Earlier this year Uber sold off its ride-hailing business in southeast Asia to a competitor, Grab, which is now raising $3 billion to further expand operations. Today Microsoft announced it's making a "strategic investment" in Grab, as the two launch a "broad partnership" to use Microsoft's machine learning and AI tech. The first step is adopting Microsoft's Azure servers as the cloud platform backing Grab's ride-hailing and digital wallet. After that the plans get bigger, as it anticipates using machine learning and image recognition to let passengers share their location with a driver by taking a picture of their surroundings that the system recognizes and converts into an address. Otherwise it could handle recommendations, improve fraud detection, improve its maps or power facial recognition to identify drivers and passengers.

Microsoft has announced making a strategic investment in ride-sharing service Grab, as one of the first moves under a recently forged partnership between the two companies. Grab will also adopt Microsoft Azure as its preferred cloud platform, hoping to use the latter's cloud and artificial intelligence capabilities to "scale its platform and increase its capacity and capabilities". Under the five-year agreement, the companies will work together on a range of technology projects, including in the areas of big data and AI, that president of Grab Ming Maa expects will transform the delivery of everyday services and mobility solutions in Southeast Asia. "Microsoft's investment into Grab highlights our position as the leading homegrown technology player in the region," he said. "We look forward to collaborating with Microsoft in the pursuit of enhancing on-demand transportation and seamless online-to-offline experiences for users."

For one 43-year-old Beijing patient, relief had seemed an impossible dream. His arm had been numb for 14 months and every hospital he went to gave him the same answer to his questions about a remedy. Surgeons told him that the risks of mass bleeding, stroke or even paralysis were too great with the delicate operation needed to fix the abnormalities in his spine and skull that were causing the condition. Then three years ago the patient met Tian Wei, a top spinal surgeon at Beijing's Jishuitan Hospital and an advocate of using robotics in medical operations. Tian and his team used a technology called the TiRobot system to create a 3D scan of the patient's torso and plot a surgical path to the affected area.

Microsoft has made a strategic investment in ride-hailing and on-demand services company Grab as part of a deal that includes collaborating on big data and AI projects. Under the agreement, Singapore-based Grab will adopt Microsoft Azure as its preferred cloud platformAzure cloud computing service. Microsoft and Grab didn't disclose financial terms. The idea behind the tie-up is for Grab to use Microsoft's product to scale its own digital platform, which has grown beyond ride-hailing. Grab also has its own payment service and makes food deliveries.

Deep neural networks (DNNs) are vulnerable to maliciously generated adversarial examples. These examples are intentionally designed by making imperceptible perturbations and often mislead a DNN into making an incorrect prediction. This phenomenon means that there is significant risk in applying DNNs to safety-critical applications, such as driverless cars. To address this issue, we present a visual analytics approach to explain the primary cause of the wrong predictions introduced by adversarial examples. The key is to analyze the datapaths of the adversarial examples and compare them with those of the normal examples. A datapath is a group of critical neurons and their connections. To this end, we formulate the datapath extraction as a subset selection problem and approximately solve it based on back-propagation. A multi-level visualization consisting of a segmented DAG (layer level), an Euler diagram (feature map level), and a heat map (neuron level), has been designed to help experts investigate datapaths from the high-level layers to the detailed neuron activations. Two case studies are conducted that demonstrate the promise of our approach in support of explaining the working mechanism of adversarial examples.

Many deep learning models are vulnerable to the adversarial attack, i.e., imperceptible but intentionally-designed perturbations to the input can cause incorrect output of the networks. In this paper, using information geometry, we provide a reasonable explanation for the vulnerability of deep learning models. By considering the data space as a non-linear space with the Fisher information metric induced from a neural network, we first propose an adversarial attack algorithm termed one-step spectral attack (OSSA). The method is described by a constrained quadratic form of the Fisher information matrix, where the optimal adversarial perturbation is given by the first eigenvector, and the model vulnerability is reflected by the eigenvalues. The larger an eigenvalue is, the more vulnerable the model is to be attacked by the corresponding eigenvector. Taking advantage of the property, we also propose an adversarial detection method with the eigenvalues serving as characteristics. Both our attack and detection algorithms are numerically optimized to work efficiently on large datasets. Our evaluations show superior performance compared with other methods, implying that the Fisher information is a promising approach to investigate the adversarial attacks and defenses.

