Don't Be Fooled by This Giant 'Mech' Robot:

New video clips purporting to show a 13-foot-tall (4 meters) humanoid robot piloted by a person in its torso look like something straight out of "Avatar" or "Transformers," but a Live Science investigation has revealed reasons to believe some skepticism might be in order. 

The robot clips have been picked up by a variety of online news and technology outlets, including Kotaku and Wired UK. But the South Korean company that is supposedly developing the robot has virtually no online presence and was unfamiliar to robotics researchers contacted by Live Science.

Furthermore, the only source for the videos or any information about them is the Facebook and Instagram pages of a designer whose website mentions a conceptual art project about a "fictional robotics corporation that develops its products in a not-so-distant future."

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Ups & Downs: The Evolution of Elevators:

The need to move things to the next level has been recognized for thousands of years. Elevators have a long history, going from a platform attached to a rope pulled by a human to the smooth, electric rides in boxes that we now enjoy.

Invention and evolution

Vertical lifts may have been used to build the pyramids in Egypt. However, the first recorded use came in the third century B.C., according to Elevator History. Archimedes, the Greek mathematician, physicist and astronomer, is typically credited with inventing the first known elevator, according to Landmark Elevator. His device was operated by ropes and pulleys. The ropes were coiled around a winding drum by a capstan and levers, according to Otis World Wide. These early lifts, or hoists, powered by people, animals or water, were primarily used to lift heavy loads, such as water or building materials.

Crude elevator systems lifted people as early as the first century A.D, according to Otis. The Roman Coliseum used lifts to raise gladiators and wild animals up from the lower levels to the arena level. In medieval times, hoists were the only way to get to the monastery in St. Barlaam, Greece, which stood on a pinnacle about 200 feet (60 meters) off the ground.

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IBM's Watson Turns Its Computer Brain to NASA Research:

IBM's question-answering whiz, the Watson computer system, famously beat former winners on Jeopardy in 2011 — and now it's digging into aerospace research and data to help NASA answer questions on the frontier of spaceflight science and make crucial decisions in the moment during air travel.

More than 60 years after the first IBM computing machines showed up in the halls of NASA's Langley Research Center, new work at Langley will use IBM tech to help researchers sort through the huge volumes of data that is generated by aerospace research.

"There's so much data out there that consists of unstructured text that usually only humans can make sense of, but the challenge is that there's too much of it for any human being to read," Chris Codella, an IBM Distinguised Engineer who is working on Watson, told Space.com. "The idea here is to have a Watson system that can be a research development advisor to people who work in the aerospace fields." [Forget Jeopardy: 5 Abilities That Make IBM's Watson Amazing]

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New Flying Robots Take Cues From Airborne Animals:

From navigating turbulence, to sleeping midflight, to soaring without a sound, animals' flight adaptations are helping scientists design better flying robots.Airborne drones and the animals they mimic are featured in 18 new studies published online Dec. 15 in the journal Interface Focus. This special issue is intended "to inspire development of new aerial robots and to show the current status of animal flight studies," said the issue's editor, David Lentink, an assistant professor of mechanical engineering at Stanford University in California.Though humans have been building flying machines since the 18th century, these new studies revealed that there is still much to be learned from looking closely at how birds, insects and bats take flight, keep themselves aloft and maneuver to safe landings.

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8 Ways Animal Flight Inspires Drone Designs:

Flight of the drones

How do scientists build better flying robots? They look to the natural world for inspiration, investigating the adaptations that allow winged animals to efficiently navigate through the air, even under difficult conditions.

Today's aerial drones are more sophisticated than ever, and will likely continue to improve in performance as scientists uncover more of the secrets to insects', bats' and birds' flying success.

Here are some examples of the latest discoveries in animal flight research and bio-mimicking drones, from studies published Dec. 16, 2016, in the journal Interface Focus.

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Who Invented the Traffic Light?

Traffic lights, or traffic signals, are located on most major corners in cities and towns around the world. The red, yellow and green lights let us know when it is safe to drive through the intersection and when to walk across the street as well as when to stop and let other drivers, bikers and pedestrians take their turns to continue on their way.

