The UK government is to fund a trial of drone-based deliveries of blood and other medical supplies in Tanzania. The goal is to radically reduce the amount of time it takes to send stock to health clinics in the African nation by road or other means. The scheme involves Zipline, a Silicon Valley start-up that began running a similar service in Rwanda in October. Experts praised that initiative but cautioned that "cargo drones" are still of limited use to humanitarian bodies. The Department for International Development (Dfid) has not said how much money will be invested in the Tanzanian effort or for how long.

HARI SREENIVASAN: But first: 2016 has been a wild ride, one that we're not likely to forget anytime soon, much of it focused on politics, but many things happened in the world of science and technology as well. William Brangham starts our review for our weekly segment, the Leading Edge. WILLIAM BRANGHAM: Indeed, we wanted to look at some of the more remarkable discoveries and innovations, and setbacks that we saw in the scientific world this year. WILLIAM BRANGHAM: So, everyone is doing their year-end best-of list for 2016. The biggest one by far was a historic find announced in February, the detection of gravity waves.

Ford has unveiled a sleeker design for its self-driving Fusion. The new model is still equipped with the current autonomous vehicle platform, but now has more computing power that will help the firm unleash them on the road by 2021. Also, unlike other autonomous models, the American car maker removed the bundle of sensors from the roof, giving this prototype a more traditional automobile look. Ford has unveiled a sleeker design of it self-driving car Fusion. It process data transmitted from an array of sensors that help the car'see' its surroundings.

SYS-CON Events announced today that Technologic Systems Inc., an embedded systems solutions company, will exhibit at SYS-CON's @ThingsExpo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Technologic Systems is an embedded systems company with headquarters in Fountain Hills, Arizona. They have been in business for 32 years, helping more than 8,000 OEM customers and building over a hundred COTS products that have never been discontinued. Technologic Systems' product base consists of a wide variety of off-the-shelf PC/104 single board computers, computer-on-modules, touch panel computers, peripherals and industrial controllers. They also offer custom configurations and design services.

As seen in Part 1 and Part 2 of this series, it is hard not to feel excited about machine learning. First, it empowers machines to teach themselves the tasks that humans can perform but find difficult to "teach" a computer via conventional coding (e.g. Secondly, it enables computers to perform tasks that far exceed human abilities, like analysing terabytes of data at lightning speed to unearth hidden patterns and make sense of them. But it is also hard not to feel some unease about the prospect of self-improving computer systems with increasingly human-like and super-human aptitudes, whether it is the threat of mass unemployment, the erosion of privacy, or simply the inability to understand, validate and trust the technologies that will increasingly impact our lives. These problems that artificial intelligence (AI) is throwing back at us are complex and multifaceted, and to tackle them requires concerted endeavours by our technologists, entrepreneurs, lawmakers and thinkers from all fields and walks of life. It will be a test of humankind's collective wisdom to ensure that our social institutions keep up with our technological progress. The advent of autonomous vehicles (AVs) illustrates the wide-ranging economic, legal and ethical questions that new technologies raise. AVs are already roaming the streets and conveying passengers in parts of the world, and many more are expected to hit the roads over the next five years as tech companies like Google, Baidu and Lyft race against incumbent automakers to make reliable and affordable self-driving cars. This is likely to dramatically alter the economics of transportation, from ownership rate to utilisation rate. It is estimated that in the US and the UK our cars on average are being driven just 5% of the time and they spend the remaining 95% in a garage or a car park.[1] That ratio may well be reversed if the availability of door-to-door transport is no longer linked to the availability of human drivers.

An innovative AI startup from Israel is using deep-learning AI technology to listen to machinery and predict whether it's about to go wrong. Wondering if your car engine has a problem just got a lot easier. A lot of the most high-profile applications of deep learning technology involve aspects of computer vision, such as cutting-edge facial-recognition technology. However, an innovative artificial intelligence startup from Israel is looking to apply those same neural networks and smart algorithms to another area -- acoustics. Better yet, they are doing so to help users spot early warning signs that machines, such as cars, may be about to fail. "I was on a train about three years ago, going back to my hotel after a business meeting," 3DSignals CEO Amnon Shenfeld told Digital Trends.

A prototype robot that could assist senior citizens by detecting vital signs. Fifty-year-olds, take notice: In a few decades you might have a robot roommate taking care of you. In conjunction with Rice University, IBM is developing a series of sensors that can someday live inside a robot interface to help senior citizens stay safe. Susanne Keohane, senior technologist at IBM Research, says the project addresses a growing need for technology that helps aging populations preserve both their independence and their overall health, while also avoiding disruption in their daily lives. "If you slap an Apple Watch on an 88-year-old, that's not feasible for most 88-year-olds," Keohane tells Business Insider.

Ten years from today, the center of our digital lives will no longer be the smart phone, but device that looks like ordinary eyeglasses: except those glasses will have settings for Virtual and Augmented Reality. What you really see and what is computer generated will be mixed so tightly together, that we won't really be able to tell what is real and what is illusion. Instead of touching and sliding on a mobile phone, we will make things happen by moving our eyes or by brainwaves. When we talk with someone or play an online game, we will see that person in the same room with us. We will be able to touch and feel her or him through haptic technology. We won't need to search online with words, because there will be a new Visual Web 100 times larger than the current Internet, and we will find things by images, buy things by brands, or just by looking at a logo on the jacket of a passerby.

Raj Singh is a Los Angeles-based entrepreneur and product design expert. In 2011, after a creating and growing a series of previous companies to seven-figure valuations, the energetic and personable Singh founded his current company, Go Moment, which, Singh says, is dedicated to making customer service instant and unforgettable." Go Moment's platform, Ivy, is the world's leading automated customer service platform for hotels. By making use of Watson artificial intelligence (yes, that Watson, IBM's famously inhuman Jeopardy champion), Ivy is able to automatically correspond with and engage hotel guests and intelligently responds to guest inquiries and concerns via mobile messaging, in real time. Under Singh's leadership, the Ivy platform has been adopted by hundreds of hotels and casino properties nationwide-including well-known 4- and 5-star properties-and is available to assist literally millions of guests.

Inference in complex and high-dimensional statistical models is a very challenging problem that is ubiquitous in applications such as climate informatics [Monteleoni et al., 2013], bioinformatics [Cohen, 2004] and machine learning [Wainwright and Jordan, 2008], to mention a few. We are interested in sequential Bayesian inference in settings where we have a sequence of posterior distributions that we need to compute. To be specific, we are focusing on settings where the model (or state variable) is high-dimensional, but where there are local dependencies. One example of the type of models we consider are the so-called spatiotemporal models [Wikle, 2015, Cressie and Wikle, 2011, Rue and Held, 2005]. Sequential Monte Carlo (SMC) methods comprise one of the most successful methodologies for sequential Bayesian inference. However, SMC struggles in high dimensions and these methods are rarely used for dimensions, say, higher than ten [Rebeschini and van Handel, 2015].