago during an MIT symposium. “If I were to guess at what our biggest existential threat is, it’s probably that… with artificial intelligence, we are summoning the demon. In all those stories with the guy with the pentagram and the holy water, and he’s sure he can control the demon. It doesn’t work out.”

Today, Musk is moving to help programmers use A.I. and machine learning to build smart robots and smart devices.

“We’re releasing the public beta of OpenAI Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms,” wrote Greg Brockman, OpenAI’s CTO, and John Schulman, a scientist working with OpenAI, in a blog post . “We originally built OpenAI Gym as a tool to accelerate our own RL research. We hope it will be just as useful for the broader community.”

The OpenAI Gym is meant as a tool for programmers to use to teach their intelligent systems better ways to learn and develop more complex reasoning. In short, it’s meant to make smart systems smarter.

Musk is a co-chair of OpenAI, a $1 billion organization that was unveiled last December as an effort focused on advancing artificial intelligence that will benefit humanity.

While Musk has warned of what he sees as the perils of A.I., it’s also a technology that he needs for his businesses.

The OpenAI Gym is made up of a suite of environments, including simulated robots and Atari games, as well as a site for comparing and reproducing results.

It’s focused on reinforcement learning, a field of machine learning that involves decision-making and motor control.

According to OpenAI, reinforcement learning is an important aspect of building intelligent systems because it encompasses any problem that involves making a sequence of decisions. For instance, it could focus on controlling a robot’s motors so it’s able to run and jump, or enabling a system to make business decisions regarding pricing and inventory management.

Two major challenges for developers working with reinforcement learning are the lack of standard environments and the need for better benchmarks.

Musk’s group is hoping that the OpenAI Gym addresses both of those issues.

Source- http://www.thegurureview.net/aroundnet-category/elon-musk-opens-training-gym-for-ai-programmers.html

Google Says A.I. Is The Next Big Thing

May 3, 2016 by  
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Every decade or so, a new era of computing comes along that influences everything we do. Much of the 90s was about client-server and Windows PCs. By the aughts, the Web had taken over and every advertisement carried a URL. Then came the iPhone, and we’re in the midst of a decade defined by people tapping myopically into tiny screens.

So what comes next, when mobile gives way to something else? Mark Zuckerberg thinks it’s VR. There’s likely to be a lot of that, but there’s a more foundational technology that makes VR possible and permeates other areas besides.

“I do think in the long run we will evolve in computing from a mobile-first to an A.I.-first world,” said Sundar Pichai, Google’s CEO, answering an analyst’s question during parent company Alphabet’s quarterly earnings call Thursday.

He’s not predicting that mobile will go away, of course, but that the breakthroughs of tomorrow will come via smarter uses of data rather than clever uses of mobile devices like those that brought us Uber and Instagram.

Forms of artificial intelligence are already being used to sort photographs, fight spam and steer self-driving cars. The latest trend is in bots, which use A.I. services on the back end to complete tasks automatically, like ordering flowers or booking a hotel.

Google believes it has a lead in A.I. and the related field of machine learning, which Alphabet’s Eric Schmidt has already pegged as key to Google’s future.

Machine learning is one of the ways Google hopes to distinguish its emerging cloud computing business from those of rivals like Amazon and Microsoft, Pichai said.

Source-http://www.thegurureview.net/aroundnet-category/google-says-a-i-is-the-next-big-thing-in-computing.html

Is nVidia Going All-In On Autonomous Cars?

January 27, 2016 by  
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Nvidia is applying all that it knows about deep learning to enable autonomous vehicles.

The GPU vendor has launched NVIDIA DRIVE PX 2 which is an autonomous vehicle development platform powered by the 16nm FinFET-based Pascal GPU.

The GPU maker issued a version of DRIVE PX last year to its automotive partners including Audi, BMW, Daimler, Ford and dozens more. This newer  version is equipped with two Tegra SOCs with ARM cores plus two discrete Pascal GPUs.

Nvidia said that the new platform is capable of 24 trillion deep learning operations per second ten times more than the last generation.

It can also offer an aggregate of 8 teraflops of single-precision performance which is a four-fold increase over the PX 1 and many times faster than using a slide rule or counting on your fingers.

The development platform includes the Caffe deep learning framework to run DNN models designed and trained on DIGITS, NVIDIA’s interactive deep learning training system.

Nivida wants to take humans out of the drivers’ seat to reduce the one million automotive-related fatalities each year.

Perception is the main issue and deep learning is able to achieve super-human perception capability. DRIVE PX 2 can process 12 video cameras, plus lidar, radar and ultrasonic sensors. This 360 degree assessment makes it possible to detect objects, identify them and their position relative to the car, and then calculate a safe and comfortable trajectory.

