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Intel To Acquire Deep Learning Company Nervana
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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
IBM’s Watson Goes Cybersecurity
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
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
Is nVidia Going All-In On Autonomous Cars?
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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 admin
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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
Google Buys A.I. Firm
Google has purchased DeepMind Technologies, an artificial intelligence company in London, reportedly for $400 million.
A Google representative confirmed the via email, but said the company’s isn’t providing any additional information at this time.
News website Re/code said in a report this past Sunday that Google was paying $400 million for the company, founded by games prodigy and neuroscientist Demis Hassabis, Shane Legg and Mustafa Suleyman.
The company claims on its website that it combines “the best techniques from machine learning and systems neuroscience to build powerful general-purpose learning algorithms.” It said its first commercial applications are in simulations, e-commerce and games.
Google announced this month it was paying $3.2 billion in cash to acquire Nest, a maker of smart smoke alarms and thermostats, in what is seen as a bid to expand into the connected home market. It also acquired in January a security firm called Impermium, to boost its expertise in countering spam and abuse.
The Internet giant said on a research site that much of its work on language, speech, translation, and visual processing relies on machine learning and artificial intelligence. “In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, and we apply learning algorithms to generalize from that evidence to new cases of interest,” it said.
In May, Google launched a Quantum Artificial Intelligence Lab, hosted by NASA’s Ames Research Center. The Universities Space Research Association was to invite researchers around the world to share time on the quantum computer from D-Wave Systems, to study how quantum computing can advance machine learning.
Will Computer Obtain Common Sense?
Even though it may appear PCs are getting dumbed down as we see constant images of cats playing the piano or dogs playing in the snow, one computer is doing the same and getting smarter and smarter.
A computer cluster running the so-called the Never Ending Image Learner at Carnegie Mellon University runs 24 hours a day, 7 days a week searching the Internet for images, studying them on its own and building a visual database. The process, scientists say, is giving the computer an increasing amount of common sense.
“Images are the best way to learn visual properties,” said Abhinav Gupta, assistant research professor in Carnegie Mellon’s Robotics Institute. “Images also include a lot of common sense information about the world. People learn this by themselves and, with [this program], we hope that computers will do so as well.”
The computers have been running the program since late July, analyzing some three million images. The system has identified 1,500 types of objects in half a million images and 1,200 types of scenes in hundreds of thousands of images, according to the university.
The program has connected the dots to learn 2,500 associations from thousands of instances.
Thanks to advances in computer vision that enable software to identify and label objects found in images and recognize colors, materials and positioning, the Carnegie Mellon cluster is better understanding the visual world with each image it analyzes.
The program also is set up to enable a computer to make common sense associations, like buildings are vertical instead of lying on their sides, people eat food, and cars are found on roads. All the things that people take for granted, the computers now are learning without being told.
“People don’t always know how or what to teach computers,” said Abhinav Shrivastava, a robotics Ph.D. student at CMU and a lead researcher on the program. “But humans are good at telling computers when they are wrong.”
He noted, for instance, that a human might need to tell the computer that pink isn’t just the name of a singer but also is the name of a color.
While previous computer scientists have tried to “teach” computers about different real-world associations, compiling structured data for them, the job has always been far too vast to tackle successfully. CMU noted that Facebook alone has more than 200 billion images.
The only way for computers to scan enough images to understand the visual world is to let them do it on their own.
“What we have learned in the last five to 10 years of computer vision research is that the more data you have, the better computer vision becomes,” Gupta said.
CMU’s computer learning program is supported by Google and the Office of Naval Research.
Is Twitter Selling Your Tweets?
March 9, 2012 by admin
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Twitter users are about to become major marketing meat, as two research companies prepare to release information to clients who will pay for the rights to mine that data.
Boulder, Colorado-based Gnip Inc and DataSift Inc, based in the U.K. and San Francisco, are licensed by Twitter to analyze archived tweets and basic information about users, like geographic location. DataSift announced this week that it will release Twitter data in packages that will encompass the last two years of activity for its customers to mine, while Gnip can go back only 30 days.
“Harvesting what someone said a year or more ago is game-changing,” said Paul Stephens, director of policy and advocacy for the Privacy Rights Clearinghouse in San Diego. As details emerge on the kind of information being mined, he and other privacy rights experts are concerned about the implications of user information being released to businesses waiting to pore through it with a fine-tooth comb.
“As we see Twitter grow and social media evolve, this will become a bigger and bigger issue,” said Graham Cluley, senior technology consultant for British-based Internet security company Sophos Ltd. “Online companies know which websites we click on, which adverts catch our eye, and what we buy … increasingly, they’re also learning what we’re thinking. And that’s quite a spooky thought.”
Twitter opted not to comment on the sale and deferred questions to DataSift. In 2010, Twitter agreed to share all of its tweets with the U.S. Library of Congress. Details of how that information will be shared publicly are still in development, but there are some stated restrictions, including a six-month delay and a prohibition against using the information for commercial purposes.