A new article in ScienceDaily reports, "A century ago, more than 60,000 tigers roamed the wild. Today, the worldwide estimate has dwindled to around 3,200. Poaching is one of the main drivers of this precipitous drop. Whether killed for skins, medicine or trophy hunting, humans have pushed tigers to near-extinction. 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. However, protection agencies have limited resources for patrols. With support from the National Science Foundation (NSF) and the Army Research Office, researchers are using artificial intelligence (AI) and game theory to solve poaching, illegal logging and other problems worldwide, in collaboration with researchers and conservationists in the U.S., Singapore, Netherlands and Malaysia."

AzureML is the cloud hosted machine learning platform on top of Microsoft's cloud platform. Readers of Data Science Central will realize that AzureML have hosted a few webinars about their platform. This tutorial will walk you through integrating Python with AzureML. You are planning to move out of the place you are currently staying and are looking for a place place which is similar to the current place. How will you decide where to go?

Training Tensorflow's large language model on the Penn Tree Bank yields a test perplexity of 82. It depends on your personal taste. The high temperature sample displays greater linguistic variety, but the low temperature sample is more grammatically correct. Such is the world of temperature sampling - lowering the temperature allows you to focus on higher probability output sequences and smooth over deficiencies of the model. Temperature sampling works by increasing the probability of the most likely words before sampling.

Robots just helped shed light on a maritime tragedy. The US Coast Guard, National Transportation Safety Board and Woods Hole Oceanographic have used both an autonomous underwater vehicle (AUV) and a fiber-controlled craft to find the voyage data recorder of the El Faro, a cargo ship that sank near the Bahamas during Hurricane Joaquin last October. That's no mean feat when its remains are 15,000 feet deep, and the recorder is roughly the size of a coffee can. The recovery should not only help explain the exact circumstances of the El Faro's final moments, but provide some closure to the families of the 33 crew members that lost their lives.

The next chapter of early childhood education may be coming courtesy of Sesame Workshop and the letters I-B-M. Sesame Workshop, which has made the beloved children's education show "Sesame Street" for decades, and IBM's Watson -- of "Jeopardy!" The firms will work together for three years to develop products for the classroom and the home, which combine the artificial intelligence prowess of Watson with Sesame Workshop's deep knowledge of how to teach to the preschool set. The hope is that Watson, which can learn and adapt based on its user, will be able to adjust its teaching based on a child's skill level and learning style. Sesame Workshop has worked for years to provide a mix of learning styles in its flagship show, but is looking to do more.

The good news: The Loch Ness Monster has been captured on sonar by an underwater robot operated by the British division of Norway's Kongsberg Maritime. The bad news: "Nessie" is a prop from a Sherlock Holmes film that sank in the loch in 1969. The monstrous model was long thought lost until it was discovered this week by the Munin Autonomous Underwater Vehicle (AUV) as part of an underwater survey of the loch for The Loch Ness Project and VisitScotland. There have been sporadic sightings of what is purported to be the Loch Ness Monster since the first recorded encounter by St Columba in 565 AD. After a supposed photograph was taken in 1933, public interest in some sort of large, dinosaur-like creature making its home in the Highlands skyrocketed, and in the decades since the loch has been subjected to sonar scans, submersible hunts, hydrophone surveys, and enough photographs taken above and below the surface to wallpaper the Grand Canyon.

Let's say you're trying to pinpoint when a particular past event occurred, but your best possible estimate puts it only within a span of 10,000 years. Now imagine if something could shrink that window of "when" to just 30 years. That's the power of a new mathematical tool devised and tested by an international team of scientists, led by two from the University of Wisconsin-Milwaukee. The tool, a machine-learning algorithm honed by Abbas Ourmazd and Russell Fung, reduces timing uncertainties during changing events, improving accuracy by a factor of up to 300. It could have numerous applications, from dating past climate-change events with better precision to determining when molecular bonds form or break during chemical reactions lasting only a few quadrillionths of a second.

David Eagleman is one of those rare writers who's as likable in person as he is in his books. His 20-year career as a neuroscientist has been unusual; my personal introduction to his work was his 2010 book Sum: Forty Tales from the Afterlives, which combined the bite-sized brilliance of Calvino's Invisible Cities with the wry pathos of Borges. Eagleman was recently the writer and presenter of The Brain, a six-part PBS television series that beautifully illuminates "the most complex object we've discovered in the universe." Eagleman holds joint appointments in the Departments of Neuroscience and Psychiatry at Baylor College of Medicine in Houston, Texas. Along with Sum, his books include The Brain: The Story of You (2015) and Incognito: The Secret Lives of the Brain (2012).

A full-scale figure of a Terminator "T-800" robot used in the movie "Terminator 2" is displayed at a preview of the Terminator Exhibition in Tokyo on March 18, 2009. UNITED NATIONS -- Technology allowing a pre-programmed robot to shoot to kill, or a tank to fire at a target with no human involvement, is only years away, experts say. A new report called Monday for a ban on such "killer robots." The report by Human Rights Watch and the Harvard Law School International Human Rights Clinic was released as the United Nations kicked off a week-long meeting on such weapons in Geneva. The report calls for humans to remain in control over all weapons systems at a time of rapid technological advances.

Researchers at Waseda University in Tokyo have created a way to realistically colorize black-and-white photos without any human intervention for the first time ever. The team's approach is based on convolutional neural networks -- a type of machine learning originally inspired by the visual cortex of a cat. The researchers used artificial intelligence to classify a full image and then identify parts of that image to label its components before filling them in with the appropriate colors. Previous research efforts in automated colorization fell short of being totally automatic. Most required users to provide a reference image that was similar to the black-and-white image in order to colorize it properly.