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If you’ve a penchant for liking superhero-themed anything and playing games in 3D, the Batman: Arkham series has been a match made in heaven. Simply put, when it comes to 3D Vision titles it just doesn’t get much better – and it’s hard to see how it could.We’re happy to report that Batman: Arkham Origins, which releases today, continues this tradition. Out of the box, Origins is rated 3D Vision Ready, so you know it’s going to look spectacular. We’ve played it quite a bit...
Contest closed - stay tuned to for details about upcoming contests. is excited to unveil the latest in a series of photo contests aimed at giving you a platform to show off your images and potentially win some cool prizes. Like our most recent Spring Contest, this one will span three months - October, November, and December - and is themed: Your image must be something that captures or shows the essence of "nature" and what...
With sincere apologies for the delay, NVIDIA is pleased to announce the results of the Spring Photo Contest. We received more than 80 submissions from 3DVisionLive members and, for the first time, invited the membership to select the winner. The only criteria for the contest was the photos had to represent the meaning of Spring in some fashion, and be an original image created by the member that submitted it. All submitted photos were put in a gallery and ample time was...
For the third year in a row, NVIDIA worked with the National Stereoscopic Association to sponsor a 3D digital image competition called the Digital Image Showcase, which is shown at the NSA convention - held this past June in Michigan. This year, the 3D Digital Image Showcase competition consisted of 294 images, submitted by 50 different makers. Entrants spanned the range from casual snapshooters to both commercial and fine art photographers. The competition was judged by...
  VOTING IS NOW CLOSED - Thanks to all that participated. Results coming soon!   The submission period for the Spring Photo Contest is now closed, and we are happy to report we’ve received 80 images from our members for consideration. And, for the first time, we’re opening the judging process to our community as well to help us determine the winners. So, between now and the end of June (11:59 PST, June 30st), please view all of the images in the gallery and place...

Recent Blog Entries

This is the first of a multi-part series explaining the fundamentals of deep learning by long-time tech journalist Michael Copeland.

Artificial intelligence is the future. Artificial intelligence is science fiction. Artificial intelligence is already part of our everyday lives. All those statements are true, it just depends on what flavor of AI you are referring to.

For example, when Google DeepMind’s AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go earlier this year, the terms AI, machine learning, and deep learning were used in the media to describe how DeepMind won. And all three are part of the reason why AlphaGo trounced Lee Se-Dol. But they are not the same things.

The easiest way to think of their relationship is to visualize them as concentric circles with AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion —  fitting inside both.

From Bust to Boom

AI has been part of our imaginations and simmering in research labs since a handful of computer scientists rallied around the term at the Dartmouth Conferences in 1956 and birthed the field of AI. In the decades since, AI has alternately been heralded as the key to our civilization’s brightest future, and tossed on technology’s trash heap as a harebrained notion of over-reaching propellerheads. Frankly, until 2012, it was a bit of both.

Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Let’s walk through how computer scientists have moved from something of a bust — until 2012 — to a boom that has unleashed applications used by hundreds of millions of people every day.

Artificial Intelligence   Human Intelligence Exhibited by Machines King me: computer programs that played checkers were among the earliest examples of artificial intelligence, stirring an early wave of excitement in the 1950s.

Back in that summer of ’56 conference the dream of those AI pioneers was to construct complex machines — enabled by emerging computers — that possessed the same characteristics of human intelligence. This is the concept we think of as “General AI” —  fabulous machines that have all our senses (maybe even more), all our reason, and think just like we do. You’ve seen these machines endlessly in movies as friend —  C-3PO —  and foe —  The Terminator. General AI machines have remained in the movies and science fiction novels for good reason; we can’t pull it off, at least not yet.

What we can do falls into the concept of “Narrow AI.” Technologies that are able to perform specific tasks as well as, or better than, we humans can. Examples of narrow AI are things such as image classification on a service like Pinterest and face recognition on Facebook.

