This is the web version of Data Sheet, Fortune's daily newsletter on the top tech news. Sign up here to get it delivered to your inbox. The number of transistors packed onto a modern chip inside your phone or PC runs into the billions but it's still sometimes amazing to comprehend the computing power you can easily hold in the palm of your hand. When I met Intel vice presidents Gadi Singer and Carey Kloss on Wednesday, they showed me a new circuit board the company has created for speeding up artificial intelligence apps. The board is the size of an SSD drive, made to plug into a standard PC or server.

National Workshops to Explore Long-range Strategies 7. Collect suggestions from the national planning workshops, distilled in to 93 actions, assess all via five (5) Real-Time Delphi's 8. Final Report for Public Discussion 10.

A collaborative group that includes Salesforce, Deloitte, the World Economic Forum (WEF) and the United Kingdom Office of AI today introduced guidelines for government officials to procure artificial intelligence systems. The guidelines that counsel vendors to ask particular questions before selling their AI to government agencies is being called by the WEF the first for any national government worldwide. In the works for about 10 months, the guidelines made by the previously mentioned organizations were brought together by the WEF's Centre for the Fourth Industrial Revolution and its AI and ML team. The Centre for the Fourth Industrial Revolution hosts fellows from nations around the world to focus on major initiatives. Governments the world over are increasingly using AI to do things like predict the needs of citizens, help with health care screening, or do things in criminal justice like power predictive policing, track suspects with facial recognition, or determine if people deserve pretrial bail hearings.

Linking facts across documents is a challenging task, as the language used to express the same information in a sentence can vary significantly, which complicates the task of multi-document summarization. Consequently, existing approaches heavily rely on hand-crafted features, which are domain-dependent and hard to craft, or additional annotated data, which is costly to gather. To overcome these limitations, we present a novel method, which makes use of two types of sentence embeddings: universal embeddings, which are trained on a large unrelated corpus, and domain-specific embeddings, which are learned during training. To this end, we develop SemSentSum, a fully data-driven model able to leverage both types of sentence embeddings by building a sentence semantic relation graph. SemSentSum achieves competitive results on two types of summary, consisting of 665 bytes and 100 words. Unlike other state-of-the-art models, neither hand-crafted features nor additional annotated data are necessary, and the method is easily adaptable for other tasks. To our knowledge, we are the first to use multiple sentence embeddings for the task of multi-document summarization.

Many attempts have been done to extend the great success of convolutional neural networks (CNNs) achieved on high-end GPU servers to portable devices such as smart phones. Providing compression and acceleration service of deep learning models on the cloud is therefore of significance and is attractive for end users. However, existing network compression and acceleration approaches usually fine-tuning the svelte model by requesting the entire original training data (\eg ImageNet), which could be more cumbersome than the network itself and cannot be easily uploaded to the cloud. In this paper, we present a novel positive-unlabeled (PU) setting for addressing this problem. In practice, only a small portion of the original training set is required as positive examples and more useful training examples can be obtained from the massive unlabeled data on the cloud through a PU classifier with an attention based multi-scale feature extractor. We further introduce a robust knowledge distillation (RKD) scheme to deal with the class imbalance problem of these newly augmented training examples. The superiority of the proposed method is verified through experiments conducted on the benchmark models and datasets. We can use only $8\%$ of uniformly selected data from the ImageNet to obtain an efficient model with comparable performance to the baseline ResNet-34.

Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health records where sepsis labels were defined automatically following established clinical criteria. We argue that the practice of incorporating the clinical criteria that are used to automatically define ground truth sepsis labels as features of severity scoring models is inherently circular and compromises the validity of the proposed approaches. We propose to create an independent ground truth for sepsis research by exploiting implicit knowledge of clinical practitioners via an electronic questionnaire which records attending physicians' daily judgements of patients' sepsis status. We show that despite its small size, our dataset allows to achieve state-of-the-art AUROC scores. An inspection of learned weights for standardized features of the linear model lets us infer potentially surprising feature contributions and allows to interpret seemingly counterintuitive findings.

