Groundbreaking research has always been an important aspect of SIGGRAPH, as scientists and researchers present the latest industry advancements to conference-goers. So, the fact that Nvidia, in collaboration with top academic researchers at 14 universities, will be presenting a record number (16) of research papers at this year's conference is astounding. When a reinforcement learning model is used to develop a physics-based animated character, the AI typically learns just one skill at a time: walking, running, or perhaps cartwheeling. But researchers from UC Berkeley, the University of Toronto, and Nvidia have created a framework that enables AI to learn a whole repertoire of skills--demonstrated with a warrior character who can wield a sword, use a shield, and get back up after a fall. Achieving these smooth, lifelike motions for animated characters is usually tedious and labor-intensive, with developers starting from scratch to train the AI for each new task.

Would you like to turn your child's drawings into real? It'd be the best gift to give your child their own work of art. If you'd like to do it, then here's a great invention for you. NVIDIA researchers invented Artificial Intelligence, which is called DIB-R, that can turn 2D images into 3D models. The machine can predict what would the 2D image look like in three dimensions and create a 3D model, by taking lighting, texture, and depth into the consideration. SEE ALSO: NVIDIA LETS YOU RECREATE YOUR DOG'S SMILE ON ANOTHER ANIMAL WITH NEW APP The model will be presented by the NVIDIA researchers at the annual Conference on Neural Information Processing Sytems (NeurIPS), Vancouver.

Every March US chip giant NVIDIA hosts its GPU Technology Conference in Santa Clara, rolling out new chip designs and products while showcasing its latest tech and AI breakthroughs. GTC 2019 runs next Monday through Thursday (March 18–21), and while we can only speculate what surprises NVIDIA CEO Jensen Huang might have in store for us, we can get some sense of where the company is headed by looking at what it's been up to for the last 12 months. NVIDIA is steadily expanding its foothold in artificial intelligence and in 2018 contributed numerous noteworthy research results to the machine learning community, including StyleGAN, video-to-video translation, WaveGlow, and more. The company's AI-related efforts make sense, as NVIDIA is a major supplier of graphics cards to AI researchers and developers. Because GPUs remain the dominant option for training machine learning models, NVIDIA sales will obviously benefit from AI development and deployment.

Compared to other fields, machine learning / artificial intelligence seems to have a much higher frequency of super-interesting developments these days. Things that make you say "wow" or even "what a time to be alive!" Disclaimer: I'm not using any rigorous definition of "mind-blowing" or "breakthrough"; it's a casual list.. and I might use less rigorous terminology to make this post more accessible We can accurately estimate how a human on the other side of a wall is standing/sitting/walking just from perturbations in Wifi signals caused by that human. The researchers first demonstrated in 2014 that they can e.g. This part was done without machine learning.

A new website lets users click through an endless library of fake human faces created by artificial intelligence. Called ThisPersonDoesNotExist.com, the results are startlingly lifelike and may make you question what's real and what isn't. Every time a user refreshes their browser, the site spits out a randomly generated face, spanning from older men and women to even children. At first glance, these photos appear to be of random people. However, a website called ThisPersonDoesNotExist.com uses AI to generate startlingly realistic copies of human faces ThisPersonDoesNotExist.com was created by a software engineer at Uber.

The faces above don't seem particularly remarkable. They could easily be taken from, say, Facebook or LinkedIn. In reality, they were dreamed up by a new kind of AI algorithm. Nvidia researchers posted details of the method for producing completely imaginary fake faces with stunning, almost eerie, realism (here's the paper). The researchers, Tero Karras, Samuli Laine, and Timo Aila, came up with a new way of constructing a generative adversarial network, or GAN.

Check out these rather ordinary looking portraits. Not in the sense that they were Photoshopped, but rather they were completely generated by artificial intelligence. That's right: none of these people actually exist. NVIDIA researchers have published a new paper on easily customizing the style of realistic faces created by a generative adversarial network (GAN). The Verge points out that GAN has only existed for about four years.

Artificial intelligence and machine learning have come a long way and have become buzzwords in many tech products today. Impressive as they are, however, their methods of learning are still mostly, well, artificial. NVIDIA researchers are developing a new way to train AI for industrial robots that almost closely mimics the way we ourselves learn. And that's by watching how another, more experienced human perform a task and then trying to repeat it. Most neural networks are trained using what almost seems like brute force, except that it takes hours rather than years.

The woman in the photo seems familiar. She looks like Jennifer Aniston, the "Friends" actress, or Selena Gomez, the child star turned pop singer. But not exactly. She appears to be a celebrity, one of the beautiful people photographed outside a movie premiere or an awards show. And yet, you cannot...

The woman in the photo seems familiar. She looks like Jennifer Aniston, the "Friends" actress, or Selena Gomez, the child star turned pop singer. She appears to be a celebrity, one of the beautiful people photographed outside a movie premiere or an awards show. That's because she's not real. She was created by a machine.