Businesses and nonprofits are finding novel ways to employ artificial intelligence in the developing world, using the tools to improve agriculture yields, infant health care, and entrepreneur earnings, according to speakers at MIT Technology Review's EmTech Digital conference in San Francisco on Tuesday. Solomon Assefa, who oversees IBM's research labs in Kenya and South Africa, said the company has been using AI to more accurately predict crop yields in specific regions, based on shifting weather patterns, soil moisture, and other conditions. This insight into growing conditions has helped local farmers raise financing to expand their operations, or make better decisions about the right seeds, appropriate fertilizer, and ideal times to plant and harvest. Separately, the tech giant's research lab has partnered with a startup, Hello Tractor, that links farmers in need of tractors with owners looking to lease equipment. By forecasting demand for the vehicles, IBM has also helped owners raise money to expand their fleet, boosting their profits, Assefa said.

Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world. This survey reviews computational approaches for code-switched Speech and Natural Language Processing. We motivate why processing code-switched text and speech is essential for building intelligent agents and systems that interact with users in multilingual communities. As code-switching data and resources are scarce, we list what is available in various code-switched language pairs with the language processing tasks they can be used for. We review code-switching research in various Speech and NLP applications, including language processing tools and end-to-end systems. We conclude with future directions and open problems in the field.

If you're inclined to puns, you might say medical samples are the lifeblood of hospital systems. But if you actually work with them, you know they're more of a headache. Because the same road traffic that keeps you from getting home keeps the couriers charged with moving these tissue and blood samples, collected by the millions daily and often in urgent need of analysis, from completing their missions. So it makes a lot of sense that when the FAA decided to sanction the first revenue-generating drone delivery scheme in the US, it went with one that promises to speed up that process, run by UPS and autonomous drone technology firm Matternet. It makes sense from the tech perspective, too: The cargo is extremely lightweight and compact, allowing the companies involved to focus on the delivery processes and mechanisms rather than trying to manage unwieldy payloads.

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.