September 28, 2017 -- Cirrascale Cloud Services, a premier provider of multi-GPU deep learning cloud solutions, today announced it will begin offering NVIDIA Tesla V100 GPU accelerators as part of its dedicated, multi-GPU deep learning cloud service offerings. The company plans to fully expand its current offerings to include the Tesla V100 within its PCIe-based multi-GPU cloud solution for the highest versatility for all workloads. Additionally, Cirrascale Cloud Services will offer NVIDIA NVLink high-speed interconnect technology on POWER9 and NVIDIA DGX solutions with Tesla V100 accelerators for the ultimate performance with deep learning frameworks. "Our customers are experts in artificial intelligence, deep learning and analytics and they demand the absolute highest performing tools available," said Mike LaPan, vice president of marketing, Cirrascale Cloud Services. "The NVIDIA Tesla V100 fast-tracks their AI initiatives by providing increased performance and versatility while enabling them to tackle challenges that were once impossible."

Artificial Intelligence has been described as "Thor's Hammer" and "the new electricity." But it's also a bit of a mystery – even to those who know it best. We'll connect with some of the world's leading experts in artificial intelligence, deep learning, and machine learning to explain how it works, how it's evolving, and how it intersects with every facet of human endeavor, from art to science. We release new episodes every week. A long-time journalist based in Silicon Valley, Michael has been in the thick of technological change since the web took hold.

If you enjoy art but most of your drawings resemble a child's doodles, then you'll be happy to hear that help is at hand – in the form of an artificial intelligence computer. Scientists have created a new system called'Vincent' that uses deep learning to transform sketches into'works of art.' Completed'works of art' combine a users' sketch with art since the renaissance, as if Van Gogh, Cézanne and Picasso were inside the machine. If you enjoy art but most of your drawings resemble a child's doodles, then you'll be happy to hear that help is at hand – in the form of an artificial intelligence computer. Vincent is the first system with the ability to interpret what a human is drawing, and then complete the piece for them. To design Vincent, the researchers showed the computer thousands of paintings from the Renaissance period to the current day, training the computer on contrast, colour and texture.

In this series of exploratory blog posts, we explore the relationship between recurrent neural networks (RNNs) and IoT data. The article is written by Ajit Jaokar, Dr Paul Katsande and Dr Vinay Mehendiratta as part of the Data Science for Internet of Things practitioners course. RNNs are already used for Time series analysis. Because IoT problems can often be modelled as a Time series, RNNs could apply to IoT data. In this multi-part blog, we first discuss Time series applications and then discuss how RNNs could apply to Time series applications.

Netflix Inc. isn't going to leave the success of its series and films to chance--and analysts say its stock should be rewarded. The company wants to be able to "combine great story telling and the great technological aspects," Chief Executive Reed Hastings told MarketWatch in 2015. "That's where we want to be." Netflix's use of convolutional neural network and proprietary algorithms, which is essentially deep machine learning used to analyze visual imagery, is a prime example of its approach. And it's just that approach that grabbed the attention of Wells Fargo analysts Ken Sena and Marci Ryvicker. They initiated coverage of Netflix NFLX, 1.44% on Wednesday with an overweight rating and a $230 12-month price target, which is the highest price target among analysts covering the stock, according to FactSet.

We will now take a closer look at each. There are other promises of deep learning for natural language processing; these were just the 5 that I chose to highlight. What do you think the promise of deep learning is for natural language processing? Let me know in the comments below. The first promise for deep learning in natural language processing is the ability to replace existing linear models with better performing models capable of learning and exploiting nonlinear relationships. Yoav Goldberg, in his primer on neural networks for NLP researchers, highlights both that deep learning methods are achieving impressive results. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results.

Imagine a future where complex decisions could be made faster and adapt over time. Where societal and industrial problems can be autonomously solved using learned experiences. It's a future where first responders using image-recognition applications can analyze streetlight camera images and quickly solve missing or abducted person reports. It's a future where stoplights automatically adjust their timing to sync with the flow of traffic, reducing gridlock and optimizing starts and stops. It's a future where robots are more autonomous and performance efficiency is dramatically increased.

Artificial intelligence is giving rise to unprecedented capabilities for surveillance, from facial recognition at bridge crossings to the ability to identify thousands of people at once. Now, new research suggests that AI could potentially be used to identify people who have taken steps to conceal their identities by wearing hats, sunglasses, or scarves over their faces. The paper, accepted to appear in a computer vision conference workshop next month and detailed in Jack Clark's ImportAI newsletter, shows that identifying people covering their faces is possible, but there's a long way to go before it's accurate enough to be relied upon. Researchers used a deep-learning algorithm--a flavor of artificial intelligence that detects patterns within massive amounts of data--to find specific points on a person's face and analyze the distance between those points. When asked to compare a face concealed by a hat or scarf against photos of five people, the algorithm was able to correctly identify the person 56% of the time.

A challenge in training discriminative models like neural networks is obtaining enough labeled training data. Recent approaches use generative models to combine weak supervision sources, like user-defined heuristics or knowledge bases, to label training data. Prior work has explored learning accuracies for these sources even without ground truth labels, but they assume that a single accuracy parameter is sufficient to model the behavior of these sources over the entire training set. In particular, they fail to model latent subsets in the training data in which the supervision sources perform differently than on average. We present Socratic learning, a paradigm that uses feedback from a corresponding discriminative model to automatically identify these subsets and augments the structure of the generative model accordingly. Experimentally, we show that without any ground truth labels, the augmented generative model reduces error by up to 56.06% for a relation extraction task compared to a state-of-the-art weak supervision technique that utilizes generative models.

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep $Q$-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.