Skip to main content

3D News

Okay, we've gone over all the submissions for our first Winter Photo Contest and debated at length over our favorites. And, we've finally come to a consensus, which will introduce our second, second-time contest winner: ZZ3D.   First Prize: Snow Fight   ZZ3D's a long-time contributor to 3DVisionLive and has shared some amazing work with us. Snow Fight is certainly no exception! We felt this image captured the essence of the contest's Winter theme very well, and...
The votes have all been cast and we can now, finally, bring you the results of our First Annual Summer Photo Contest. Dozen's of excellent images were submitted and it was a challenge to whittle all the entries down and select the prize winners. Without further ado we get to the results - drumroll please!   First Prize: "Soap Bubble" Zoran Zelic (ZZ3D)'s "Soap Bubble 1" takes the top prize. We like the spontaneity the image implies along with the overall composition...
Sometimes it’s just great “when a plan comes together.” An avid warbird photographer, I’d been familiar with Christian Kieffer’s outstanding pinup photography for years – his company produces some truly amazing nostalgic calendars featuring vintage WWII aircraft and models done up to mimic the pinups from the same era that helped to keep many an airman’s spirits high. Thinking the subject matter would lend itself well to 3D, I approached Christian a few months ago about...
The 2004 release of id Software’s Doom 3 spurred many PC gamers to upgrade their rigs – with many building completely new machines with the sole intent of driving this game at its ultimate eye-candy settings. And many gamers still came up a bit short, which is just one reason why they are looking forward to jumping into the corridor-crawling fray again with the release of Doom 3 BFG Edition.Silverlight.createObject("http://d2q1944p6r21t1.cloudfront.net/files/...
We’ve rolled out a new look for the Photo page that updates the page to have a similar look and feel to the home and video pages. We’ve added a pane of larger thumbnails across the top that is user-navigable. Just click the right or left arrows to cycle. (We will be adding an auto-scroll mechanism to this soon.) And these are viewable in 3D - just click the 2D/3D toggle button at the top right of the page. Make sure to upgrade to the most recent drivers for best performance...

Recent Blog Entries

We’ll know AI really works when we hardly notice it at all, according to Bryan Catanzaro, a key figure in the field.

“AI gets better and better until it kind of disappears into the background,” says Catanzaro — NVIDIA’s head of applied deep learning research — in conversation with host Michael Copeland on this week’s edition of the new AI Podcast. “Once you stop noticing that it’s there because it works so well — that’s when it’s really landed.”

Bryan’s been in AI since the beginning. Or, as Michael says, as “about as long as it has really worked.” It’s a journey that’s taken him from UC Berkeley, where he earned his Ph.D., to NVIDIA, to Baidu — where he worked on a team that’s made a number of deep learning breakthroughs — and back to NVIDIA.

Along the way, he’s seen deep learning make incredible advances. It’s much further along than he would have predicted five years ago, he says. Image recognition has been one major success, with sophisticated facial recognition capabilities built into photo sharing services used by hundreds of millions of people every day.

“We’re at a point now where computers are actually better at recognizing objects in images than a person is,” Bryan says.

More’s coming, he explains. Deep learning — powered by ever more powerful GPUs — only grows more useful as the amount of data in the world grows.

“There’s a great number of problems that can be framed in this way, where you have a huge number of labeled examples, and you want a system to learn what that input means,” Bryan says.

To hear the whole conversation, tune into this week’s AI Podcast.

And if you missed our podcast last week, it’s definitely worth a listen: NVIDIA’s Will Ramey, a gifted explainer of all things deep learning, provides a clear explanation of the key concepts driving the field forward.

Finally, don’t miss next week’s podcast, where we talk about how you can use deep learning to accomplish some surprising do-it-yourself projects.

The post AI Podcast: Where Is Deep Learning Going Next? appeared first on The Official NVIDIA Blog.

A team from the Massachusetts General Hospital was among the researchers talking about how they’re using AI at GTC DC earlier this year.

The Mass General researchers joined colleagues from across the healthcare industry to help tell the story of how deep-learning – which is already used by hundreds of millions of people on smartphones – can improve health care.

Mass General became the first medical institute in the world — and among the first five research institutions of any kind — to receive an NVIDIA DGX-1. We delivered the supercomputer at Mass General’s historic Ether Dome, where the first public demonstration of surgery using anesthetic took place in 1846.

Mass General’s Clinical Data Science Center joins other early DGX-1 users, including the Open AI Institute, the Stanford Artificial Intelligence Laboratory, benevolent.ai, SAP and the Berkeley Artificial Intelligence Research Lab.

The center is already using GPUs to make significant medical advances. Researchers are testing an automated bone-age analyzer they’ve created that speeds diagnosis of children’s growth problems and is nearly as accurate as human radiologists (see “Deep Learning Speeds Diagnosis of Kids’ Growth Problems”).

More is coming. The Clinical Data Science Center is using AI and deep learning to advance healthcare, beginning with radiology, pathology and genetics. The center will research, test and implement new ways to improve the detection, diagnosis, treatment and management of diseases by training a deep neural network using Mass General’s vast stores of phenotypic, genetics and imaging data. The hospital has a database containing 10 billion medical images.

We delivered the supercomputer at Mass General’s historic Ether Dome, where the first public demonstration of surgery using anesthetic took place in 1846.

“The intent is to be able to explore the integration of man and machine at this point of clinical care, taking some of the data historically and using that data to actually create information in the machine so that we can see into the future what’s happening with patients before the human has the idea that there are changes taking place,” said Dr. Keith Dreyer, vice chairman and associate professor of radiology at Mass General and Harvard Medical School and executive director of the Mass General Clinical Data Science Center.

Radiology and Medical Imaging

DGX-1 also promises to help accelerate the adoption of AI in fields where machine learning techniques have already made a difference, such as radiology and medical imaging.

“The importance of machine learning and machine learning for radiology is unquestioned,” said Dr. James Brink, head of radiology at Mass General and chair of the American College of Radiologists. “I think there’s an enormous amount of opportunity for us to improve the efficiency of our work and the accuracy of our work through automation and semi-automation.”

Work with Patients

Longer term, deep learning also promises to help deliver better care for today’s patients by letting doctors better use the flood of medical research and patient data being produced by Mass General and other medical centers.

“I see deep learning and other machine learning techniques that could help us on a day-to-day basis make the process more efficient and in essence even more accurate,” said Dr. Long Li, assistant in pathology at Mass General and an assistant professor of pathology at Harvard Medical School.

Sounds like just what the doctor ordered.

Learn more about the DGX-1. Questions? Request a call.

The post Man, Machine and Medicine: Mass General Researchers Using AI appeared first on The Official NVIDIA Blog.