Even a high-tech factory floor will still have a place for people, says Simon Jacobson, vice president of research at Gartner Inc. IT -0.15 % Human workers give manufacturers flexibility, allowing companies to reap the benefits of automation while preserving the ability to fill special orders. To have that kind of potential with an end-to-end automated system would require repeated reprogramming, adding cost and time, Mr. Jacobson says. By contrast, keeping people as an integral part of the process, working in modular assembly cells, for example, makes it easy to tweak production according to demand. "The trick is to automate with the human, not automate the human, or automate the human out of the job," says Mr. Jacobson. To be those people in the still often physically demanding environment of high-tech manufacturing, however, isn't easy.

For almost 50 years, CSV has been the format of choice for tabular data. Given the ubiquity of CSV and the pervasive need to deal with CSV in real workflows -- where speed, accuracy, and fault tolerance is a must -- we decided to build a CSV reader that runs in parallel. We conducted extensive benchmarks of ParaText against 7 CSV readers and 5 binary readers. Please refer to our benchmarking whitepaper for more details. In our tests, ParaText can load a CSV file from a cold disk at a rate of 2.5 GB/second and 4.2 GB/second out-of-core from a warm disk.

Part of Microsoft Accelerator's batch 3 of startups, DefinedCrowd is filling a niche in the big data and machine learning community, providing near-real-time feeds of rich language data, checked by actual well-informed humans all over the world. The need comes from the Catch-22 that often arrests deep data analysis, in that you have to understand the data to analyze it, but you must analyze it to understand it. The vast landscape of the spoken and written word and its big data counterpart in natural language processing is especially troublesome in this way. "In the artificial intelligence space, to develop virtual assistants like Cortana, or Apple's Siri and things like that, you need large amounts of voice recordings, you need transcriptions of those voices, you need intents and empathy labeling of those voices," said Daniela Braga, co-founder and chief scientist, in an interview with TechCrunch. "The crowd input provides the extra refinement of the data that basically no machine can do."

The position is within Informatics, with a specialization in data science. At the University of Skövde Informatics is defined as the science that addresses how information is represented, processed and communicated in artificial and natural systems, and how such systems are used and developed in order to achieve usable and effective applications and solutions for individuals, organizations or society. Data science can overall be defined as the collection of theories, methods and techniques that all strive to convert large volumes of complex and heterogeneous data into knowledge that supports various decision-makers. Data science, thus, overlaps with traditional scientific disciplines, such as applied mathematics, information science, computer technology, statistics and computer science, along with a rapidly increasing number of application areas, e.g., business intelligence, biomedicine, textual analysis, geo-temporal analysis and medical and healthcare informatics. As one of the oldest and most prominent research groups in artificial intelligence (AI) in Sweden, the Skövde Artificial Intelligence Lab (SAIL) at the University of Skövde consists of more than 15 researchers conducting research within applied AI in close collaboration with businesses and organizations.

Should Politicians be Replaced by Artificial Intelligence?

Fans of the computer game, Minecraft, may have already grown their own Yellow Meranti tree. But unlike on your computer screen, Yellow Meranti trees are huge and can grow to be as tall as 20 double-decker buses. Researchers have now found a tree in Malaysia which stands at a massive 89.5 metres tall, placing it 1.2 metres ahead of the previous record holder. The tree is part of the forest known as'Sabah's Lost World' - the Maliau Basin Conservation Area, one of Malaysia's last few untouched wildernesses The huge tree was discovered by scientists from the University of Cambridge working with the Sabah Forestry Department to help protect the area's biodiversity. Dr David Coomes, who led the study, said: 'It's a smidgen taller than the record, which makes it quite probably the tallest tree recorded in the Tropics!' At 89.5 metres, it is the height of 20 double decker buses, and just shorter than Big Ben.

Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a non-parametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted in Bayesian statistical modeling we use the predictive likelihood to investigate if the model can discriminate between a motor task and rest both within and across subjects. We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest. We find that the number of states extracted are driven by subject variability and preprocessing differences while the individual states are almost purely defined by either task or rest. This questions how we in general interpret dynamic FC and points to the need for more research on what drives dynamic FC.

We consider online optimization in the 1-lookahead setting, where the objective does not decompose additively over the rounds of the online game. The resulting formulation enables us to deal with non-stationary and/or long-term constraints , which arise, for example, in online display advertising problems. We propose an on-line primal-dual algorithm for which we obtain dynamic cumulative regret guarantees. They depend on the convexity and the smoothness of the non-additive penalty, as well as terms capturing the smoothness with which the residuals of the non-stationary and long-term constraints vary over the rounds. We conduct experiments on synthetic data to illustrate the benefits of the non-additive penalty and show vanishing regret convergence on live traffic data collected by a display advertising platform in production.

In decentralized networks (of sensors, connected objects, etc.), there is an important need for efficient algorithms to optimize a global cost function, for instance to learn a global model from the local data collected by each computing unit. In this paper, we address the problem of decentralized minimization of pairwise functions of the data points, where these points are distributed over the nodes of a graph defining the communication topology of the network. This general problem finds applications in ranking, distance metric learning and graph inference, among others. We propose new gossip algorithms based on dual averaging which aims at solving such problems both in synchronous and asynchronous settings. The proposed framework is flexible enough to deal with constrained and regularized variants of the optimization problem. Our theoretical analysis reveals that the proposed algorithms preserve the convergence rate of centralized dual averaging up to an additive bias term. We present numerical simulations on Area Under the ROC Curve (AUC) maximization and metric learning problems which illustrate the practical interest of our approach.

Completely random measures (CRM) represent the key building block of a wide variety of popular stochastic models and play a pivotal role in modern Bayesian Nonparametrics. A popular representation of CRMs as a random series with decreasing jumps is due to Ferguson and Klass (1972). This can immediately be turned into an algorithm for sampling realizations of CRMs or more elaborate models involving transformed CRMs. However, concrete implementation requires to truncate the random series at some threshold resulting in an approximation error. The goal of this paper is to quantify the quality of the approximation by a moment-matching criterion, which consists in evaluating a measure of discrepancy between actual moments and moments based on the simulation output. Seen as a function of the truncation level, the methodology can be used to determine the truncation level needed to reach a certain level of precision. The resulting moment-matching \FK algorithm is then implemented and illustrated on several popular Bayesian nonparametric models.