About NVIDIA NVIDIA (NASDAQ: NVDA) is a computer technology company that has pioneered GPU-accelerated computing. It targets the world's most demanding users -- gamers, designers and scientists -- with products, services and software that power amazing experiences in virtual reality, artificial intelligence, professional visualization and autonomous cars. Certain statements in this press release including, but not limited to, statements as to: the benefits and impact of the NVIDIA Inception Program; NVIDIA's commitment to help companies related to artificial intelligence; and funding through NVIDIA's GPU Ventures Program are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its Form 10-Q for the fiscal period ended May 1, 2016. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge.

This book provides a good start point for Deep learning in R. It starts by defining deep learning and introducing related packages in R. Specifically it talks about H2O package an interface to the H2O software a powerful library for deep learning and machine learning. Next it shows how we can apply neural network algorithm on particular problem with one chapter about different approaches to avoid of overfitting data. It continues with dealing with anomalous data and how unsupervised learning and auto-encoder model can be used to identify atypical data. In the other chapter it elaborates supervised learning, with focus on training and building feedforward neural networks to develop prediction models. The book is ended with two approaches for optimizing models i.e. dealing with missing data and choosing best hyper parameters value.

CES--Volvo Cars will use the NVIDIA DRIVE PX 2 deep learning- based computing engine to power a fleet of 100 Volvo XC90 SUVs starting to hit the road next year in the Swedish carmaker's Drive Me autonomous-car pilot program, NVIDIA announced today. Autonomous technology is an important contributor to Volvo's Vision 2020 -- its guiding principles for creating safer vehicles. This work has resulted in world-leading advancements in autonomous and semi-autonomous driving, and a new safety benchmark for the automotive industry. "Our vision is that no one should be killed or seriously injured in a new Volvo by the year 2020," said Marcus Rothoff, director of the Autonomous Driving Program at Volvo Cars. "NVIDIA's high-performance and responsive automotive platform is an important step towards our vision and perfect for our autonomous drive program and the Drive Me project."

IBM has given its investors a sign that Big Blue may finally be turning things around. Revenue increased for the first time in a key unit -- cognitive solutions, including its Watson artificial intelligence platform -- that the company has been touting as crucial to future growth. The results may signal that Chief Executive Officer Ginni Rometty is making good on her promise to shift IBM's software and services offerings to match customers' increasing appetite for cloud-based solutions. It's been an uphill battle. Overall, sales have declined for 17 quarters in a row, while margins have also narrowed.

For their study, researchers from New York University Shanghai and University of Hong Kong had 80 students and faculty from the University of Hong Kong participate in several experiments involving different video games. Action-based video games, for example, force the gamer to respond to visual cues. Think driving-centric games, like "Mario Kart," or first-person shooter games, such as "Unreal Tournament." Non-action games, on the other hand, include those like "Sims 2" and "Roller Coaster Tycoon," where the gamer is responsible for directing the action. In one experiment, subjects with no action-based video game experience were asked to played "Mario Kart" or a first-person shooter game.

Classification is an important statistical learning tool. In real application, besides high prediction accuracy, it is often desirable to estimate class conditional probabilities for new observations. For traditional problems where the number of observations is large, there exist many well developed approaches. Recently, high dimensional low sample size problems are becoming increasingly popular. Margin-based classifiers, such as logistic regression, are well established methods in the literature. On the other hand, in terms of probability estimation, it is known that for binary classifiers, the commonly used methods tend to under-estimate the norm of the classification function. This can lead to biased probability estimation. Remedy approaches have been proposed in the literature. However, for the simultaneous multicategory classification framework, much less work has been done. We fill the gap in this paper. In particular, we give theoretical insights on why heavy regularization terms are often needed in high dimensional applications, and how this can lead to bias in probability estimation. To overcome this difficulty, we propose a new refit strategy for multicategory angle-based classifiers. Our new method only adds a small computation cost to the problem, and is able to attain prediction accuracy that is as good as the regular margin-based classifiers. On the other hand, the improvement of probability estimation can be very significant. Numerical results suggest that the new refit approach is highly competitive.

We consider the problem of predicting to which classes an image belongs, where the number of classes is large (many thousands or tens of thousands) and where each image typically belongs to multiple classes that should all be properly identified: multi-label, massively multi-class classification. In such classification problems, the best practice until now (for instance in use at Google, Inc.) has been to use a deep convolutional neural network such as the ones described in [19] or [18], culminating in a logistic regression layer with a sigmoid cross-entropy loss, with target labels encoded as high-dimensional sparse binary vectors. The use of logistic regression implies an important yet oft overlooked assumption made about the label space: the classes are considered to be statistically independent, each class being treated as an independent dimension in the label space. This is generally not the case in practice: mirroring statistical dependencies found in the real world, label spaces often have a well-defined internal structure, with some labels being more likely to cooccur than other labels. For instance, "sky" and "beach" are frequently cooccurring labels, while "crane" and "manta ray" are rarely cooccurring. The sigmoid cross-entropy loss with sparse binary targets does not allow to leverage such observations about the structure of the label space. 1 There is therefore an opportunity to exploit the internal structure of the label space for gains in training speed, precision, and recall. One simple way to achieve this is to project the labels onto a lower-dimensional manifold -an embedding space-where a distance function between embedded labels would capture useful statistical dependencies. An appropriate loss function may then allow a parametric model trained via stochastic gradient descent to benefit from the structure of the manifold during training and inference.

As robots become an increasingly prominent asset in the workforce, jobs in food service and manufacturing may be among the first lost to automation. This is according to a recent McKinsey report, which analyzed thousands of workplace activities in the United States to determine which occupations are most at risk. The researchers say those which rely on'predictable physical activities', like food preparation, have a higher likelihood of automation, followed by data processing and data collection. As robots become an increasingly prominent asset in the workforce, jobs in food service and manufacturing may be among the first lost to automation. In the McKinsey report, the researchers analysed more than 2,000 work activities for over 800 occupations, looking at the amount of time spent on particular activities, and the feasibility of automating these tasks.

The purpose of this blog post is to demonstrate how to install the Keras library for deep learning. Let me start by saying that Keras is my favorite deep learning Python library. It's a minimalist, modular neural network library that can use either Theano or TensorFlow as a backend. Furthermore, the primary motivation behind Keras really resonates with me: you should be able to experiment super quickly -- going from idea to result, as fast as possible. Coming from a world that mixes both academia and entrepreneurship, the ability to iterate quickly is extremely valuable, especially in the deep learning world where it can take days to weeks to train just a single model.

There's no doubt Stephen Hawking is a smart guy. But the world-famous theoretical physicist recently declared that women leave him stumped. "Women should remain a mystery," Hawking wrote in response to a Reddit user's question about the realm of the unknown that intrigued him most. While Hawking's remark was meant to be light-hearted, he sounded quite serious discussing the potential dangers of artificial intelligence during Reddit's online Q&A session: A superintelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we're in trouble. Hawking's comments might seem unrelated.