Close your left eye as you look at this screen. Now close your right eye and open your left -- you'll notice that your field of vision shifts depending on which eye you're using. That's because while we see in two dimensions, the images captured by your retinas are combined to provide depth and produce a sense of three-dimensionality. Machine learning models need this same capability so that they can accurately understand image data. NVIDIA researchers have now made this possible by creating a rendering framework called DIB-R -- a differentiable interpolation-based renderer -- that produces 3D objects from 2D images. The researchers will present their model this week at the annual Conference on Neural Information Processing Systems (NeurIPS), in Vancouver.

When Megan Gray, CEO of Moment AI, first tested one of her company's services -- a tool using AI to determine facial signs indicating a driver may have fallen asleep or suffered a medical issue -- it didn't work. "The technology worked on our CTO, who is a white male. But then I tried it, and it couldn't detect that my eyes were closed," Gray said. This is just one example of how a lack of diversity in the field of AI affects the technologies that are created. At GTC DC, this week's Washington edition of the GPU Technology Conference, a range of events focused on sharing ideas on how workplaces can become more inclusive, and how researchers can improve their AI technology to avoid bias.

AI is the greatest IT disruption of our time, promising to transform society and industry. I've never seen a technology with as much potential to boost the security, health and prosperity of our country. It's estimated to make an economic impact measured in trillions of dollars. The U.S. federal government has been moving quickly, especially this past year, to help advance our nation's adoption of this transformative technology. From the White House to agency leaders and department heads in dozens of federal organizations, the government is acutely aware of the competitive international environment, with more than 35 countries that have already announced AI strategies.

From enhancing in-store customer experiences to streamlining back-end supply chain operations, AI is revolutionizing the retail industry. Attendees at this year's GPU Technology Conference in Silicon Valley can learn how AI improves loss prevention and powers shopper tracking and autonomous checkout for more personalized customer experience and cost-effective business. Here are six can't-miss retail-focused sessions at GTC to attend: How Walmart Improves Forecast Accuracy with NVIDIA GPUs -- John Bowman, director of data science at Walmart Labs, shows how GPU computing has enabled the retailing giant to significantly improve forecast accuracy while remaining within execution time windows -- using RAPIDS open source data science and machine learning libraries. Semantic Understanding for E-Commerce Search -- Learn how deep learning-fueled semantic understanding is helping solve the problem of e-commerce search, with Somnath Banerjee, director of machine learning at Walmart Labs. What Every Industry Can Learn About AI from Retail -- A panel of experts from GOAT, Focal Systems, Fellow Robots and NVIDIA outline the success criteria that any industry can apply to its strategy -- from supply chain optimization to better customer experience.

Artificial intelligence is years, even decades, from replicating functions of the human mind, but it's still getting serious work done today. And its influence will only expand. The irony of all that promise: Human minds are way behind. Relatively few have a baseline understanding about how AI and deep learning truly work. Techniques like machine learning, which underpin many of today's AI tools, aren't easy to grasp.

Jacques Mattheij didn't expect to buy two tons of Lego bricks. But that's what happened after an evening of bidding -- or rather, overbidding -- on bulk lots of used bricks on eBay. His plan was to resell the bricks at a profit. But he won more than expected, and by morning, he owned more than 2 million pieces. Now he needed to sort them to get the best price.

New York University's Center for Data Science is at the cutting edge of fields with revolutionary implications such as machine learning, natural language processing, computer vision and intelligent machines. Because computing speed is critical to accelerating experimentation and advancing research, the center's Computational Intelligence, Learning, Vision and Robotics (CILVR) lab recently acquired a NVIDIA DGX-1 AI supercomputer to fuel this work like never before. The CILVR lab has "unsupervised learning" as its focus. The lab's faculty, research scientists and graduate students are developing techniques that allow machines to learn from raw, unlabeled data by, for example, observing video, looking at images or listening to speech. These techniques are then applied to computer vision applications like self-driving cars that can understand the environment around them, medical image analysis that can detect tumors or disease earlier and more accurately than traditional methods, and natural language processing that can translate languages, answer questions or hold a dialogue with people. "The DGX-1 is going to be used in just about every research project we have here," said Yann LeCun, founding director of the NYU Center for Data Science and a pioneer in the field of AI. "The students here can't wait to get their hands on it."

New York University's Center for Data Science is at the cutting edge of fields with revolutionary implications such as machine learning, natural language processing, computer vision and intelligent machines. Because computing speed is critical to accelerating experimentation and advancing research, the center's Computational Intelligence, Learning, Vision and Robotics (CILVR) lab recently acquired a NVIDIA DGX-1 AI supercomputer to fuel this work like never before. The CILVR lab has "unsupervised learning" as its focus. The lab's faculty, research scientists and graduate students are developing techniques that allow machines to learn from raw, unlabeled data by, for example, observing video, looking at images or listening to speech. These techniques are then applied to computer vision applications like self-driving cars that can understand the environment around them, medical image analysis that can detect tumors or disease earlier and more accurately than traditional methods, and natural language processing that can translate languages, answer questions or hold a dialogue with people. "The DGX-1 is going to be used in just about every research project we have here," said Yann LeCun, founding director of the NYU Center for Data Science and a pioneer in the field of AI. "The students here can't wait to get their hands on it."

In turn, each layer of abstraction lets a larger group of developers build more proficient programs in less time. AI is at the level of the assembly language currently. Toolkits like TensorFlow are phenomenally helpful for data scientists previously used to working at the equivalent of the machine code level, but there are only about 19,000 data scientists worldwide. Bonsai's AI Engine works at a higher level of abstraction so millions of developers, and the companies that employ them, can more efficiently build AI into applications and systems. Imagine when the developers at GE, the U.S. Department of Education or the Red Cross are able to program intelligent applications as quickly and collaboratively as they might program a database. Scaled with AI technology, the unique expertise and data locked within these organizations stand to create monumental change.