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."

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.

This report on evaluating machine learning models arose out of a sense of need. The content was first published as a series of six technical posts on the Dato Machine Learning Blog. I was the editor of the blog, and I needed something to publish for the next day. Dato builds machine learning tools that help users build intelligent data products. In our conversations with the community, we sometimes ran into a confusion in terminology.

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).

The latest animated video from The School of Life gently postulates that the technology of the future will be able to read moods, detect emotional states and help humans communicate the nebulous world of feelings through the ever-advancing strides being made in artificial intelligence. One such emotional technology tool could be the yet-to-be-invented Socrates Mood Reader. Named after the world's greatest early philosopher Socrates who famously said that the first philosophical priority is to know yourself. Socrates will be a piece of wearable emotional technology that will make up for a failures of self-knowledge in real time. We imagine it as a kind of wearable life coach with the total understanding of our mental health, who we are and what we need to thrive emotionally at key moments.

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.

The Friday afternoon news dump, a grand tradition observed by politicians and capitalists alike, is usually supposed to hide bad news. So it was a little weird that Elon Musk, founder of electric car maker Tesla, and Sam Altman, president of famed tech incubator Y Combinator, unveiled their new artificial intelligence company at the tail end of a weeklong AI conference in Montreal this past December. But there was a reason they revealed OpenAI at that late hour. It wasn't that no one was looking. It was that everyone was looking. When some of Silicon Valley's most powerful companies caught wind of the project, they began offering tremendous amounts of money to OpenAI's freshly assembled cadre of artificial intelligence researchers, intent on keeping these big thinkers for themselves. The last-minute offers--some made at the conference itself--were large enough to force Musk and Altman to delay the announcement of the new startup.

"The robots are coming for your jobs!" That was the gist of numerous news reports following the release of the 2016 U.S. Economic Report of the President. My first thought on reading this was that anyone who saw the videos of clumsy robots falling helplessly during the recent DARPA Robotics Challenge must have been incredulous: "That's what's coming after my job!?" My second thought was more sobering. Robots are, after all, only a subset of the computerization leading to the automation of traditional jobs. As engineers we can see steady progress in machine learning, artificial intelligence, and big data.