For many people, Google is simply the gateway to a vast archive of facts and memories. For those who pay closer attention to its business dealings, the company also invests billions to find new ways to use the power of computers: it's developing robots, virtual reality gear and self-driving cars. Remember all the hubbub about Google Glass? Google has been using the same approach in sustainability – spreading its wealth in a variety of projects to cut its waste and carbon footprint, initiatives which may one day generate profits. During the SXSW Eco conference this week, I caught up with Google's sustainability officer, Kate Brandt, to find out more.

If you'd like to learn how to run R within Azure Machine Learning and SQL Server, you may be interested in these upcoming 4-day Practical Data Science courses, presented by Rafal Lukawiecki from Project Botticelli. In this classroom-based course, you will learn machine learning, data mining, some statistics, data preparation, and how to interpret the results. You will also learn how to formulate business questions in terms of data science hypotheses and experiments, and how to prepare inputs to answer those questions. Rafal will share his decade of hands-on experience while teaching you about Azure Machine Learning (Azure ML) which is the foundation of Cortana Analytics Suite, and its highly-visual, on-premise companion, the SQL Server Analysis Services Data Mining engine, supplemented with the free Microsoft R Open and Microsoft R Server software. By the end of this course you will be able to plan and run data science projects.

After breaking his neck more than a decade ago, Nathan Copeland has finally regained his sense of touch. The only catch is that those feeling are fake -- artificially generated by a robotic arm and an array of tiny electrodes embedded in his brain by researchers at the University of Pittsburgh Medical Center. According to a study published in the journal Science Translational Medicine on Thursday, this system fully bypasses Copeland's natural sensory pathway, which had been severed in his accident, and instead transmits electrical signals directly into his sensory and motor cortices. This enables him to not only feel when something presses against the robotic hand but also control the mechanical arm using only his thoughts. Well, technically, his thoughts are first routed through an external computer which decodes the intent and translates it into commands that drive the robot arm.

Many machine learning algorithms expect data to be scaled consistently. There are two popular methods that you should consider when scaling your data for machine learning. In this tutorial, you will discover how you can rescale your data for machine learning. How To Prepare Machine Learning Data From Scratch With Python Photo by Ondra Chotovinsky, some rights reserved. Many machine learning algorithms expect the scale of the input and even the output data to be equivalent. It can help in methods that weight inputs in order to make a prediction, such as in linear regression and logistic regression.

Artificial-intelligence (AI) systems known as neural networks can recognize images, translate languages and even master the ancient game of Go. But their limited ability to represent complex relationships between data or variables has prevented them from conquering tasks that require logic and reasoning. In a paper published in Nature on 12 October1, the Google-owned company DeepMind in London reveals that it has taken a step towards overcoming this hurdle by creating a neural network with an external memory. The combination allows the neural network not only to learn, but to use memory to store and recall facts to make inferences like a conventional algorithm. This in turn enables it to tackle problems such as navigating the London Underground without any prior knowledge and solving logic puzzles.

Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (\textgreater{}150 Mg/ha, and \textgreater{}300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean \textgreater{}300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter-and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R 2 =0.54, RMSE=48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain "wall-to-wall" AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ~51 Mg/ha and R${}^2$=0.48 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values.

Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits and three languages (English, Spanish and Italian) compared with prior work in author profiling. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.

In our today's information society more and more data emerges, e.g.~in social networks, technical applications, or business applications. Companies try to commercialize these data using data mining or machine learning methods. For this purpose, the data are categorized or classified, but often at high (monetary or temporal) costs. An effective approach to reduce these costs is to apply any kind of active learning (AL) methods, as AL controls the training process of a classifier by specific querying individual data points (samples), which are then labeled (e.g., provided with class memberships) by a domain expert. However, an analysis of current AL research shows that AL still has some shortcomings. In particular, the structure information given by the spatial pattern of the (un)labeled data in the input space of a classification model (e.g.,~cluster information), is used in an insufficient way. In addition, many existing AL techniques pay too little attention to their practical applicability. To meet these challenges, this article presents several techniques that together build a new approach for combining AL and semi-supervised learning (SSL) for support vector machines (SVM) in classification tasks. Structure information is captured by means of probabilistic models that are iteratively improved at runtime when label information becomes available. The probabilistic models are considered in a selection strategy based on distance, density, diversity, and distribution (4DS strategy) information for AL and in a kernel function (Responsibility Weighted Mahalanobis kernel) for SVM. The approach fuses generative and discriminative modeling techniques. With 20 benchmark data sets and with the MNIST data set it is shown that our new solution yields significantly better results than state-of-the-art methods.

We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given pre-specified level that bounds the tail probability of the maximum of a (possibly dependent but stationary) random sequence. We do not assume data distribution, but rather relying on the asymptotic distribution of extremal values, and reduce the problem to estimate three parameters of the extreme value distributions and the extremal index. We specially take care of data dependence via estimating extremal index since in many settings, such as scan statistics, change-point detection, and extreme bandits, where dependence in the sequence of statistics can be significant. Key features of our DTM also include robustness and the computational efficiency, and it only requires one sample path to form a reliable estimate of the threshold, in contrast to the Monte Carlo sampling approach which requires drawing a large number of sample paths. We demonstrate the good performance of DTM via numerical examples in various dependent settings.

Various alignment problems arising in cryo-electron microscopy, community detection, time synchronization, computer vision, and other fields fall into a common framework of synchronization problems over compact groups such as Z/L, U(1), or SO(3). The goal of such problems is to estimate an unknown vector of group elements given noisy relative observations. We present an efficient iterative algorithm to solve a large class of these problems, allowing for any compact group, with measurements on multiple 'frequency channels' (Fourier modes, or more generally, irreducible representations of the group). Our algorithm is a highly efficient iterative method following the blueprint of approximate message passing (AMP), which has recently arisen as a central technique for inference problems such as structured low-rank estimation and compressed sensing. We augment the standard ideas of AMP with ideas from representation theory so that the algorithm can work with distributions over compact groups. Using standard but non-rigorous methods from statistical physics we analyze the behavior of our algorithm on a Gaussian noise model, identifying phases where the problem is easy, (computationally) hard, and (statistically) impossible. In particular, such evidence predicts that our algorithm is information-theoretically optimal in many cases, and that the remaining cases show evidence of statistical-to-computational gaps.