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Sorting through the hundreds of 3D videos uploaded to 3DVisionLive.com during the past year to choose our favorite was just as challenging, if not more so, than the one posed by our Best Image of 2011 selection. We looked at all sorts of criteria, from page view metrics to our Highest Rated and Most Watched filters, and in the end it came to us choosing the video we felt was the most ambitious, compelling, and technically excellent, which also turned out to be the video...
Hello, and welcome to the new 3DVisionLive.com. It’s been an amazing first year for the site and we look forward to bringing you even more compelling reasons to visit in 2012! My name is Steve Klett; I’m a 3D fanatic, hard-core gamer, and photo enthusiast. It’s my job to manage the site on a daily basis and help to bring you compelling 3D news, images, and videos that you can experience with NVIDIA’s 3D Vision. To get things started we’ve...
In our opinion, there are far too few people out there taking 3D images and one major reason is the perceived difficulty barrier—taking two images and combining them for a stereo effect with special software or using custom twin digital SLR camera rigs is simply too complex and/or expensive for most of us mere mortals. Enter the 3D-capable point-and-shoot, the latest of which is Panasonic’s upcoming Lumix DMC-3D1. Similar to Fujifilm...

Recent Blog Entries

This April 22 put a little AI in your Earth Day.

Whether you’re cleaning up a beach, planting a tree or starting a garden, iNaturalist makes it easy to get in touch with nature.

iNaturalist is a crowd-sourced species identification app powered by AI. For the casual nature observer, the app allows people to snap photos of such easy targets as backyard plants and bugs and upload images for its AI to provide a match or for members of the community to identify.

The app is also a social network for nature enthusiasts to record information on species, meet others with similar interests and learn. It’s available on Android and iOS , where it’s already been downloaded nearly a million times.

It began as a website, iNaturalist.org, founded in 2008 by students at the University of California, Berkeley. Now it’s a joint program of the California Academy of Sciences and the National Geographic Society.

Previously it took 18 days on average for species to be identified by the website’s community. But that all changed after iNaturalist worked with researchers from Caltech and Cornell to build a computer vision AI into the app.

Now species can be identified in a matter of milliseconds, and much more accurately, with the use of AI, said Scott Loarie, co-director of iNaturalist.

“Our goal is to get millions of people outside exploring and connecting to nature and engage them to become lifelong stewards of the natural world,” Loarie said.

People mostly put up observations of plants to iNaturalist, but posts of birds, insects and other organisms can be found as well.

The app harnesses NVIDIA GPUs and the CUDA deep neural network library along with the TensorFlow deep learning framework, allowing training of the neural networks on a database of images that have been labeled by the site’s community of experts.

Today iNaturalist boasts 8.6 million observations uploaded and more than 155,000 species observed.

The post iNaturalist: An AI-Powered App to Crow About on Earth Day appeared first on The Official NVIDIA Blog.

Assessing risk is a concern in most industries, although perhaps never more so than following a merger or acquisition. It turns out AI may be just the tool to help.

GPU Technology Conference attendees last month got a high-level education in how AI can bring speed and precision to this process from Congruity360, a Massachusetts-based data management consultancy.

Mitigating Risk in M&A

Two people deciding to live together can see all of their belongings and make quick decisions about what to do with them. But a company that’s joined another is almost always saddled with data that’s not so easy to categorize.

While AI may not be well suited to analyzing text-based documents, Congruity360 has developed a method for parsing text data with GPU-powered machine learning.

“GPUs are not going to operate on text,” said Chris Ryan, vice president of sales engineering at Congruity360. “We need to come up with a mathematical representation of text documents.”

Doing so has allowed Congruity360 to classify unstructured documents based on whether they look the same or contain some of the same keywords. At its essence, the company’s work involves taking data it knows nothing about — “dark data,” as Ryan called it — and assigning high-level headers so it can separate the data into buckets related to topics such as invoices, taxes, intellectual property or even code.

The result is a visual representation that groups data in topical clusters, some of which stand on their own and some of which overlap. Companies can use this method to zero in on clusters of riskier documents, such as those that have regulatory implications.

Turning Data into Useful Information

Congruity360’s approach starts with the assumption that as much as 80 percent of all corporate data is unstructured, and seeks to answer the question, how can GPUs help machine learning turn raw text into information?

Most obviously, GPUs bring speed to the equation.

“If you’re a data scientist and you want to do this, you don’t want to wait weeks and weeks for models to run,” said Jonathan Bailey, vice president of analytics at Congruity360.

Speeding up the process translates to identifying — and mitigating — risks sooner. M&A activity involves working with legal teams, which are typically most concerned with ensuring that data is defensible. Congruity360 uses GPUs to perform comparisons of documents and compute their defensibility. It’s a process took four weeks using CPUs, and now unfolds in just 20 seconds on GPUs.

“We’re just trying to give users a tool to learn about data,” said Bailey. “We want to see if there’s any risky data in there.”

The post Risky Business: Tapping AI to Assess and Limit Risk in M&A appeared first on The Official NVIDIA Blog.