Many animal species use vocal communication1, but the chaotic nature of the acoustics of these vocalizations often complicates their cataloging into clearly divided types and contexts2. Understanding the encapsulated information in animal vocalizations is central to the study of sociality, communication, and language evolution. Yet, in the research of nonhuman animals, the purpose and meaning of the vocal message often remain obscure. Researchers of animal communication, seeking homology to semantics, may relate behavioral observations to acoustic measurements, and thus reveal some of the information content of vocal expressions. Indeed, several studies have described cases of vocalizations as functionally referential, i.e. signals which are both specific to a certain context and elicit an appropriate response by a receiver3,4,5.

Apple has published its first academic research paper, and it's going to help A.I. get smart by exploring virtual lands. The breakthrough is all about making it easier to train computers to recognize the contents of a photo. Publishing research is a new approach for Apple, and it could help improve A.I. services like Siri that really struggle compared to its competitors. Starting with iOS 10, Apple scans your iPhone's images to make them easier to find, without using tags. Searching "dog," for example, will bring up all your pictures of dogs.

Expedia is the most recognisable brand in the world of online travel and owns several others, including Hotels.com and Trivago. Its companies operate more than 100 branded points of sale in over 60 countries. As a parent company, Expedia has made a steady flow of acquisitions over the past 15 years, and last year stepped up its M&A strategy with the takeovers of online travel agencies Travelocity and Orbitz, and holiday rental website HomeAway. A major goal in this M&A activity is to control and maintain Expedia's market-leading position in an increasingly competitive market for online travel booking, and is reflective of a general industry trend towards consolidation. Expedia was the first online travel giant and has been at the forefront of the transition in the way people book holidays, but that counts for little in the disruptive world of digital.

Robots are omnipresent in pop culture. Since the term was coined nearly a century ago, robots have played the role of sidekick, villain, and protagonist in some of the greatest science fiction works of all time. But there's a lot that books and movies get wrong about our mechanical companions. Here are 11 myths about robots that your favorite TV shows and films have helped spread. It's hard not to associate robots with visions of the future, but we've been building artificial helpers to complete tasks for us for thousands of years.

As we move into 2017 there is probably a long list of items that you are looking forward to tackling next year. In the past few months, we have witnessed some fascinating shifts in our national and global priorities and we are beginning to plan - as individual professionals, teams, leaders, organizations, and communities – how we will navigate the changing economic, social and political landscapes. Beyond the widely discussed political changes, there is also a host of technology trends and drivers that are working to redefine nearly every aspect of our lives. As we approach 2017, I wanted to share a few thought-provoking resources that I consider to be must-reads. Although there are many subjects worth exploring over the next few months, I wanted to highlight one area that is becoming exceedingly important.

The company has been a leader when it comes to digital technology, and it plans to keep pushing the bar higher. Starbucks (NASDAQ:SBUX) has led the way for not just fast-casual restaurants, but all of retail when it comes to using customer-facing technology in its stores. The company was the first major chain to integrate digital payment into its app, making it a common sight to see people pay by holding up their phones to a scanner. That happened well before payment via phone become a relatively common thing, and it forced other chains to follow. Starbucks also led the way with Mobile Order & Pay.

There is no question that the portability and omnipresence of cameras in today's society has improved driver safety -- video of a vehicle crash helps people find out specifically what went wrong. But what if you could impart artificial intelligence into those camera systems in vehicles, and predict problems on the road and prevent disaster? Netradyne's Driver-I technology uses machine learning to predict and prevent accidents in the commercial transportation industry San Diego, California-based Netradyne has developed technology designed to do just that, integrating cameras and deep learning with their Driver-i, a "vision based" system, mounted in or on commercial vehicles. Rather than merely recording events triggered by the vehicle's movements, Driver-i uses a TeraFLOP processor - one trillion calculations per second - connected to cameras to identify information such as road signs, traffic lights by color, pedestrians, other vehicles, following distance, tailgating, lane prediction and even weather to learn about driving conditions. Sandeep Pandya, president of Netradyne, said he and his colleagues envisioned a driver safety system that was one step beyond simple recording.

Apple's first public research paper on AI was penned by vision expert Ashish Shrivastava and a team of engineers including Tomas Pfister, Oncel Tuzel, Wenda Wang, Russ Webb and Apple Director of Artificial Intelligence Research Josh Susskind, appleinsider.com Shrivastava holds a PhD in computer vision from the University of Maryland. Titled'Learning from Simulated and Unsupervised Images through Adversarial Training', the paper describes techniques of training computer vision algorithms to recognise objects using synthetic, or computer generated, images. However, learning from synthetic images may not achieve the desired performance owing to a gap between synthetic and real image distributions. To reduce this gap, Apple has proposed Simulated plus Unsupervised (S U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabelled real data while preserving the annotation information from the simulator.

Each of these trucks is the size of a small two-story house. None has a driver or anyone else on board. Mining company Rio Tinto has 73 of these titans hauling iron ore 24 hours a day at four mines in Australia's Mars-red northwest corner. At this one, known as West Angelas, the vehicles work alongside robotic rock drilling rigs. The company is also upgrading the locomotives that haul ore hundreds of miles to port--the upgrades will allow the trains to drive themselves, and be loaded and unloaded automatically.

Stacking (also called meta ensembling) is a model ensembling technique used to combine information from multiple predictive models to generate a new model. Often times the stacked model (also called 2nd-level model) will outperform each of the individual models due its smoothing nature and ability to highlight each base model where it performs best and discredit each base model where it performs poorly. For this reason, stacking is most effective when the base models are significantly different. Here I provide a simple example and guide on how stacking is most often implemented in practice. Feel free to follow this article using the related code and datasets here in the Machine Learning Problem Bible.