A major concern for our rights as consumers is the way that machines direct us according to their interests and not ours. Experts such as Dr Jonathan Cave warn about the growing influence of software machines on our lives. Cave says that software machines will make use of what they know about us to present information to us which may not be to our advantage. Because the search engines that we have used know a certain amount about us and our previous buying decisions, they are keen to exploit that by turning us into a buyer of something, by a process known as'filter bubbles' – a feedback loop where recommendations only reinforce existing patterns. As Dr Rupp states'if you are not paying then you are not the customer'. Thus if you are not paying for an internet technology such as Google or Facebook it is not acting in your interests, but rather in the interests of the customers who are paying to present information to you.

In 1913, Ford Motor Company changed the car-making game by installing moving assembly belts in its production facilities. The move made building automobiles quicker, cheaper, and more efficient than before, while also setting the tone for modern manufacturing in general. Since then, the industry has made leaps and bounds by automating much of the process, but Ford isn't done innovating yet. At an assembly plant in Cologne, Germany, the American brand is running a trial program where humans and robots work side by side. Called co-bots, these small machines help workers install shock absorbers onto Fiesta subcompact vehicles, but according to Ford, the co-bots can even be programmed to make coffee or give massages.

Recently, the need of deploying new wireless networks for smart gas metering has raised the problem of radio planning in the169 MHz band. Unluckily, software tools commonly adopted for radio planning in cellular communication systems cannot be employed to solve this problem because of the substantially lower transmission frequencies characterizing this application. In this manuscript a novel data-centric solution, based on the use of support vector machine techniques for classification and regression, is proposed. Our method requires the availability of a limited set of received signal strength measurements and the knowledge of a three-dimensional map of the propagation environment of interest, and generates both an estimate of the coverage area and a prediction of the field strength within it. Numerical results referring to different Italian villages and cities evidence that our method is able to achieve good accuracy at the price of an acceptable computational cost and of a limited effort for the acquisition of measurements in the considered environments.

The satisfiability problem for SPARQL 1.0 patterns is undecidable in general, since the relational algebra can be emulated using such patterns. The goal of this paper is to delineate the boundary of decidability of satisfiability in terms of the constraints allowed in filter conditions. The classes of constraints considered are bound-constraints, negated bound- constraints, equalities, nonequalities, constant-equalities, and constant-nonequalities. The main result of the paper can be summarized by saying that, as soon as inconsistent filter conditions can be formed, satisfiability is undecidable. The key insight in each case is to find a way to emulate the set difference operation. Undecidability can then be obtained from a known undecidability result for the algebra of binary relations with union, composition, and set difference. When no inconsistent filter conditions can be formed, satisfiability is decidable by syntactic checks on bound variables and on the use of literals. Although the problem is shown to be NP-complete, it is experimentally shown that the checks can be implemented efficiently in practice. The paper also points out that satisfiability for the so-called ‘well-designed’ patterns can be decided by a check on bound variables and a check for inconsistent filter conditions.

Random survival forests (RSF) are a powerful method for risk prediction of right-censored outcomes in biomedical research. RSF use the log-rank split criterion to form an ensemble of survival trees. The most common approach to evaluate the prediction accuracy of a RSF model is Harrell's concordance index for survival data ('C index'). Conceptually, this strategy implies that the split criterion in RSF is different from the evaluation criterion of interest. This discrepancy can be overcome by using Harrell's C for both node splitting and evaluation. We compare the difference between the two split criteria analytically and in simulation studies with respect to the preference of more unbalanced splits, termed end-cut preference (ECP). Specifically, we show that the log-rank statistic has a stronger ECP compared to the C index. In simulation studies and with the help of two medical data sets we demonstrate that the accuracy of RSF predictions, as measured by Harrell's C, can be improved if the log-rank statistic is replaced by the C index for node splitting. This is especially true in situations where the censoring rate or the fraction of informative continuous predictor variables is high. Conversely, log-rank splitting is preferable in noisy scenarios. Both C-based and log-rank splitting are implemented in the R~package ranger. We recommend Harrell's C as split criterion for use in smaller scale clinical studies and the log-rank split criterion for use in large-scale 'omics' studies.

For more than a decade, Silicon Valley's technology investors and entrepreneurs obsessed over social media and mobile apps that helped people do things like find new friends, fetch a ride home or crowdsource a review of a product or a movie. Now Silicon Valley has found its next shiny new thing. And it does not have a "Like" button. The new era in Silicon Valley centers on artificial intelligence and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.

Though deals to private artificial intelligence companies -- excluding incubator/accelerator rounds -- fell 10% in Q2'16, dollar funding reached an all-time high. Q2'16 saw 3 100M mega-rounds by companies using AI: a 154M Series A round to China-based healthcare startup iCarbonX (backed by Tencent, Vcanbio), a 100M growth equity round raised by New Jersey-based Fractal Analytics (backed by Khazanah Nasional Berhad) and a 100M Series D round raised by California-based cybersecurity unicorn Cylance (backed by investors including Blackstone Group, Insight Venture Partners, and Khosla Ventures). Our AI category includes companies applying AI solutions to verticals like healthcare, security, advertising, and finance as well as those developing general-purpose AI tech. Nearly 70% of the deals went to startups in the United States in Q2'16. A majority of the startups raising funds were still in their early-stages: Nearly 60% of the deals went to startups raising seed/angel and Series A rounds, while mid-stage startups (Series B and C) received 12% of the deals.

Renewable energy like solar and wind power are changing the way we generate electricity. Our energy production is becoming cleaner and cheaper, and in many countries renewables are starting to overtake fossil fuels as the primary power source. But one of the biggest problems with renewables has yet to be solved: what happens if it's cloudy? More specifically, the problem is that renewable energy sources can never provide a constant source of power. No matter how many solar panels you build, they all provide zero power when the sun goes down.

Google has acquired Moodstocks, a Paris-based startup that specialises in smartphone image recognition as part of its continued efforts to boost its own artificial intelligence (AI) research, development and capabilities. Announced in a blog post on 6 July, Vincent Simonet, head of Google's research and development (R&D) centre in Paris, says the tech giant's latest purchase is proof of its commitment to the promising sector. "Many Google services use machine learning to make them simpler and more useful in everyday life such as Google Translate, Smart Reply Inbox, or the Google app," Simonet wrote in French. "We have made great strides in terms of visual recognition: Now you can search in Google Pictures such as'party' or'beach' and the application will offer you good pictures without you needing to categorise them manually. But there is still much to do in this area. And this is where Moodstocks comes in."

How an ecommerce company used predictive analytics to improve customer retention. Acquiring new customers is much more expensive than retaining current ones. Keeping customers from unsubscribing from a company's services or from choosing another company's solution is therefore a challenge that should be at the top of every corporate agenda. A way to address this challenge is through predictive customer churn prevention, in which data is used to find out which customers are likely to churn in order to win them back -- before they are gone. Showroomprivé.com, an ecommerce company founded in 2006, sought ways to employ machine learning approaches to retain more customers.