The first traffic signal

Traffic jams were a problem even before the invention of the automobile. Horse-drawn carriages and pedestrians crowded the roads of London in the 1860s, according to the BBC. A British railway manager, John Peake Knight, suggested adapting a railroad method for controlling traffic.

Railroads used a semaphore system with small arms extending from a pole to indicate whether a train could pass or not. In Knight's adaptation, semaphores would signal "stop" and "go" during the day, and at night red and green lights would be used. Gas lamps would illuminate the sign at night. A police officer would be stationed next to the signals to operate them.

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Jingle Bytes? Artificial Intelligence Writes a Christmas Song:

You might find yourself wishing for a silent night after you hear the first Christmas carol written by artificial intelligence.

The new tune makes its holiday season debut courtesy of a team of computer scientists in the Department of Computer Science at the University of Toronto. The researchers fed 100 hours of pop songs to a type of artificial intelligence (AI) known as a recurrent neural network, which learns and performs by building connections between input data, much like the human brain does.

In this case, the uploaded songs taught the neural network about the general structure of pop music. The researchers then tested its ability to generate a song about an image — a decorated Christmas tree surrounded by wrapped presents — using a process called "neural story singing," which they described in a study currently under review for a conference presentation. [Super-Intelligent Machines: 7 Robotic Futures]

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Forget Selfie Sticks: This Drone Captures Photos and Videos in Midair:

The drone's four rotors help it fly up to 65 feet (20 meters) in the air. The flying camera measures only about 3.72 by 2.65 by 0.42 inches (9.45 by 6.73 by 1.07 centimeters) — "smaller than a smartphone," Stroppiana said — and weighs 1.83 ounces (52 grams).The drone uses sonar to measure its altitude and keeps itself stable with the help of a tiny extra camera to monitor its surroundings for signs of jitter. It is also equipped with gyroscopes, barometers and geomagnetic sensors that help it navigate as it flies, said AirSelfie Holdings Ltd. in London, the company that Stroppiana co-founded in 2016 to manufacture the drone.The AirSelfie is controlled via a free iOS or Android app. The app can make the drone take off; adjust its height and direction; let it hover autonomously; and help users take an HD aerial shot or video with just a push of a button. Users can also activate a 10-second timer, giving people enough time to hide their phones so they don't appear in the picture or video. The drone can take up to eight consecutive shots, the company said.

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'Star in a Jar' Fusion Reactor Works and Promises Infinite Energy:

For several decades now, scientists from around the world have been pursuing a ridiculously ambitious goal: They hope to develop a nuclear fusion reactor that would generate energy in the same manner as the sun and other stars, but down here on Earth.

Incorporated into terrestrial power plants, this "star in a jar" technology would essentially provide Earth with limitless clean energy, forever. And according to new reports out of Europe this week, we just took another big step toward making it happen.

In a study published in the latest edition of the journal Nature Communications, researchers confirmed that Germany's Wendelstein 7-X(W7-X) fusion energy device is on track and working as planned. The space-age system, known as a stellerator, generated its first batch of hydrogen plasma when it was first fired up earlier this year. The new tests basically give scientists the green light to proceed to the next stage of the process.

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Unlock the cloud’s full value: Make devops mandatory:

Most people who are good with cloud technology are also good with devops. That’s not an accident: It’s impossible to get the full value out of cloud computing unless it’s done in the context of automated devops.

Why is automated devops so important? It’s a competitive advantage that creates faster time to market. Organizations that require weeks or month to deploy software are at a distinct disadvantage.

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The best hardware, software, and cloud services:

InfoWorld’s Technology of the Year Awards have celebrated the most important technology trends and the best IT products for 15 years now. Our awards have marked the rise of everything from 64-bit hardware to hardware virtualization, from Java servers to JavaScript servers, from XML Web services to REST APIs, and from Microsoft Word for Windows to Microsoft Word for iOS. We’ve seen a lot of changes.

