NVIDIA DALI (Data Loading LIbrary) is an open source library researchers can use to accelerate data pipelines by 15% or more. By accelerating data augmentations using GPUs, NVIDIA DALI addresses performance bottlenecks in today's computer vision deep learning applications that include complex, multi-stage data augmentation steps. With DALI, deep learning researchers can scale training performance on image classification models such as ResNet-50 with MXNet, TensorFlow, and PyTorch across Amazon Web Services P3 8 GPU instances or DGX-1 systems with Volta GPUs. Framework users will have lesser code duplication due to consistent high-performance data loading and augmentation across frameworks. To demonstrate its power, NVIDIA data scientists used it to fine-tune DGX-2 to achieve a record-breaking 15,000 images per second in training.

I once asked a deep learning researcher what he'd like for Christmas. Nerd jokes aside, the lack of so-called "labeled" training data in deep learning is a real problem. Deep learning relies on millions upon millions of examples to tell the algorithm what to look for--cat faces, vocal patterns, or humanoid things strolling on the street. A deep learning algorithm is only as good as the data it's trained on--"garbage in, garbage out"--so accurately gathering and labeling existing data is essential. For the human researchers tasked with the job, carefully parsing the training data is a horrendously boring and time-consuming process.

Godfather of artificial intelligence Geoffrey Hinton gives an overview of the foundations of deep learning. In this talk, Hinton breaks down the advances of neural networks, as applied to speech and object recognition, image segmentation and reading or generating natural written language.

Early applications of deep learning to imagery and video focused primarily on cataloging that content, identifying the objects, activities, locations and text within. As neural algorithms have improved and new techniques developed, there has been an increasing focus on using deep learning approaches to actually generate completely new visual content autonomously. The cost of such AI generated synthetic content is dropping rapidly to the point that in the very near future you'll likely be able to generate it on your smartphone with a click of a button. These images and videos are a far cry from the ham-handed Photoshop jobs of yesteryear, with some appearing nearly flawless even to a trained eye. At the same time, we've taught society that "seeing is believing" and to put far more trust in visual material we see online – an image might be miscaptioned, but the picture itself is likely real.

Our own Chief Data Scientist Dr. Tim Oates recently give a talk at the Thug Think conference in Portland, Oregon. He focused in on reinforcement learning, a branch of machine learning with exciting applications. Whether you're familiar or brand new to the subject, his engaging presentation is going to be of interest to you. If you want to hear more of Tim's thoughts on reinforcement learning, read "Exploration vs. Exploitation"

Creating slow motion footage is all about capturing a large number of frames per second. If you don't record enough, then as soon as you slow down your video it becomes choppy and unwatchable. Unless, that is, you use artificial intelligence to imagine the extra frames. New research from chip designer Nvidia does exactly that, using deep learning to turn 30 frames-per-second video into gorgeous, 240 frames-per-second slow-motion. Essentially, the AI system looks at two different frames and then creates intermediary footage by tracking the movement of objects from one frame to the next. It's not the same as actually imagining footage like a human brain does, but it produces accurate (though not perfect) results.

The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

The base of the VECID deep-learning algorithms is the TensorFlow open source framework and library where the models are trained. The pre-trained networks can be used to classify the image data. EyeVision provides standard networks for applications such as Number Plate Reading (NPR), Optical Character Recognition and Make & Model identification. Much more powerful than the usual OCR tool alone, VECID deep learning is not susceptible to distortion, reflections or partial over- as well as underexposure. Through flexible hardware support with the EVOS (EyeVision Operating System), it can run on x86, embedded ARM, Windows and Linux platforms.

Not a week goes by that I don't see dozens of headlines blaming "AI" or "deep learning" for yet another ethical conundrum that will doom human society. Whether it is predictive policing or facial recognition or autonomous weapons, it seems nearly every facet of society is facing an "AI revolution" that will destroy humankind. If one peels back the breathless hype and viral buzzwords, however, is it really AI that we are worried about or is it the shift towards a data-centric society with or without deep learning advances? To the general public, "big data" and "deep learning" are increasingly becoming synonymous, fueled by the never-ending hype machine of Silicon Valley and the very legitimate advances occurring in deep learning powered largely by the massive availability of large datasets and the unique abilities of those models to make sense of all that data. From a technical standpoint, however, these are two entirely distinct concepts.

No industry counts more than healthcare and medicine. And I don't think I should explain'why'. So whatever advancements we may have done in any industry till today with Artificial Intelligence or with any other cutting edge technologies for that matter, our healthcare and medicine should come top in that list and there should not be any disagreement on this. Let's do a reality check and see how far Artificial Intelligence has already been leveraged in our healthcare and medicine. When you go to your doctor today, your doctor can't guarantee that a particular treatment selected for you is going to work for sure.