KPMG recently conducted its annual survey of 800 global technology industry leaders, from startups to Fortune 500. The results are the top 3 emerging technology trends that are expected to disrupt business significantly in the next three years. Some 41 percent of respondents identified these three below. Take a closer look at and follow any new developments there, as your business will be disrupted by these technologies sooner than you think. This is the leading trend, with 20 percent of respondents identifying it as driving business transformation.

In late June, BlackBerry reported quarterly earnings that missed analysts' forecasts due to an unexpected sales decline, ramping up pressure on the company to meet its goal of boosting software and services revenue by 10 percent to 15 percent this year. BlackBerry declined to comment on the stock price, but QNX manager Grant Courville said it expected to sign deals with at least three major companies to get QNX into self-driving vehicles.

A June 2017 study by PricewaterhouseCoopers (PwC) found relatively low current or anticipated use of virtual assistants--such as Amazon's Alexa and Apple's Siri--among Germany's internet users. But a new report by PwC has revealed that internet users in the country are much more interested in other artificial intelligence (AI) applications. PwC polling in July found 85% of adult internet users in Germany had used or would like to use AI in some capacity. Those polled were most interested in using AI in the form of robots. The study found 58% of respondents were using or interested in having an AI-powered robot to clean their home.

These and other findings are from the McKinsey Global Institute Study, and discussion paper, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in) published last month. McKinsey Global Institute published an article summarizing the findings titled How Artificial Intelligence Can Deliver Real Value To Companies. McKinsey interviewed more than 3,000 senior executives on the use of AI technologies, their companies' prospects for further deployment, and AI's impact on markets, governments, and individuals. McKinsey Analytics was also utilized in the development of this study and discussion paper. The current rate of AI investment is 3X the external investment growth since 2013.

Bad memories can haunt us for a long time, and in some cases, the impact of such painful experiences can last a lifetime. However, it is possible to bid adieu to such memories that cause stress and even induce insomnia in some individuals. Fear memories are developed in response to dangerous situations, however, not all such memories are beneficial to our survival, reveals a new study conducted by the researchers at the University of California, Riverside. The formation of these fear memories strengthens the connection between neurons in the brain. So, weakening these connections can help in erasing the memories, the research published Thursday in the journal, Neuron, reveals.

The cloud is getting smarter by the minute. In fact, it will soon know more about the photos you've uploaded than you do. Cloud storage company Box announced today that it is adding computer-vision technology from Google to its platform. Users will be able to search through photos, images, and other documents using their visual components, instead of by file name or tag. "As more and more data goes into the cloud, we're seeing they need more powerful ways to organize and understand their content," says CEO Aaron Levie.

Raman spectroscopy is a ubiquitous method for characterisation of substances in a wide range of settings including industrial process control, planetary exploration, homeland security, life sciences, geological field expeditions and laboratory materials research. In all of these environments there is a requirement to identify substances from their Raman spectrum at high rates and often in high volumes. Whilst machine classification has been demonstrated to be an essential approach to achieve real time identification, it still requires preprocessing of the data. This is true regardless of whether peak detection or multivariate methods, operating on whole spectra, are used as input. A standard pipeline for a machine classification system based on Raman spectroscopy includes preprocessing in the following order: cosmic ray removal, smoothing and baseline correction.

Among the local consistency techniques used for solving constraint networks, path-consistency (PC) has received a great deal of attention. However, enforcing PC is computationally expensive and sometimes even unnecessary. Directional path-consistency (DPC) is a weaker notion of PC that considers a given variable ordering and can thus be enforced more efficiently than PC. This paper shows that DPC (the DPC enforcing algorithm of Dechter and Pearl) decides the constraint satisfaction problem (CSP) of a constraint language if it is complete and has the variable elimination property (VEP). However, we also show that no complete VEP constraint language can have a domain with more than 2 values. We then present a simple variant of the DPC algorithm, called DPC*, and show that the CSP of a constraint language can be decided by DPC* if it is closed under a majority operation. In fact, DPC* is sufficient for guaranteeing backtrack-free search for such constraint networks. Examples of majority-closed constraint classes include the classes of connected row-convex (CRC) constraints and tree-preserving constraints, which have found applications in various domains, such as scene labeling, temporal reasoning, geometric reasoning, and logical filtering. Our experimental evaluations show that DPC* significantly outperforms the state-of-the-art algorithms for solving majority-closed constraints.

Generative adversarial networks (GAN's) [2] are a recent popular technique for learning generative models for high-dimensional unstructured data (typically images). GAN's employ two networks - a generator G that is tasked with producing samples from the data distribution, and a discriminator D that aims to distinguish real samples from the samples produced by G. The two networks alternatively try to best each other, ultimately resulting in the generator G converging to the true data distribution. While most of the research on GAN's is focused on the unsupervised setting, where the data is comprised of unlabeled images, there has been research on conditional GAN's [1] where the goal is to learn a conditional model of the data, i.e. to build a conditional model that can generate images given a particular attribute setting. In one approach [1], both the generator and discriminator are fed attributes as side information so as to enable the generator to generate images conditioned on attributes. In an alternative approach proposed in [5], the authors build auxiliary classifier GAN's (AC-GAN's) where side information is reconstructed by the discriminator instead. Irrespective of the specific approach, this line of research focuses on the supervised setting where it is assumed that all the images have attribute tags. Given that labels are expensive, it is of interest to explore semi-supervised settings where only a small fraction of the images have attribute tags, while a majority of the images are unlabeled. There has been some work on using GAN's in the semi-supervised setting.

We consider the analysis of high dimensional data given in the form of a matrix with columns consisting of observations and rows consisting of features. Often the data is such that the observations do not reside on a regular grid, and the given order of the features is arbitrary and does not convey a notion of locality. Therefore, traditional transforms and metrics cannot be used for data organization and analysis. In this paper, our goal is to organize the data by defining an appropriate representation and metric such that they respect the smoothness and structure underlying the data. We also aim to generalize the joint clustering of observations and features in the case the data does not fall into clear disjoint groups. For this purpose, we propose multiscale data-driven transforms and metrics based on trees. Their construction is implemented in an iterative refinement procedure that exploits the co-dependencies between features and observations. Beyond the organization of a single dataset, our approach enables us to transfer the organization learned from one dataset to another and to integrate several datasets together. We present an application to breast cancer gene expression analysis: learning metrics on the genes to cluster the tumor samples into cancer sub-types and validating the joint organization of both the genes and the samples. We demonstrate that using our approach to combine information from multiple gene expression cohorts, acquired by different profiling technologies, improves the clustering of tumor samples.