User profiling means exploiting the technology of machine learning to predict attributes of users, such as demographic attributes, hobby attributes, preference attributes, etc. It's a powerful data support of precision marketing. Existing methods mainly study network behavior, personal preferences, post texts to build user profile. Through our data analysis of micro-blog, we find that females show more positive and have richer emotions than males in online social platform. This difference is very conducive to the distinction between genders. Therefore, we argue that sentiment context is important as well for user profiling.This paper focuses on exploiting microblog user posts to predict one of the demographic labels: gender. We propose a Sentiment Representation Learning based Multi-Layer Perceptron(SRL-MLP) model to classify gender. First we build a sentiment polarity classifier in advance by training Long Short-Term Memory(LSTM) model on e-commerce review corpus. Next we transfer sentiment representation to a basic MLP network. Last we conduct experiments on gender classification by sentiment representation. Experimental results show that our approach can improve gender classification accuracy by 5.53\%, from 84.20\% to 89.73\%.

Abstract--Smart services are an important element of the smart cities and the Internet of Things (IoT) ecosystems where the intelligence behind the services is obtained and improved through the sensory data. Providing a large amount of training data is not always feasible; therefore, we need to consider alternative ways that incorporate unlabeled data as well. In recent years, Deep reinforcement learning (DRL) has gained great success in several application domains. It is an applicable method for IoT and smart city scenarios where auto-generated data can be partially labeled by users' feedback for training purposes. In this paper, we propose a semi-supervised deep reinforcement learning model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent. To the best of our knowledge, the proposed model is the first investigation that extends deep reinforcement learning to the semi-supervised paradigm. As a case study of smart city applications, we focus on smart buildings and apply the proposed model to the problem of indoor localization based on BLE signal strength. Indoor localization is the main component of smart city services since people spend significant time in indoor environments. Our model learns the best action policies that lead to a close estimation of the target locations with an improvement of 23% in terms of distance to the target and at least 67% more received rewards compared to the supervised DRL model. The rapid development of Internet of Things (IoT) technologies motivated researchers and developers to think about new kinds of smart services that extract knowledge from IoT generated data. The scarcity of labeled data is a main issue for developing such solutions especially for IoT applications where a large number of sensors participate in generating data without being able to obtain class labels corresponding to the collected data. This publication was made possible by NPRP grant# [71113-1-199] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Google will shut down the consumer version of its social network Google after announcing data from up to 500,000 users may have been exposed to external developers by a bug that was present for more than two years in its systems. The company said in a blog that it had discovered and patched the leak in March of this year and had no evidence of misuse of user data or that any developer was aware or had exploited the vulnerability. Shares of its parent company Alphabet Inc, however, were down 1.5 per cent at $1150.75 (£878.71) in response to what was the latest in a run of privacy issues to hit the United States' big tech companies. Google said it had reviewed the issue, looking at the type of data involved, whether it could accurately identify the users to inform, whether there was any evidence of misuse, and whether there were any actions a developer or user could take. "None of these thresholds were met in this instance," it said.

The traditional view of outsourcing has tended to see cost reduction as one of the primary drivers for any customer. The idea that the'total cost of ownership' of a particular business function over the term of the outsourcing contract should be lower is very often part of the business case. Similarly, seeing outsourcing as a means of transforming a collection of assets on the balance sheet into a recurring service charge, and reducing (or at least apparently reducing) capital costs is another common refrain at the outset of deals. Whilst technology transformation / business change deals do happen where the aim is for a service upgrade at an increased overall cost, they are by no means as common as cost-led deals. To date, a large portion of the cost savings delivered through outsourcing deals – especially those that involve any sort of offshoring, nearshoring or even (in the London-centric UK at least) 'northshoring' – come from labour arbitrage: the central idea being that the activity undertaken by the outsourcing customer's relatively more expensive current local employees can be performed at a lower cost, but to the materially the same or even a better overall standard, by the outsourcing vendor's resources.