The area around Hillview Avenue in Palo Alto is dotted with well-known tech innovators: Xerox's PARC, Microsoft's Skype, VMware Inc. and HP Labs, among others. Amid the research centers and campuses of these tech stalwarts is another well-known company, but one whose name might seem somewhat out of place among these Silicon Valley trailblazers. Ford Motor Co. F, 1.32% is hoping to change that. Last year, the auto giant hung its shingle outside what it calls the Ford Research and Innovation Center, Palo Alto, as it seeks to embrace technology's disruption of its 100-plus-year-old business. With personal auto ownership as passe as telephone landlines to a new generation of consumers, electric-car powerhouse Tesla Motors Inc. TSLA, 1.27% -- also based in Palo Alto--upending the industry, and self-driving vehicles predicted in our future, Ford, like most auto makers around the world, is behind the proverbial eight ball. The automotive pioneer that developed the first mass produced, affordable car is experiencing the "innovator's dilemma," a conundrum faced by leading companies when a new, often cheaper, "good enough" technology breaks into its market dominance.

NEW YORK, NY--(Marketwired - Jun 15, 2016) - Datorama, a global leader in marketing analytics innovation, today announced the company has added an office in Europe. The latest addition to Datorama's global footprint is located in Hamburg, Germany and marks a critical milestone as the company expands into the German, Austrian and Swiss (DACH) region. Datorama's Hamburg office further strengthens a robust EMEA presence, which includes: Amsterdam, Barcelona, London and Paris. Designed for marketers, Datorama's Marketing Integration Engine helps leading enterprises, agencies and publishers centralize all of their marketing data across silos for cross-channel visualization, analysis and data-driven insight generation. By analyzing inputs from unlimited data sources, including online and offline marketing channels, and first- and third-party applications across CRM, billing, call centers, and more, the company's patent-pending artificial intelligence (AI)-based software delivers a single source of truth at the data layer to drive tactical and strategic marketing performance optimization.

The Earth isn't getting any bigger, so we need to start finding more efficient ways to feed the projected 10 billion people by 2050 using the same amount of land. Researchers from EPFL in Switzerland and Penn State University used the Caffe deep learning framework and Tesla K40 GPUs to train a model that identifies crop diseases. For now, the researchers created a website, Plant Village, an open access database of 50,000 images of healthy and diseased crops. The goal is to launch a mobile app to help farmers around the world by providing them with the ability to snap a photo of their diseased plant and the app would automatically diagnose it. Silicon Valley-based Blue River Technology has developed a deep learning solution called LettuceBot that rolls through a field photographing 5,000 young plants a minute, using algorithms and machine vision to identify each sprout as lettuce or a weed.

As what was once mere data evolves into actionable intelligence, the context that binds that data becomes ever more essential. With no context around those four letters, you might not understand the reference or make any sort of connection. But if you add just one word to "java," such as "development," "island," or "coffee," the reference changes completely--and that's with just a single word of context. This is the type of active context and connection that the Brainspace engine provides. "Context is a very important part of what we do. When we analyze documents, we take the context into consideration," says Ravi Sathyanna, vice president of technology and product management at Brainspace.

The increasing interest in complex networks research has been a consequence of several intrinsic features of this area, such as the generality of the approach to represent and model virtually any discrete system, and the incorporation of concepts and methods deriving from many areas, from statistical physics to sociology, which are often used in an independent way. Yet, for this same reason, it would be desirable to integrate these various aspects into a more coherent and organic framework, which would imply in several benefits normally allowed by the systematization in science, including the identification of new types of problems and the cross-fertilization between fields. More specifically, the identification of the main areas to which the concepts frequently used in complex networks can be applied paves the way to adopting and applying a larger set of concepts and methods deriving from those respective areas. Among the several areas that have been used in complex networks research, pattern recognition, optimization, linear algebra, and time series analysis seem to play a more basic and recurrent role. In the present manuscript, we propose a systematic way to integrate the concepts from these diverse areas regarding complex networks research. In order to do so, we start by grouping the multidisciplinary concepts into three main groups, namely features, similarity, and network connectivity. Then we show that several of the analysis and modeling approaches to complex networks can be thought as a composition of maps between these three groups, with emphasis on nine main types of mappings, which are presented and illustrated. Such a systematization of principles and approaches also provides an opportunity to review some of the most closely related works in the literature, which is also developed in this article.

Regression trees are becoming increasingly popular as omnibus predicting tools and as the basis of numerous modern statistical learning ensembles. Part of their popularity is their ability to create a regression prediction without ever specifying a structure for the mean model. However, the method implicitly assumes homogeneous variance across the entire explanatory-variable space. It is unknown how the algorithm behaves when faced with heteroscedastic data. In this study, we assess the performance of the most popular regression-tree algorithm in a single-variable setting under a very simple step-function model for heteroscedasticity. We use simulation to show that the locations of splits, and hence the ability to accurately predict means, are both adversely influenced by the change in variance. We identify the pruning algorithm as the main concern, although the effects on the splitting algorithm may be meaningful in some applications.

We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is suitable for both batch and online learning, and admits a fast kernel-width-selection procedure as the random features can be re-used efficiently for all kernel widths. The features are constructed by sampling trees via a Mondrian process [Roy and Teh, 2009], and we highlight the connection to Mondrian forests [Lakshminarayanan et al., 2014], where trees are also sampled via a Mondrian process, but fit independently. This link provides a new insight into the relationship between kernel methods and random forests.

Migration crisis, climate change or tax havens: Global challenges need global solutions. But agreeing on a joint approach is difficult without a common ground for discussion. Public spheres are highly segmented because news are mainly produced and received on a national level. Gain- ing a global view on international debates about important issues is hindered by the enormous quantity of news and by language barriers. Media analysis usually focuses only on qualitative re- search. In this position statement, we argue that it is imperative to pool methods from machine learning, journalism studies and statistics to help bridging the segmented data of the international public sphere, using the Transatlantic Trade and Investment Partnership (TTIP) as a case study.

Artificial neural networks are powerful models, which have been widely applied into many aspects of machine translation, such as language modeling and translation modeling. Though notable improvements have been made in these areas, the reordering problem still remains a challenge in statistical machine translations. In this paper, we present a novel neural reordering model that directly models word pairs and their alignment. Further by utilizing LSTM recurrent neural networks, much longer context could be learned for reordering prediction. Experimental results on NIST OpenMT12 Arabic-English and Chinese-English 1000-best rescoring task show that our LSTM neural reordering feature is robust, and achieves significant improvements over various baseline systems. 1 Introduction In statistical machine translation, the language model, translation model, and reordering model are the three most important components.

Tall grass hid the advancing cadets from my perch in building 7. The tall grass hid nothing from the drone the defenders flew over their position, a Parrot AR 2.0, a common model used by civilian fliers. A minute later, after the drone pilot filmed the crawling cadets, instructors called in mock artillery fire. The cadets' position was compromised, and while the rest of their platoon advanced to take the buildings, these 10 cadets instead spent an hour in the sun contemplating what they could have done about the drone. The answer was standing right behind them.