Prague's Karlovy Lazne nightclub has recently embraced automation in the workplace. Its shiny new Kuka robot arm has been employed to lay down some of its beats. It rotates on an hourly basis with meatbot DJs, and was programmed by a Czech robotics firm to pluck CDs from a rack and toss them onto one of three decks in front of it. It even seems to interact with the crowd like any Eastern European house DJ. If you can get beyond the fact that a Kuka robot arms costs tens of thousands of dollars and is clearly complete overkill for this application, there's an obvious question to ask: how does it sound?

French train engineering giant Alstom is to test automated freight trains in the Netherlands this year. The automated train prototype can travel for about 100km (60 miles) without driver intervention. Automation will free the train driver to focus on supervising the train's progress. The test's purpose is to provide a live demonstration that the train and the signal system can communicate effectively to drive the train. Alstom signed an agreement with the the Dutch infrastructure operator ProRail and Rotterdam Rail Feeding (RRF) to carry out the tests along the Betuweroute - a 150km double track freight railway line connecting Rotterdam to Germany.

It's no secret that AI is being applied to virtually everything, but this was a big week for enterprise conversational AI. Salesforce's Einstein debuted a platform for businesses to deploy bots on apps and websites, Facebook Messenger is coming to the web, and Microsoft Word now has a Resume Assistant imbued with intelligence from LinkedIn. It's pretty exciting to see Microsoft begin to incorporate insights from LinkedIn into products like Cortana and Microsoft Word. Using AI to determine the best job title to put on a resume can be powerful, as Indeed demonstrated last month at VB Summit, but the most intriguing part of the assistant may be its ability to surface good examples of work experience derived from LinkedIn public profiles written by humans. Salesforce's bots can be configured to only operate when a business is closed, and if the bot lacks confidence in its answer to a question or a high-value lead lands on your website, the conversation can be routed to an experienced (human) customer service agent.

The restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep learning. RBM finds wide applications in dimensional reduction, feature extraction, and recommender systems via modeling the probability distributions of a variety of input data including natural images, speech signals, and customer ratings, etc. We build a bridge between RBM and tensor network states (TNS) widely used in quantum many-body physics research. We devise efficient algorithms to translate an RBM into the commonly used TNS. Conversely, we give sufficient and necessary conditions to determine whether a TNS can be transformed into an RBM of given architectures. Revealing these general and constructive connections can cross-fertilize both deep learning and quantum many-body physics. Notably, by exploiting the entanglement entropy bound of TNS, we can rigorously quantify the expressive power of RBM on complex data sets. Insights into TNS and its entanglement capacity can guide the design of more powerful deep learning architectures. On the other hand, RBM can represent quantum many-body states with fewer parameters compared to TNS, which may allow more efficient classical simulations.

Multi-camera full-body pose capture of humans and animals in outdoor environments is a highly challenging problem. Our approach to it involves a team of cooperating micro aerial vehicles (MAVs) with on-board cameras only. The key enabling-aspect of our approach is the on-board person detection and tracking method. Recent state-of-the-art methods based on deep neural networks (DNN) are highly promising in this context. However, real time DNNs are severely constrained in input data dimensions, in contrast to available camera resolutions. Therefore, DNNs often fail at objects with small scale or far away from the camera, which are typical characteristics of a scenario with aerial robots. Thus, the core problem addressed in this paper is how to achieve on-board, real-time, continuous and accurate vision-based detections using DNNs for visual person tracking through MAVs. Our solution leverages cooperation among multiple MAVs. First, each MAV fuses its own detections with those obtained by other MAVs to perform cooperative visual tracking. This allows for predicting future poses of the tracked person, which are used to selectively process only the relevant regions of future images, even at high resolutions. Consequently, using our DNN-based detector we are able to continuously track even distant humans with high accuracy and speed. We demonstrate the efficiency of our approach through real robot experiments involving two aerial robots tracking a person, while maintaining an active perception-driven formation. Our solution runs fully on-board our MAV's CPU and GPU, with no remote processing. ROS-based source code is provided for the benefit of the community.

Extracting information from functional magnetic resonance images (fMRI) has been a major area of research for many years, but is still demanding more accurate techniques. Nowadays, we have a plenty of available information about the brain-behavior that can be used to develop more precise methods. Thus, this paper presents a new Dictionary Learning method that allows incorporating external information regarding the studied problem, through a novel sets of constraints. Finally, we apply this proposed method to synthetic fMRI data, where several tests show an improvement in the performance compared with other common techniques.

Melanoma is the deadliest form of skin cancer. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to investigate the impact of segmentation and classifier. The unexpected small impact of automatic versus semi-automatic/manual segmentation suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.

For a graph representation of a dataset, a straightforward normality measure for a sample can be its graph degree. Considering a weighted graph, degree of a sample is the sum of the corresponding row's values in a similarity matrix. The measure is intuitive given the abnormal samples are usually rare and they are dissimilar to the rest of the data. In order to have an in-depth theoretical understanding, in this manuscript, we investigate the graph degree in spectral graph clustering based and kernel based point of views and draw connections to a recent kernel method for the two sample problem. We show that our analyses guide us to choose fully-connected graphs whose edge weights are calculated via universal kernels. We show that a simple graph degree based unsupervised anomaly detection method with the above properties, achieves higher accuracy compared to other unsupervised anomaly detection methods on average over 10 widely used datasets. We also provide an extensive analysis on the effect of the kernel parameter on the method's accuracy.

Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them. But also generic hardware and software implementations of deep learning run more efficiently for sparse networks. Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints. We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network. DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded. We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance. DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.

The implementation, data privacy, and operational challenges facing life science and healthcare professionals dedicated to integrating AI into their organization vastly differ by therapeutic area and patient population, size of the organization, and the number of resources available to them. Cookie-cutter solutions cannot address the unique challenges faced by a company. It is critical that the education available to these professionals meets the varying needs of the industry. As such, ExL Events has expanded its Artificial Intelligence conference series to now introduce the European Artificial Intelligence Innovation Summit. Through keynotes, panel discussions, and case studies, the executive speaking faculty provides key insights into the ethical standards of AI; explores the realistic steps to achieve successful AI execution; defines the regulatory boundaries and limitations; reviews the necessary metrics to measure the success and efficacy of the AI system; and examines case studies on industry vertical use. Dr. Alfa is Vice President, Discovery, and Product at Recursion Pharmaceuticals (recursionpharma.com), an AI-enabled drug discovery company combining state-of-the-art machine learning with automated cell biology.