Here is the May 2018 issue of Computer Vision News, published by RSIP Vision: 52 pages about Computer Vision, Biomedical Imaging, Deep Learning and Artificial Intelligence.

Artificial intelligence (AI) is already changing the business of law. Many firms are adopting the rapidly expanding suite of AI-powered tools to help their legal practitioners improve client relationships and deliver better outcomes. The burgeoning legal tech industry is putting an ever-expanding suite of AI-powered tools in the hands of law firms. Most tools are currently focused on lifting the burden of document review, analysis and research off your shoulders. Like all legal tech solutions, the goal of AI-powered tools is to offer lawyers new ways to facilitate the just, quick and cheap resolution of disputes and respond more appropriately to client needs.

Intel is becoming quite popular for its awesome drone light shows. The company doesn't make consumer drones but had made the events like Super Bowl and Olympics amazing with synchronized aerial drone shows. Lately, the Olympic drone shows flew 1218 drones in the air at one time. It also won a new Guinness World Records for the most drones flown simultaneously. Recently, it did another show, having a team of drones flying over headliner Odesza as the electronic music duo performed at the Coachella Music Festival in Indio, Calif. "It's all with software," said Anil Nanduri, VP & GM of Intel's drone team.

Vinci believes in a future where your headphones are the central hub of your connected world. Over the last two years Vinci has been testing and perfecting a smart, wireless headphone that is controlled by your voice and powered by cloud technology, with no connection needed to your mobile phone. Wire-free and phone-free, Vinci is perfect for joggers, travelers and all music lovers. Vinci allows you to voice control your music when manually choosing songs on your phone is inconvenient. No matter what you're doing - walking, running, or commuting - you can tell Vinci exactly what song or artist you want to listen to, say a specific genre or mood, or let Vinci pick a song for you.

Less than two weeks after second season premiered, HBO has confirmed Westworld will return for a third go-round. The sci-fi drama, which centers around a theme park filled with humanoid robots, has proven a hit for HBO, so it's hardly a surprise the network is saddling up for season three. It's not clear when the third season will hit screens, but given season two is just a couple of episodes in following a 16-month hiatus, it might be some time before it airs. Westworld creators Lisa Joy and Jonathan Nolan took a slightly different approach to marketing the second season, after releasing a video promising to reveal its secrets before the first episode had aired. Given Nolan's track record, though, we bet he has a few more tricks up his sleeve to keep those fan theories bubbling away.

China's first virtual reality (VR) theme park opened near Guiyang, the provincial capital of Guizhou. The park features 35 virtual attractions -- instead of getting on a roller coaster, you don VR goggles -- but there is a spectacular physical construction, too: a 53-meter-tall, 700-ton statue of a robot. You can watch a CGTN video report on Oriental Science Fiction Valley, or read these reports on the park from Reuters and Kotaku. Guizhou, one of China's poorest provinces, is perhaps most famous for Lao Ganma 老干妈 spicy sauce and Maotai, the sorghum-based spirit that lubricated Richard Nixon's visit to China in 1972. But the VR theme park is just the most recent manifestation of an initiative to drastically change Guizhou's economy and image, with support from the very top:

Deep Convolutional features extracted from a comprehensive labeled dataset, contain substantial representations which could be effectively used in a new domain. Despite the fact that generic features achieved good results in many visual tasks, fine-tuning is required for pretrained deep CNN models to be more effective and provide state-of-the-art performance. Fine tuning using the backpropagation algorithm in a supervised setting, is a time and resource consuming process. In this paper, we present a new architecture and an approach for unsupervised object recognition that addresses the above mentioned problem with fine tuning associated with pretrained CNN-based supervised deep learning approaches while allowing automated feature extraction. Unlike existing works, our approach is applicable to general object recognition tasks. It uses a pretrained (on a related domain) CNN model for automated feature extraction pipelined with a Hopfield network based associative memory bank for storing patterns for classification purposes. The use of associative memory bank in our framework allows eliminating backpropagation while providing competitive performance on an unseen dataset.

In this paper we show that the computational complexity of the Iterative Thresholding and K-Residual-Means (ITKrM) algorithm for dictionary learning can be significantly reduced by using dimensionality reduction techniques based on the Johnson-Lindenstrauss Lemma. We introduce the Iterative Compressed-Thresholding and K-Means (IcTKM) algorithm for fast dictionary learning and study its convergence properties. We show that IcTKM can locally recover a generating dictionary with low computational complexity up to a target error $\tilde{\varepsilon}$ by compressing $d$-dimensional training data into $m < d$ dimensions, where $m$ is proportional to $\log d$ and inversely proportional to the distortion level $\delta$ incurred by compressing the data. Increasing the distortion level $\delta$ reduces the computational complexity of IcTKM at the cost of an increased recovery error and reduced admissible sparsity level for the training data. For generating dictionaries comprised of $K$ atoms, we show that IcTKM can stably recover the dictionary with distortion levels up to the order $\delta \leq O(1/\sqrt{\log K})$. The compression effectively shatters the data dimension bottleneck in the computational cost of the ITKrM algorithm. For training data with sparsity levels $S \leq O(K^{2/3})$, ITKrM can locally recover the dictionary with a computational cost that scales as $O(d K \log(\tilde{\varepsilon}^{-1}))$ per training signal. We show that for these same sparsity levels the computational cost can be brought down to $O(\log^5 (d) K \log(\tilde{\varepsilon}^{-1}))$ with IcTKM, a significant reduction when high-dimensional data is considered. Our theoretical results are complemented with numerical simulations which demonstrate that IcTKM is a powerful, low-cost algorithm for learning dictionaries from high-dimensional data sets.

Convolution operation helps in leaning the context much faster than the LSTMs. The encoder can be parallelized using the Convolution, however, I am confused with parallelization of the decoder. During training, when we know the output translated sentence, we can provide the decoder the output sentence as the input by shifting it one time to the right. However, during testing, we have to run the decoder n times to extract n words of the output sentence, using the predicted word in the current time step as the input to the decoder in the next timestep. Using a decoder with LSTM / RNN layers would have increased the per layer execution time complexity, where a convolutional decoder can execute each layer parallel, but LSTM decoder would have still run 1 time compared to n times of convolutional decoder.

Artificial intelligence (AI) has worked its way into a variety of industries, from the obvious (autonomous vehicles) to the hidden (anti-money laundering due diligence). But while organizations are clearly recognizing the value associated with incorporating AI into their business processes, they also are encountering a number of challenges with integrating this new intelligence into their operational processes. The value of using algorithms to unmask hidden patterns and then correlate findings with other seemingly unrelated variables to create real "intelligence" is becoming increasingly clear with each completed proof-of-concept (POC) project. But it is the larger, organization-wide deployment of AI that will generate the return on investment (ROI) that companies large and small have been seeking. To fully access the operational and economic benefits of AI, however, organizations are realizing that, in most cases, enabling AI is not a plug-and-play proposition.