--The purpose of this work is to develop and study a distributed strategy for Pareto optimization of an aggregate cost consisting of regularized risks. Each risk is modeled as the expectation of some loss function with unknown probability distribution while the regularizers are assumed deterministic, but are not required to be differentiable or even continuous. The individual, regularized, cost functions are distributed across a strongly-connected network of agents and the Pareto optimal solution is sought by appealing to a multi-agent diffusion strategy. T o this end, the regularizers are smoothed by means of infimal convolution and it is shown that the Pareto solution of the approximate, smooth problem can be made arbitrarily close to the solution of the original, non-smooth problem. Performance bounds are established under conditions that are weaker than assumed before in the literature, and hence applicable to a broader class of adaptation and learning problems. Index T erms --Distributed optimization, diffusion strategy, smoothing, proximal operator, non-smooth regularizer, proximal diffusion, regularized diffusion. The objective of distributed learning is the solution of global, stochastic optimization problems across networks of agents through localized interactions and without information about the statistical properties of the data. Using streaming data, the resulting strategies are adaptive in nature and able to track drifts in the location of the minimizers due to variations in the statistical properties of the data. Regularization is one useful technique to encourage or enforce structural properties on the sought after minimizer, such as sparsity or constraints. A substantial number of regularizers are inherently non-smooth, while many cost functions are differentiable.

Differentiable Architecture Search (DARTS) has attracted a lot of attention due to its simplicity and small search costs achieved by a continuous relaxation and an approximation of the resulting bi-level optimization problem. However, DARTS does not work robustly for new problems: we identify a wide range of search spaces for which DARTS yields degenerate architectures with very poor test performance. We study this failure mode and show that, while DARTS successfully minimizes validation loss, the found solutions generalize poorly when they coincide with high validation loss curvature in the space of architectures. We show that by adding one of various types of regularization we can robustify DARTS to find solutions with smaller Hessian spectrum and with better generalization properties. Based on these observations we propose several simple variations of DARTS that perform substantially more robustly in practice. Our observations are robust across five search spaces on three image classification tasks and also hold for the very different domains of disparity estimation (a dense regression task) and language modelling. We provide our implementation and scripts to facilitate reproducibility.

Driving in dynamically changing traffic is a highly challenging task for autonomous vehicles, especially in crowded urban roadways. The Artificial Intelligence (AI) system of a driverless car must be able to arbitrate between different driving strategies in order to properly plan the car's path, based on an understandable traffic scene model. In this paper, an AI behavior arbitration algorithm for Autonomous Driving (AD) is proposed. The method, coined AIBA (AI Behavior Arbitration), has been developed in two stages: (i) human driving scene description and understanding and (ii) formal modelling. The description of the scene is achieved by mimicking a human cognition model, while the modelling part is based on a formal representation which approximates the human driver understanding process. The advantage of the formal representation is that the functional safety of the system can be analytically inferred. The performance of the algorithm has been evaluated in Virtual Test Drive (VTD), a comprehensive traffic simulator, and in GridSim, a vehicle kinematics engine for prototypes.

Walgreens is getting its wings. The pharmacy chain has teamed up with Alphabet's drone delivery service Wing to bring food and beverage, over-the-counter medication and other items to consumers. This'store to door' testing is set to begin next month in Virginia and will offer more than 100 products and pre-built'packs' for purchase in the Wing app. Walgreens has teamed up with Alphabet's drone delivery service Wing to bring food and beverage, over-the-counter medication and other items to consumers The partnership between Walgreens and Wing aims to further explore the future of health and wellness products and retail delivery through the air, offering product availability and home delivery minutes after placing orders via the Wing app. 'Walgreens continues to explore partnerships to transform and modernize our customer experience and we are proud to be the first retailer in the U.S. to offer an on-demand commercial drone delivery option with Wing,' said Vish Sankaran, chief innovation officer, Walgreens Boots Alliance, Inc., in a press release.