A California-based start-up is building drones to drop medical supplies in some of the most remote areas of Rwanda, putting the emerging technology where it can do some real good. Here's the challenge, according to Zipline International, a Silicon Valley robotics start-up that is developing and building the drones: somewhere in the neighborhood of two billion people lack adequate access to essential medical products because they live in inaccessible areas. As a result, almost 3 million children under age five die every year. Initially, the company's drone service is slated to deliver blood to 21 transfusion clinics across the western half of Rwanda, one of the poorest nations in the world. Then it will expand service to the remainder of the country in early 2017, Justin Hamilton, a company spokesperson, said.

Dung beetles record a mental image of the positions of the Sun, the Moon and the stars and use the snapshot to navigate, according to researchers. Scientists in Sweden found that the beetles capture the picture of the sky while dancing on a ball of manure. As they roll away with their malodorous prize, the beetles compare the stored image with their current location. The beetles' navigational skills could aid the development of driverless vehicles, the researchers suggest. Previous studies have shown that dung beetles have an amazing ability to navigate by the light of the Milky Way.

Let's say that the average lifespan of a human being is L 70 years. The probability of dying this year, at any given age y, is p(y). Let's assume that the number of people that are y years old is M(y), and that p(y) is a monotonic decreasing function of age. If we have 7 billion human beings on Earth today, given these assumptions, how many will die this year? Now the question is: what is the lower and upper bounds for N (number of people who will die this year), regardless of the functions M() and p(), provided p() is monotonic decreasing, and that average age at death is L 70 years.

Artificial intelligence (AI) is an emotionally loaded term that strikes fascination into some and fear into others. But if we strip it of fantasy and ignore cyborgs and apocalypse, there is a near-term, practical side of AI that is already unfolding. Most humans can recognise a chair because they have learnt what a chair is – they can identify thousands of examples of chairs even if they have never seen that chair before. Instead of memorising every image of what a chair could be, humans learn what a chair is and then apply that to new images and examples of chairs. But how does a computer learn what a chair is?

WASHINGTON – U.S. special operations forces working with African partners called in an airstrike against the al-Qaida-linked al-Shabaab group in Somalia on Thursday, killing five, the Pentagon said. Jeff Davis said U.S. troops were advising and assisting Ugandan troops from the African Union Mission to Somalia (AMISOM) in southern Somalia, west of Mogadishu. The AMISOM troops were raiding an illegal Shabaab roadblock where the jihadis were extorting payments from drivers. "They came under fire from the al-Shabaab militants, and we called in an airstrike in their defense," Davis said. A U.S. defense official said the strike was conducted by drone.

Merriam-Webster dictionary defines a trend as a "current movement in a particular direction," however, VLAB keynote speaker and Venture Capitalist Ann Winblad likes to say "wave" instead. The companies her firm Hummer Winblad Venture Partners (HWVP) invests in are typically riding enterprise software waves. "Waves form far out into the ocean and can go very deep," said Winblad comparing nature's waves to what's happening in the software industry. Big data is a term for data sets that are so large and complex that traditional data processing applications are inadequate to handle them. An example of a company riding this wave is MuleSoft, an HWVP investment. They make it easy to connect applications, data and devices.

A century ago, more than 60,000 tigers roamed the wild. Today, that number has dwindled to around 3,200. Poaching is one of the main drivers of this steep decline. Humans have pushed tigers to near-extinction, whether for their skins, medicine or for trophy hunting. The same applies to other large animal species like elephants and rhinoceros that play unique and crucial roles in the ecosystems where they live. Human patrols serve as the most direct form of protection of endangered animals, especially in large national parks.

While CRISPR, nanobots and head transplants are making headlines as medical breakthroughs, a number of new technologies are also making progress tackling some of the toughest age-old diseases still plaguing millions of people in the poorest parts of the world. In low income countries, over 75% of the population dies before the age of 70 due to infectious diseases including HIV/AIDS, lung infections, tuberculosis, diarrheal diseases, malaria, and increasingly, cardiovascular diseases. Over a third of deaths in low income countries are among children under age 14 primarily due to pneumonia, diarrheal diseases, malaria and neonatal complications. In the developed world, those living in extreme poverty, such as homeless populations, also die on average at age 48. Over the last year, artificial intelligence, robotics and biotechnology have all generated a number of new solutions that have the potential to dramatically reduce these problems.

At 100million a pop, you might expect F-35 fighter jets to take-off at the first time of asking. But the life of a test pilot is not that simple, with dozens of computer systems to calibrate and reset before the Air Force's most sophisticated plane can even taxi to the runway. Defense News got a glimpse of how testing F-35s works during a visit to Edwards Air Force Base in southern California. Major Raven LeClair, of the 461st flight test squadron, begins his day at around 10am by checking the plane for any issues. It immediately became clear that the day's testing would not pass without a hitch, with an alarm sounding as soon as he got to the aircraft.

Let's suppose you've agreed to a rather odd travel program, where you're going to be suddenly transported to a randomly selected country, and your job is to figure out where you end up. So, here you are in some new country, and all countries are equally likely. You make a list of places and probabilities that you're in those places (all equally likely at about 1/200 for 200 countries). You look around and appear to be in a restaurant. Some countries have more restaurants (per capita/per land area) than others, so you decrease the odds that you're in Algeria or Sudan and increase the odds that you're in Singapore or other high-restaurant-density places. That is, you just multiply the probability that you were in a country with the probability of finding oneself in a restaurant in that country, given that one were already in the country, to obtain the new probability. After a few moments, the waitress brings you sushi, so you decrease the odds for Tajikistan and Paraguay and correspondingly increase the odds on Japan, Taiwan, and such places where sushi restaurants are relatively common. You pick up the chopsticks and try the sushi, discovering that it's excellent. Japan is now by far the most likely place, and though it's still possible that you're in the United States, it's not nearly as likely (sadly for the US). Those "probabilities" are getting really hard to read with all those zeros in front. All that matters is the relatively likelihood, so perhaps you scale that last column by the sum of the whole column. Now it's a probability again, and it looks something like this: Now that you're pretty sure it's Japan, you make a new list of places inside Japan to see if you can continue to narrow it down. You write out Fukuoka, Osaka, Nagoya, Hamamatsu, Tokyo, Sendai, Sapporo, etc., all equally likely (and maybe keep Taiwan too, just in case). Now the waitress brings unagi. You can get unagi anywhere, but it's much more common in Hamamatsu, so you increase the odds on Hamamatsu and slightly decrease the odds everywhere else. By continuing in this manner, you may eventually be able to find that you're eating at a delicious restaurant in Hamamatsu Station -- a rather lucky random draw.