At its AI for Allconference in Bengaluru, Microsoft India today showcased some of its most recent artificial intelligence (AI) solutions aimed at helping improve lives and transform businesses. Microsoft India said it is helping 650 India-based partners use the Microsoft cognitive services, IoT, AI and machine learning platforms to build solutions for India. Over the last year, Microsoft and its partners have deployed AI solutions in areas such as healthcare, education, agriculture,retail, e-commerce, manufacturing and financial services. Today Microsoft also announced a partnership with Forus Health, a Bengaluru-based technology company focusing on retinal imaging devices, to leverage AI capabilities for early detection of diabetic retinopathy, glaucoma & macular degeneration, and help reduce avoidable blindness. "We are working closely with our partners to bring AI to all sectors in India,"said Mr. Anant Maheshwari, President, Microsoft India.

Pixar and Disney and Warner Bros. Interactive and TT Games and Lego and Sony and Microsoft and Nintendo are teaming up to bring you all the Incredibles action you can handle, except also now the Incredibles are made out of Lego plus it's a video game. It's the greatest team of corporate supers since 2004, when Disney and Pixar and THQ and Apple and Sony and Disney Interactive and Microsoft and Nintendo put aside their differences to turn the first movie into a video game: Technology has come a long way in the last 14 years: Where Playstation 2s and Gamecubes once struggled to approximate the slick look of Brad Bird's 2004 movie, modern gaming systems can render Mr. Incredible and the gang in much higher definitions, plus simultaneously transform them into Lego. It's the latest in a long series of popular media franchises getting converted into Lego form and also into video game form, from Lego Star Wars: The Video Game to Lego Harry Potter: Years 5–7. Sometimes these licensed properties follow circuitous paths to their final Lego video game form--Batman became Lego Batman: The Video Game in 2008, which preceded The Lego Batman Movie in 2017, which then became The Lego Batman Movie Game (iOS only), which, God willing, will lead to Lego Batman: The Lego Batman Movie: The Video Game in a year or two--but in the case of The Incredibles, we're getting the Lego and the video game and the sequel all at the same time: June 15.

The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications. In a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as a prominent agent model to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have been proposed to enable support of MAS in complex, real-time, and uncertain environments. This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages such as R, there are several practical challenges in the field of ecological modeling related to unbiased performance estimation, optimization of algorithms using hyperparameter tuning and spatial autocorrelation. We address these issues in the comparison of several widely used machine-learning algorithms such as Boosted Regression Trees (BRT), k-Nearest Neighbor (WKNN), Random Forest (RF) and Support Vector Machine (SVM) to traditional parametric algorithms such as logistic regression (GLM) and semi-parametric ones like generalized additive models (GAM). Different nested cross-validation methods including hyperparameter tuning methods are used to evaluate model performances with the aim to receive bias-reduced performance estimates. As a case study the spatial distribution of forest disease Diplodia sapinea in the Basque Country in Spain is investigated using common environmental variables such as temperature, precipitation, soil or lithology as predictors. Results show that GAM and RF (mean AUROC estimates 0.708 and 0.699) outperform all other methods in predictive accuracy. The effect of hyperparameter tuning saturates at around 50 iterations for this data set. The AUROC differences between the bias-reduced (spatial cross-validation) and overoptimistic (non-spatial cross-validation) performance estimates of the GAM and RF are 0.167 (24%) and 0.213 (30%), respectively. It is recommended to also use spatial partitioning for cross-validation hyperparameter tuning of spatial data.

Recently, image forensics community has paied attention to the research on the design of effective algorithms based on deep learning technology and facts proved that combining the domain knowledge of image forensics and deep learning would achieve more robust and better performance than the traditional schemes. Instead of improving it, in this paper, the safety of deep learning based methods in the field of image forensics is taken into account. To the best of our knowledge, this is a first work focusing on this topic. Specifically, we experimentally find that the method using deep learning would fail when adding the slight noise into the images (adversarial images). Furthermore, two kinds of strategys are proposed to enforce security of deep learning-based method. Firstly, an extra penalty term to the loss function is added, which is referred to the 2-norm of the gradient of the loss with respect to the input images, and then an novel training method are adopt to train the model by fusing the normal and adversarial images. Experimental results show that the proposed algorithm can achieve good performance even in the case of adversarial images and provide a safety consideration for deep learning-based image forensics

SMOTE is one of the oversampling techniques for balancing the datasets and it is considered as a pre-processing step in learning algorithms. In this paper, four new enhanced SMOTE are proposed that include an improved version of KNN in which the attribute weights are defined by mutual information firstly and then they are replaced by maximum entropy, Renyi entropy and Tsallis entropy. These four pre-processing methods are combined with 1NN and J48 classifiers and their performance are compared with the previous methods on 11 imbalanced datasets from KEEL repository. The results show that these pre-processing methods improves the accuracy compared with the previous stablished works. In addition, as a case study, the first pre-processing method is applied on transportation data of Tehran-Bazargan Highway in Iran with IR equal to 36.

There's been a significant increase in enterprise investment on technologies that analyse, organise, access, and provide advisory services that are based on use cases for unstructured data. In fact, worldwide spending on cognitive and artificial intelligence (AI) systems will reach $19.1 billion in 2018 - that's an increase of 54.2 percent over the amount spent in 2017. With industries investing aggressively in projects that utilise cognitive and AI software capabilities, International Data Corporation (IDC) now forecasts cognitive and AI spending will grow to $52.2 billion in 2021 and achieve a compound annual growth rate (CAGR) of 46.2 percent over the 2016-2021 forecast period. "Interest and awareness of AI is at a fever pitch. Every industry and every organisation should be evaluating AI to see how it will affect their business processes and go-to-market efficiencies," said David Schubmehl, research director at IDC. IDC has estimated that by 2019, 40 percent of digital transformation initiatives will use AI services and by 2021, 75 percent of enterprise applications will use AI.

Jaywalkers in one Chinese city will soon receive an instant notification and a fine as soon as they violate the rules, thanks to a new Big Brother-style scheme. Officials are looking to upgrade an existing system that uses artificial intelligence and facial recognition technology to name and shame offenders. Images of pedestrians crossing the road against red traffic lights are already beamed onto large LED screens. Now, the government is consulting with mobile carriers and social media firms to add the extra deterrent for the region's 12 million people. Jaywalkers in one Chinese city will soon receive an instant notification as soon as they violate the rules, thanks to a new spy scheme.

Supercell is looking for a Data Engineer to work closely with our game teams. As a Data Engineer, you will develop and operate data pipelines that help our game teams deliver fully tailored game experiences. You will also participate in machine learning model design and implementation, together with the data scientists. Your responsibility is keeping the data and models fresh for serving our 100M daily players. You enjoy working equally much in data processing, software development, and service operations.

At age 25, Anjney Midha has a stronger resume than some people twice his age. Before graduating from Stanford, he joined the venture capital firm Kleiner Perkins Caufield & Byers. He led the firm's investment in Magic Leap, the mysterious and much-hyped augmented reality company. Then he ditched venture capital to pursue a dream that had followed him from a technology-free young adulthood on a bird sanctuary in India, to the hyper-connected streets of Singapore, to his days at Stanford. That dream was to share his world--more than he could show in a photo, better than what he could convey with words--with the family and friends he'd left in India.