Courtesy-Fud

Nvidia Teams Up With Volvo For Self-Driving Car Computer 

January 15, 2016 by  
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Nvidia Corp. took the wraps off of a new, lunchbox-size super-computer for self-driving cars and announced that Volvo Car Group will be the new device’s first customer.

Volvo, of Sweden, is owned by China’s Geely Automotive Holdings.

Nvidia made the announcement at the beginning of the Consumer Electronic Show in Las Vegas. Calls to Volvo’s spokesman in China were not immediately answered.

The new Drive PX 2, said company CEO Jen-Hsung Huang, has computing power equivalent to 150 MacBook Pro computers, and can deliver up to 24 trillion “deep learning” operations – allowing the computer to use artificial intelligence to program itself to recognize driving situations – per second.

Partnerships between automakers and Silicon Valley companies on self-driving technologies are taking center stage at this year’s show.

Also on Monday, General Motors Co. announced a $500 million investment in ride-sharing service Lyft.

Huang didn’t offer revenue projections for Drive PX 2, but automotive is the fastest-growing business segment for Nvidia, whose largest revenue source is video games.

Source-http://www.thegurureview.net/aroundnet-category/nvidia-teams-up-with-volvo-for-self-driving-car-computer.html

AI Assistant on The Way

December 15, 2015 by  
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Researchers at Carnegie Mellon University are working on artificial intelligence software that could one day become a personal assistant, whispering directions to get to a restaurant, put together a book shelf or repair a manufacturing machine.

The software is named Gabriel, after the angel that serves as God’s messenger, and is designed to be used in a wearable vision system – something similar to Google Glass or another head-mounted system. Tapping into information held in the cloud, the system is set up to feed or “whisper” information to the user as needed.

At this point, the project is focused on the software and is not connected to a particular hardware device.

“Ten years ago, people thought of this as science fiction,” said Mahadev Satyanarayanan, professor of computer science and the principal investigator for the Gabriel project, at Carnegie Mellon. “But now it’s on the verge of reality.”

The project, which has been funded by a $2.8 million grant from the National Science Foundation, has been in the works for the past five years.

“This will enable us to approach, with much higher confidence, tasks, such as putting a kit together,” said Satyanarayanan. “For example, assembling a furniture kit from IKEA can be complex and you may make mistakes. Our research makes it possible to create an app that is specific to this task and which guides you step-by-step and detects mistakes immediately.”

He called Gabriel a “huge leap in technology” that uses mobile computing, wireless networking, computer vision, human-computer interaction and artificial intelligence.

Satyanarayanan said he and his team are not in talks with device makers about getting the software in use, but he hopes it’s just a few years away from commercialization.

“The experience is much like a driver using a GPS navigation system,” Satyanarayanan said. “It gives you instructions when you need them, corrects you when you make a mistake and, most of the time, shuts up so it doesn’t bug you.”

One of the key technologies being used with the Gabriel project is called a “cloudlet.” Developed by Satyanarayanan, a cloudlet is a cloud-supported data center that serves multiple local mobile users.

Source- http://www.thegurureview.net/consumer-category/want-an-ai-based-whispering-personal-assistant.html

Google Upgrades Voice Search

October 8, 2015 by  
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Google said it has constructed a better neural network that is making its voice search work faster and better in noisy environments.

“We are happy to announce that our new acoustic models are now used for voice searches and commands in the Google app (on Android and iOS), and for dictation on Android devices,” Google’s Speech Team wrote in a recent  blog post . “In addition to requiring much lower computational resources, the new models are more accurate, robust to noise, and faster to respond to voice search queries.”

In 2013, Google brought the same voice recognition tools that had been working in Google Now to Google Search.

Along with being able to find information on the Internet, Google Voice Search also was able to find information for users in their Gmail, Google Calendar and Google+ accounts.

At the 2013 Google I/O developers conference, Amit Singhai, today a senior vice president and Google Fellow, said the future of search is in voice. For Google, he said, future searches will be more like conversations with your computer or device, which also will be able to give you information before you even ask for it.

The company went on to make it clear that it would continue to focus on voice search.

And this week’s announcement backs that up.

Google explained in its blog post that it has updated the neural network it’s using for voice search. A neural network is a computer system based on the way the human brain and nervous system work. It generally uses many processors operating in parallel.

The improved neural network is able to consume the incoming audio in larger chunks than conventional models without performing as many calculations.

“With this, we drastically reduced computations and made the recognizer much faster,” the team wrote. “We also added artificial noise and reverberation to the training data, making the recognizer more robust to ambient noise.”

Source-http://www.thegurureview.net/aroundnet-category/google-upgrades-voice-search.html

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