And the changes keep coming. Among this year’s winners, handpicked by InfoWorld editors and product reviewers, you’ll find a number of “traditional” names: Cisco, IBM, Microsoft, Red Hat. But you’ll also find the names of more open source projects than we’ve ever seen in the Technology of the Year winner’s circle, thanks to the huge role open source has come to play in software development, data center (and cloud) infrastructure, and big data analytics.

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Giant machine shows how a computer works:

A giant, fully operational 16-bit computer that aims to demystify the strange and seemingly magical mechanisms of computation has been built by students and staff from the University of Bristol.

The Big Hex Machine, specifically designed to explain how a computer works, has been built out of over 100 specially designed four-bit circuit boards and will enable students to be taught about the fundamental principles of computer architecture from just a few basic components.

The computer - 'an ultimate teaching tool' - will be used as part of this year's computer architecture unit and will be an invaluable resource to enable students to get creative with what is traditionally seen as a complicated subject. The machine's instruction set requires a very small compiler, but it is powerful enough to implement useful programs.

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 Safer, less vulnerable software is the goal of new computer publication:

We can create software with 100 times fewer vulnerabilities than we do today, according to computer scientists at the National Institute of Standards and Technology (NIST). To get there, they recommend that coders adopt the approaches they have compiled in a new publication.

The 60-page document, NIST Interagency Report (NISTIR) 8151: Dramatically Reducing Software Vulnerabilities (link is external), is a collection of the newest strategies gathered from across industry and other sources for reducing bugs in software. While the report is officially a response to a request for methods from the White House's Office of Science and Technology Policy, NIST computer scientist Paul E. Black says its contents will help any organization that seeks to author high-quality, low-defect computer code.

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Team finds new method to improve predictions:

Researchers at Princeton, Columbia and Harvard have created a new method to analyze big data that better predicts outcomes in health care, politics and other fields.

The study appears this week in the journal Proceedings of the National Academy of Sciences.In previous studies, the researchers showed that significant variables might not be predictive and that good predictors might not appear statistically significant. This posed an important question: how can we find highly predictive variables if not through a guideline of statistical significance? Common approaches to prediction include using a significance-based criterion for evaluating variables to use in models and evaluating variables and models simultaneously for prediction using cross-validation or independent test data.

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Suggestions for you: A better, faster recommendation algorithm

The internet is rife with recommendation systems, suggesting movies you should watch or people you should date. These systems are tuned to match people with items, based on the assumption that similar people buy similar things and have similar preferences. In other words, an algorithm predicts which items you will like based only on your, and the item's, previous ratings.

But many existing approaches to making recommendations are simplistic, says physicist and computer scientist Cristopher Moore, a Santa Fe Institute professor. Mathematically, these methods often assume people belong to single groups, and that each one group of people prefers a single group of items. For example, an algorithm might suggest a science fiction movie to someone who had previously enjoyed another different science fiction movie— - even if the movies have nothing else in common.
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US Military Develops 'Multi-Object Kill Vehicle' to Blast Enemy Nukes:

Defensive weapons that can intercept and destroy enemy missiles before they can harm the United States or its allies have been a key part of military strategy for decades, but the rules of the game are changing.

More countries have or are developing long-range missile technology, including systems that can carry multiple warheads, known as Multiple Independently Targetable Re-entry Vehicles (MIRVs) and/or decoys.
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Making it easier to collaborate on code:

Git is an open-source system with a polarizing reputation among programmers. It's a powerful tool to help developers track changes to code, but many view it as prohibitively difficult to use.

To make it more user-friendly, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed "Gitless," an interface that fixes many of the system's core problems without fundamentally changing what it does.

"With Gitless we've developed a tool that we think is easier to learn and use, but that still keeps the core elements that make Git popular," says graduate student Santiago Perez De Rosso, who co-wrote a related paper with MIT Professor Daniel Jackson. "What's particularly encouraging about this work is that it suggests that the same approach might be used to improve the usability of other software systems, such as Dropbox and Google Inbox."

