With global climate change, devastating hurricanes are occurring with higher frequency. After a hurricane, roads are often flooded or washed out, making them treacherous for motorists. According to The Weather Channel, almost two of every three U.S. flash flood deaths from 1995–2010, excluding fatalities from Hurricane Katrina, occurred in vehicles. During my Insight A.I. Fellowship, I designed a system that detects flooded roads and created an interactive map app. Using state of the art computer vision deep learning methods, the system automatically annotates flooded, washed out, or otherwise severely damaged roads from satellite imagery.

Facial recognition using deep learning technology is being introduced by Panasonic. The new high-precision face recognition software can identify faces including at an angle, those partially hidden by sunglasses and those that are difficult to recognize with conventional technologies, according to Panasonic. The software features the iA (intelligent auto) mode, which automatically adjusts to shoot optimal images for face recognition and the best shots are then sent to the server for recognition. With conventional facial recognition systems, all the images are sent to the server, where the recognition occurs. "Using this software with cameras equipped with the iA function enables image analysis to be performed on the camera instead of the server to send only the best images to the server," states the announcement by Panasonic.

The annual business awards program organized by the Business Intelligence Group (USA) recognizes organizations, products and people that bring new ideas to life. RTB House was recognized for leading innovation by employing 100% deep learning technology within 100% of its algorithms. Deep learning technology is a rapidly trending subfield of AI which mimics human neural networks to process data and make decisions at lightning speed across countless options. RTB House was awarded the 2018 Big Innovation Award by the Business Intelligence Group for adopting deep learning within every single algorithm, resulting in higher returns on investment for its advertisers. RTB House implemented self-learning algorithms to four different areas of its core technology stack.

Date: June 13-14, 2018 Venue: Sonesta Hotel Philadelphia 1800 Market Street Philadelphia, PA 19103 Malaikannan Sankarasubbu is speaking. The post AI Innovation for Life Science and Healthcare Summit- June 13-14 appeared first on Saama.

This week on the podcast we're running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you'll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. My guest for this first show in the series is, Hillery Hunter, IBM Fellow & Director of the Accelerated Cognitive Infrastructure group at IBM's T.J. Watson Research Center. Hillery and I met a few weeks back in New York and I'm really glad that we were able to get her on the show. Hillery joins us to discuss her team's research into distributed deep learning, which was recently released as the PowerAI Distributed Deep Learning Communication Library or DDL.

"The hunter became the hunted," that is what artificial intelligence has currently done to poachers. As you've seen in the news, and all over the internet, poaching has grown into a global quandary. Poachers are killing thousands upon thousands of elephants, rhinos, and other animals and it's clear that previous efforts have not been able to stop that. But now, artificial intelligence has come to the rescue and believe it or not, the stubborn poachers have now turned into prey! Researchers from the University of Southern California Center tackling AI in Society are using deep learning to catch hunters in near real time –just before they strike.

Over the last few years Deep Learning was applied to hundreds of problems, ranging from computer vision to natural language processing. In many cases Deep Learning outperformed previous work. Deep Learning is heavily used in both academia to study intelligence and in the industry in building intelligent systems to assist humans in various tasks. The goal of this post is to share amazing applications of Deep Learning that I've seen. I hope this will excite people about the opportunities this field brings, as well as remind us that every new technology carries with it potential dangers. I believe the latter is especially true about Deep Learning, and I hope that by exposing people to all these amazing results I can encourage more discussion on the topic. There are many different applications and this list below is in no way exhaustive. So if you know of other cool applications I would appreciate it if you can mention them in the comments.

From cut-throat competition, eyebrow-raising co-opetition, and major advances in cloud-based machine learning, 2017 was a pivotal - and productive - year for the cloud, setting the stage for what looks likely to be the most exciting year yet. The market swing is already in full force. Thanks to a full-fledged embrace by the enterprise, the cloud is undergoing dramatic transformation as vendors rush to meet the infrastructure and business needs of today's top companies. According to Gartner, the overall market likely grew by close to 20 percent in 2017, and IaaS in particular saw close to 40 percent growth. With digital transformation at the top of every executive's mind, it's likely that this trend will only accelerate.

Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications. In order to obtain performance gains, these networks have grown larger and deeper, containing millions or even billions of parameters and over a thousand layers. The trade-off is that these large architectures require an enormous amount of memory, storage, and computation, thus limiting their usability. Inspired by the recent tensor ring factorization, we introduce Tensor Ring Networks (TR-Nets), which significantly compress both the fully connected layers and the convolutional layers of deep neural networks. Our results show that our TR-Nets approach {is able to compress LeNet-5 by $11\times$ without losing accuracy}, and can compress the state-of-the-art Wide ResNet by $243\times$ with only 2.3\% degradation in {Cifar10 image classification}. Overall, this compression scheme shows promise in scientific computing and deep learning, especially for emerging resource-constrained devices such as smartphones, wearables, and IoT devices.

Residual Networks (ResNets) have become state-of-the-art models in deep learning and several theoretical studies have been devoted to understanding why ResNet works so well. One attractive viewpoint on ResNet is that it is optimizing the risk in a functional space by combining an ensemble of effective features. In this paper, we adopt this viewpoint to construct a new gradient boosting method, which is known to be very powerful in data analysis. To do so, we formalize the gradient boosting perspective of ResNet mathematically using the notion of functional gradients and propose a new method called ResFGB for classification tasks by leveraging ResNet perception. Two types of generalization guarantees are provided from the optimization perspective: one is the margin bound and the other is the expected risk bound by the sample-splitting technique. Experimental results show superior performance of the proposed method over state-of-the-art methods such as LightGBM.