"Learning to Fly by Crashing," a paper from CMU roboticists Dhiraj Gandhi, Lerrel Pinto, and Abhinav Gupta, has such a nice abstract that I'll just let them explain what this research is all about: The reason most research avoids using large-scale real data is the fear of crashes! In this paper, we propose to bite the bullet and collect a dataset of crashes itself! We build a drone whose sole purpose is to crash into objects [. . We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation. One way to think of flying (or driving or walking or any other form of motion) is that success is simply a continual failure to crash.

WIRE)--H2O.ai today announced that it has collaborated with NVIDIA to offer its best-of-breed machine learning algorithms in a newly minted GPU edition. In addition, H2O's platform will be optimized for NVIDIA DGX-1 systems. Enterprises can use this end-to-end solution to operate on large datasets, iterate faster, deploy quickly and gain real-time insights. H2O's AI solutions enable customers to train machine learning models up to 75x faster compared to conventional CPU-based solutions. Potential use cases for this GPU integration include customer service, fraud prevention, financial advising and healthcare personalization.

The presence of just a few autonomous vehicles can eliminate the stop-and-go driving of the human drivers in traffic, along with the accident risk and fuel inefficiency it causes, according to new research. The finding indicates that self-driving cars and related technology may be even closer to revolutionizing traffic control than previously thought.

Larry Page's dream of using technology to fix cities may come to Canada first. Sidewalk Labs LLC, the urban innovation unit of Page's Alphabet Inc., has applied to develop a 12-acre strip in downtown Toronto, responding to a recent city agency request for proposals, according to two people familiar with the plans. Details of the proposal are private, but these people said the bid fits with the company's ambition to create a connected, high-tech city or district from scratch. Last year, the company began talking openly about building a theoretical urban zone "from the internet up," with some of the same tools and principles that have fueled success at many tech companies. Before applying in Toronto, Sidewalk Labs discussed creating a district in Denver and Detroit with Alphabet executives, according to the people. They asked not to be identified discussing private plans.

The two largest semiconductor companies in the world, Intel (NASDAQ:INTC) and Qualcomm (NASDAQ:QCOM), share a long rivalry, even though they specialize in and dominate different areas of the global processor market. So let's examine Qualcomm and Intel in three important areas to find which stock looks like the better buy today. Through the power of their business franchises, Intel and Qualcomm each earn a clean bill of financial health. The companies excel in these measures of financial fortitude in different regards. When it comes to net cash -- financial shorthand for cash and investments minus debt -- Qualcomm's $17.1 billion in net cash is miles ahead of Intel's $8.4 billion in net debt.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

I put together an in-depth tutorial to explain Transforms (Data Augmentation), the Dataset class, and the DataLoader class in Pytorch. I also show a ton of use cases for different transforms applied on Grayscale and Color images, along with Segmentation datasets where the same transform should be applied to both the input and target images. I show how to do Affine transforms (rotation, translation, shear, zoom), some awesome Image-based transforms (saturation, brightness, contrast, gamma, grayscale). These transforms can be applied with pre-determined settings or randomly sampled from a range of values. I also show some cool utility transforms like type casting, converting to tensors, and going from CHW to HWC.

Language translation is important to Facebook's mission of making the world more open and connected, enabling everyone to consume posts or videos in their preferred language -- all at the highest possible accuracy and speed. Today, the Facebook Artificial Intelligence Research (FAIR) team published research results using a novel convolutional neural network (CNN) approach for language translation that achieves state-of-the-art accuracy at nine times the speed of recurrent neural systems.1 Additionally, the FAIR sequence modeling toolkit (fairseq) source code and the trained systems are available under an open source license on GitHub so that other researchers can build custom models for translation, text summarization, and other tasks. Originally developed by Yann LeCun decades ago, CNNs have been very successful in several machine learning fields, such as image processing. However, recurrent neural networks (RNNs) are the incumbent technology for text applications and have been the top choice for language translation because of their high accuracy. Though RNNs have historically outperformed CNNs at language translation tasks, their design has an inherent limitation, which can be understood by looking at how they process information.

It is no secret that human driving is erratic, inconsistent and highly prone to accidents. Not just that, the stop and go style of driving in traffic causes accidents and mars fuel efficiency. Even a few self-driving cars on the road could change the way traffic works according to a new research. "Our experiments show that with as few as 5 percent of vehicles being automated and carefully controlled, we can eliminate stop-and-go waves caused by human driving behavior," said Daniel B. Work, assistant professor at the University of Illinois at Urbana-Champaign, a lead researcher in the study published Tuesday. According to the study, even if all cars on the road are not self-driven, just the presence of a few such cars will help in better traffic monitoring and control.

It's a common task for a data scientist: you need to generate segments (or clusters- I'll use the terms interchangably) of the customer base. With definitions, of course!!! Clustering is the subfield of unsupervised learning that aims to partition unlabelled datasets into consistent groups based on some shared unknown characteristics. All the tools you'll need are in Scikit-Learn, so I'll leave the code to a minimum. Instead, through the medium of GIFs, this tutorial will describe the most common techniques. If GIFs aren't your thing (what are you doing on the internet?), You can download this jupyter notebook here and the gifs can be downloaded from this folder (or you can just right click on the GIFs and select'Save image as…'). Clustering algorithms can be broadly split into two types, depending on whether the number of segments is explicitly specified by the user.