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Designing for 3-D printing:
“Foundry” tool from the Computer Science and Artificial Intelligence Lab lets you design a wide range of multi-material 3-D-printed objects.3-D printing has progressed over the last decade to include multi-material fabrication, enabling production of powerful, functional objects. While many advances have been made, it still has been difficult for non-programmers to create objects made of many materials (or mixtures of materials) without a more user-friendly interface.
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Making computers explain themselves:
New training technique would reveal the basis for machine-learning systems’ decisions.In recent years, the best-performing systems in artificial-intelligence research have come courtesy of neural networks, which look for patterns in training data that yield useful predictions or classifications. A neural net might, for instance, be trained to recognize certain objects in digital images or to infer the topics of texts.
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Let's Talk, Robots:
Today’s industrial robots are very well aligned with repeatable processes. They can do the same thing over and over again very accurately, and they are tireless. So, if you need 10,000 functions performed the exact same way each time, a robot might be right for your job.
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Liquid silicon: Computer chips could bridge the gap between computation and storage:
Computer chips in development at the University of Wisconsin-Madison could make future computers more efficient and powerful by combining tasks usually kept separate by design.
Jing Li, an assistant professor of electrical and computer engineering at UW-Madison, is creating computer chips that can be configured to perform complex calculations and store massive amounts of information within the same integrated unit—and communicate efficiently with other chips. She calls them "liquid silicon."
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Galactic Archeologists, Bone Scientists, and Others Sink Teeth into Hull’s Viper:
These and other researchers at the University of Hull in Yorkshire, England, are salivating at the opportunity to sink their teeth, as it were, into Viper, the first university-wide High-Performance Computing (HPC) cluster. Based on elements of the Intel Scalable System Framework—the latest Intel Xeon E5-2680v4 (Broadwell) processors and Intel Omni-Path Architecture—Viper gives Hull the opportunity to make its mark in academic research, says Graeme Murphy, head of research and enterprise ICT services.
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In a Sign of Broader Ambitions, Facebook Opens Hardware Lab:
Facebook built its fortune on the internet, that non-physical space where people share updates and digital videos with friends. But deep inside its Silicon Valley headquarters, engineers have stocked a new lab with computerized lathes, industrial mills and tools for making physical goods.
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Scalable Power Management for On-Chip:

Muhammad Shafique , Chair for Embedded Systems (CES), Karlsruhe Institute of Technology (KIT), Germany
Anton Ivanov, Chair for Embedded Systems (CES), Karlsruhe Institute of Technology (KIT), Germany
Benjamin Vogel, Chair for Embedded Systems (CES), Karlsruhe Institute of Technology (KIT), Germany
Jorg Henkel, Chair for Embedded Systems (CES), Karlsruhe Institute of Technology (KIT), Germany ABSTRACT
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Automating big-data analysis
With new algorithms, data scientists could accomplish in days what has traditionally taken months.
Last year, MIT researchers presented a system that automated a crucial step in big-data analysis: the selection of a “feature set,” or aspects of the data that are useful for making predictions. The researchers entered the system in several data science contests, where it outperformed most of the human competitors and took only hours instead of months to perform its analyses.
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Research Team Develops Plastic, Flexible Magnetic Memory Device:
It looks like a small piece of transparent film with tiny engravings on it, and is flexible enough to be bent into a tube. Yet, this piece of "smart" plastic demonstrates excellent performance in terms of data storage and processing capabilities. This novel invention, developed by researchers from the National University of Singapore (NUS), hails a breakthrough in the flexible electronics revolution, and brings researchers a step closer towards making flexible, wearable electronics a reality in the near future.
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LOS ALAMITOS, CA — The IEEE Computer Society has unveiled its Top 9 Technology Trends for 2016. According to Dejan Milojicic, past president, IEEE Computer Society, “Some of these trends will come to fruition in 2016, while others reach critical points in development during this year. You’ll notice that all of the trends interlock, many of them depending on the advancement of other technologies in order to move forward. Cloud needs network functional virtualization, 5G requires cloud, containers can’t thrive without advances in security, everything depends on data science, and so on. It’s an exciting time for technology and IEEE Computer Society is on the leading edge of the most important and potentially disruptive technology trends.”
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