Amir Kiani (from left), Chloe O'Connell and Nishit Asnani troubleshoot an algorithm to diagnose tuberculosis in computer lab at Stanford University. Amir Kiani (from left), Chloe O'Connell and Nishit Asnani troubleshoot an algorithm to diagnose tuberculosis in computer lab at Stanford University. Some computer scientists are enthralled by programs that can teach themselves how to perform tasks, such as reading X-rays. Many of these programs are called "black box" models because the scientists themselves don't know how they make their decisions. Already these black boxes are moving from the lab toward doctors' offices.

Remember the last time someone flipped you the bird? Whether or not that single finger was accompanied by spoken obscenities, you knew exactly what it meant. The conversion from movement into meaning is both seamless and direct, because we are endowed with the capacity to speak without talking and comprehend without hearing. We can direct attention by pointing, enhance narrative by miming, emphasize with rhythmic strokes and convey entire responses with a simple combination of fingers. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. The tendency to supplement communication with motion is universal, though the nuances of delivery vary slightly.

SEATTLE/WASHINGTON - Boeing Co. on Wednesday took its most aggressive moves yet to defend its core 737 airliner franchise, saying it had developed software fixes to prevent failures of an automated flight control system that is being scrutinized after two deadly crashes in the past five months. Boeing, in the midst of one its worst crises in years, is under pressure from crash victims' families, airlines, lawmakers in Washington and regulators around the world to prove that the automated flight control systems aboard its 737 Max aircraft are safe, and that pilots have the training required to override the system in an emergency. A Boeing official in Seattle said on Wednesday the timing of the software upgrade was "100 percent independent of the timing of the Ethiopian accident," and the company was taking steps to make the anti-stall system "more robust." There was no need to overhaul Boeing's regulatory relationship with the U.S. Federal Aviation Administration (FAA) now, the company said. "We are going to do everything that we can do to ensure that accidents like these never happen again," Mike Sinnett, vice president for product strategy and future airplane development told reporters.

As much as we'd like to, nobody knows exactly what the future holds. However, several individuals have put their minds, time, and effort toward figuring out what the future of marketplaces, industry, and work will look like to the greatest possible degree of accuracy. For those who are looking to figure out which industries are most ripe for disruption, which are dinosaurs, and how the future of work applies in your own life, these forward-looking influencers are must-follows. But don't just stop at their Twitter pages, as these influencers have personal websites, TED Talks, and more that are well worth checking out. This influencer has racked up several job titles on his way to becoming one of the most popular voicers in the future of work sphere.

Google is joining Facebook, Stanford and other outfits setting up institutions to support ethical AI. The company has created an Advanced Technology External Advisory Council that will shape the "responsible development and use" of AI in its products. The organization will ponder facial recognition, fair machine learning algorithms and other ethical issues. The initial council is a diverse group that tackles a range of disciplines and experiences. The current advisors include academics focused both on technical aspects of AI (such as computational mathematics and drones) as well as experts in ethics, privacy, and political policy.

An organization is only as good as its people, and therefore, effective human resource management is vital for the success of a business. This is the reason why millions upon millions of dollars are poured into HR efforts each and every year. Everyone wants the best piece of the talent cake. However, nothing comes without a struggle, and with the changing job market, alongside a widening skills gap, employers are starting to call for better and more effective ways to attract, acquire, and retain top talent. Artificial intelligence (commonly dubbed as AI) is here to answer that calling.

The area of formal ethics is experiencing a shift from a unique or standard approach to normative reasoning, as exemplified by so-called standard deontic logic, to a variety of application-specific theories. However, the adequate handling of normative concepts such as obligation, permission, prohibition, and moral commitment is challenging, as illustrated by the notorious paradoxes of deontic logic. In this article we introduce an approach to design and evaluate theories of normative reasoning. In particular, we present a formal framework based on higher-order logic, a design methodology, and we discuss tool support. Moreover, we illustrate the approach using an example of an implementation, we demonstrate different ways of using it, and we discuss how the design of normative theories is now made accessible to non-specialist users and developers.

Film is a universal language of modern societies. Larger-than-life images, stories, ideas, and characters portrayed in films can speak across the globe. This makes science and technology--which have shaped the modern world but remain little understood and poorly integrated into mainstream culture--a rich subject for film and a goldmine for filmmakers. From the mad scientist films of the '20s and '30s to the postnuclear dystopias of the '50s; and from the ecological disaster flics of the '70s and '80s to the space adventures of recent years, films have periodically reflected society's hopes and fears about science. But we can do better when it comes to dramatizing the great, ongoing human enterprise to understand and enhance the world around and inside us.

This paper presents an integrated methodology supporting decisions in urban planning. In particular, it deals with the prioritization and the selection of a portfolio of projects related to buildings of some values for the cultural heritage in cities. More precisely, our methodology has been validated to the historical center of Naples, Italy. Each project is assessed on the basis of a set of both quantitative and qualitative criteria with the purpose to determine their level of priority for further selection. This step was performed through the application of the Electre Tri-nC method which is a multiple criteria outranking based method for ordinal classification (or sorting) problems and allows to assign a priority level to each project as an analytical "recommendation" tool. To identify the efficient portfolios and to support the selection of the most adequate set of projects to activate, a set of resources (namely budgetary constraints) as well as some logical constraints related to urban policy requirements have to be taken into consideration together with the priority of projects in a portfolio analysis model. The process has been conducted by means of the interaction between analysts, municipality representative and experts. The proposed methodology is generic enough to be applied to other territorial or urban planning problems. We strongly believe that, given the increasing interest of historical cities to restore their cultural heritage, the integrated multiple criteria decision aiding analytical tool proposed in this paper has significant potential to be used in the future.

Step sizes in neural network training are largely determined using predetermined rules such as fixed learning rates and learning rate schedules, which require user input to determine their functional form and associated hyperparameters. Global optimization strategies to resolve these hyperparameters are computationally expensive. Line searches are capable of adaptively resolving learning rate schedules. However, due to discontinuities induced by mini-batch sampling, they have largely fallen out of favor. Notwithstanding, probabilistic line searches have recently demonstrated viability in resolving learning rates for stochastic loss functions. This method creates surrogates with confidence intervals, where restrictions are placed on the rate at which the search domain can grow along a search direction. This paper introduces an alternative paradigm, Gradient-Only Line Searches that are inexact (GOLS-I), as an alternative strategy to automatically resolve learning rates in stochastic cost functions over a range of 15 orders of magnitude without the use of surrogates. We show that GOLS-I is a competitive strategy to reliably resolve step sizes, adding high value in terms of performance, while being easy to implement. Considering mini-batch sampling, we open the discussion on how to split the effort to resolve quality search directions from quality step size estimates along a search direction.