Those are examples of Narrow AI in practice. These technologies exhibit some facets of human intelligence. But how? Where does that intelligence come from? That get us to the next circle, Machine Learning.

Machine Learning  An Approach to Achieve Artificial Intelligence Spam free diet: machine learning helps keep your inbox (relatively) free of spam.

Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task.

Machine learning came directly from minds of the early AI crowd, and the algorithmic approaches over the years included decision tree learning, inductive logic programming. clustering, reinforcement learning, and Bayesian networks among others. As we know, none achieved the ultimate goal of General AI, and even Narrow AI was mostly out of reach with early machine learning approaches.

As it turned out, one of the very best application areas for machine learning for many years was computer vision, though it still required a great deal of hand-coding to get the job done. People would go in and write hand-coded classifiers like edge detection filters so the program could identify where an object started and stopped; shape detection to determine if it had eight sides; a classifier to recognize the letters “S-T-O-P.” From all those hand-coded classifiers they would develop algorithms to make sense of the image and “learn” to determine whether it was a stop sign.

Good, but not mind-bendingly great. Especially on a foggy day when the sign isn’t perfectly visible, or a tree obscures part of it. There’s a reason computer vision and image detection didn’t come close to rivaling humans until very recently, it was too brittle and too prone to error.

Time, and the right learning algorithms made all the difference.

Deep Learning A Technique for Implementing Machine Learning Herding cats: Picking images of cats out of YouTube videos was one of the first breakthrough demonstrations of deep learning.

Another algorithmic approach from the early machine-learning crowd, Artificial Neural Networks, came and mostly went over the decades. Neural Networks are inspired by our understanding of the biology of our brains – all those interconnections between the neurons. But, unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation.

You might, for example, take an image, chop it up into a bunch of tiles that are inputted into the first layer of the neural network. In the first layer individual neurons, then passes the data to a second layer. The second layer of neurons does its task, and so on, until the final layer and the final output is produced.

Each neuron assigns a weighting to its input —  how correct or incorrect it is relative to the task being performed.  The final output is then determined by the total of those weightings. So think of our stop sign example. Attributes of a stop sign image are chopped up and “examined” by the neurons —  its octogonal shape, its fire-engine red color, its distinctive letters, its traffic-sign size, and its motion or lack thereof. The neural network’s task is to conclude whether this is a stop sign or not. It comes up with a “probability vector,” really a highly educated guess,  based on the weighting. In our example the system might be 86% confident the image is a stop sign, 7% confident it’s a speed limit sign, and 5% it’s a kite stuck in a tree ,and so on — and the network architecture then tells the neural network whether it is right or not.

Even this example is getting ahead of itself, because until recently neural networks were all but shunned by the AI research community. They had been around since the earliest days of AI, and had produced very little in the way of “intelligence.” The problem was even the most basic neural networks were very computationally intensive, it just wasn’t a practical approach. Still, a small heretical research group led by Geoffrey Hinton at the University of Toronto kept at it, finally parallelizing the algorithms for supercomputers to run and proving the concept, but it wasn’t until GPUs were deployed in the effort that the promise was realized.

If we go back again to our stop sign example, chances are very good that as the network is getting tuned or “trained” it’s coming up with wrong answers —  a lot. What it needs is training. It needs to see hundreds of thousands, even millions of images, until the weightings of the neuron inputs are tuned so precisely that it gets the answer right practically every time — fog or no fog, sun or rain. It’s at that point that the neural network has taught itself what a stop sign looks like; or your mother’s face in the case of Facebook; or a cat, which is what Andrew Ng did in 2012 at Google.

Ng’s breakthrough was to take these neural networks, and essentially make them huge, increase the layers and the neurons, and then run massive amounts of data through the system to train it. In Ng’s case it was images from 10 million YouTube videos. Ng put the “deep” in deep learning, which describes all the layers in these neural networks.

Today, image recognition by machines trained via deep learning in some scenarios is better than humans, and that ranges from cats to identifying indicators for cancer in blood and tumors in MRI scans. Google’s AlphaGo learned the game, and trained for its Go match —  it tuned its neural network —  by playing against itself over and over and over.

Thanks to Deep Learning, AI Has a Bright Future

Deep Learning has enabled many practical applications of Machine Learning and by extension the overall field of AI. Deep Learning breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. Driverless cars, better preventive healthcare, even better movie recommendations, are all here today or on the horizon. AI is the present and the future. With Deep Learning’s help, AI may even get to that science fiction state we’ve so long imagined. You have a C-3PO, I’ll take it. You can keep your Terminator.


The post What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? appeared first on The Official NVIDIA Blog.

To better battle cancer, we need data. Lots of it.

With cancer so prevalent, data is abundant. There’s everything from medical records stuffed with the pathology reports of millions of cancer patients to newspaper archives filled with the obituaries of cancer victims.

All this information effectively creates a dispersed database that can be used to determine ties between demographics and population cancer outcomes. But it takes a lot of time to analyze so much unstructured text data. That’s why the Surveillance, Epidemiology, and End Results (SEER) Program of the U.S. National Cancer Institute typically reports its annual cancer statistics with a five-year delay.

To speed things up, researchers at the Oak Ridge National Laboratory’s Health Data Sciences Institute have combined GPUs, deep learning algorithms, and data analytics and extraction technologies with ORNL’s Titan supercomputer.

“The goal is to be able to tell as a nation if we’re making progress” in battling cancer, said Georgia Tourassi, director of the Health Data Sciences Institute.

Deep Learning Speeds Side-by-Side Projects

Tourassi’s team is tackling both pathology reports and obituaries in two separate projects intended to provide new insights into the patterns of cancer. The obituary project, now in its fourth year, has been fully funded by an NCI grant. Researchers have been working on developing analytical tools that can perform automated research and thus be leveraged to perform more comprehensive epidemiological studies.

In the latter stages of the project, Tourassi’s team has been using a practice known as data parallelism. In this technique, data is divided among separate computing nodes on Titan, allowing the same process to be applied to different data segments simultaneously. This is speeding up efforts to establish a deep learning network that will improve the data analysis and extraction efforts.

In the meantime, Tourassi’s team has been asked to use a similar approach to analyze millions of cancer pathology reports. While not as far along as the obituary work, this project figures to benefit more from the deep learning training, which has been a recent addition to the research.

“Our results show incremental improvements from deep learning compared with traditional rules-based systems,” said Tourassi. “It is very promising, and we will continue working on it.”

The Challenge of ‘Big-but-Dirty’ Data

Much of traditional text mining systems and early deep learning systems rely on experts, who use their knowledge to guide the system’s learning by deciphering clinical text for it. Eventually, deep learning systems will be able to crunch clinical pathology reports and learn without assistance, resulting in an automated and dynamic way of sifting through “big-but-dirty data,” the name Tourassi has given to data for which there’s no way to control the quality.

In both projects, NVIDIA Tesla K20 GPU accelerators are being used to accelerate the deep learning training on Titan. Tourassi reports that the process has been unfolding eight to 10 times faster on GPUs that it did on CPUs for the obituary project. The pathology report project is too fresh to have generated concrete data, but Tourassi sees early indications of similar gains.

“Having seen the clinical performance boost in both applications, I’m a believer” in GPUs, she said. “I now understand the value of scaling these tools for use on the supercomputer.”

And while the goals of both projects are clear, Tourassi hopes to push the efforts, as any good researcher should, so that cancer research findings can be reported in as close to real time as possible.

“We would like to develop the informatics tools and give them to the different registries so they can accelerate information extraction,” she said. “We hope to modernize the cancer surveillance program.”

The post Researchers Speeding Up Reporting of Cancer Data With Help From Deep Learning, GPUs appeared first on The Official NVIDIA Blog.