It's high time for students to move beyond an hour of coding exercises and learn computational thinking. That's the message of a new report from Digital Promise that examines what's important to know and be able to do in a "computational world." Digital Promise is a non-profit that that promotes the use of innovation in education, particularly as it uses digital technologies.

Every IT organization wants a more scalable, programmable, and adaptable platform with real-time applications that can chew on ever-increasing amounts and types of data. And it would be nice if it could run in the cloud, too. Because of this, companies no longer think about databases, but rather are building or buying data platforms that are based on industry-standard technologies, big data tools like NoSQL and unified in a single place. It is a trend that started gaining momentum around 2010 and will accelerate this year, according to Ravi Mayuram, senior vice president of engineering and chief technology officer at Couchbase, one company that is building such a data platform for others to buy.

In the field of drug discovery and development, Prof Dr Chas Bountra believes that there are certain areas the pharmaceutical industry excels in and others that academicians have the advantage in. “Industry is brilliant at things that require scale and infrastructure. “So it’s great at doing high throughput screening with two million compounds; lead optimisation; identifying clinical molecules; doing toxicology, regulatory and the really big clinical studies – the phase 2b and phase 3; and then, marketing the drug,” he said.

In this video from the 2017 Intel HPC Developer Conference, Gadi Singer presents: The AI Future is Closer than it Seems. “Technology visionaries architecting the future of high-performance computing and artificial intelligence (AI) will share the key challenges as well as Intel’s direction. The talk will cover the adaptation of AI into HPC workflows, along their perspective architectural developments, upcoming transitions and range of solutions, technology opportunities, and the driving forces behind them."

The allure of capturing as much data as possible is strong. And, now that more businesses are experimenting with machine learning and AI, it’s growing stronger. When you aren’t sure what you may eventually need, might as well capture everything, right? But having more data isn’t always better — just ask Equifax. More data also means it gets harder to manage and gain valuable insights, and leverage workable data sets to accomplish specific tasks and achieve the desired outcomes.

If a community is resilient, it can withstand and recover from an unanticipated disaster, like an earthquake, fire or flood. But since every disaster and every community is unique, a uniform measure for defining "resilience" has been hard to come by for engineers and social scientists.

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. The bugs leave processors vulnerable to side channel attacks where malicious programs can steal information from applications’ memory. Worse news yet some of the fixes for these flaws are either unclear at this point or may be associated with significant slowdowns.

Artificial intelligence is now quite adept at crunching vast amounts of data and using it to draw conclusions. As a result, humans who rely solely on procedural thinking aren't as valuable as they once were. What we need now are abstract thinkers who are deeply immersed in the Big Ideas made possible through computation. But traditionally, that's not how universities have sought out, categorized, or educated computer science students.

“One man’s loss is another man’s gain.” That proverb means different things in different contexts. For recent PhD graduates who are struggling to find academic employment, many wonder “who will I be a gain to?” Most will not have a chance to apply their talents in academia. However, one sector of the economy that stands to gain from academia’s loss is technology.

Scientists from the Jülich Supercomputing Centre have set a new world record. Together with researchers from Wuhan University and the University of Groningen, they successfully simulated a quantum computer with 46 quantum bits – or qubits – for the first time. For their calculations, the scientists used the Jülich supercomputer JUQUEEN as well as the world’s fastest supercomputer Sunway TaihuLight at China’s National Supercomputing Center in Wuxi.

An international team of scientists — including several researchers from the U.S. Department of Energy’s (DOE) Argonne National Laboratory — has discovered an anode battery material with superfast charging and stable operation over many thousands of cycles.

Attempts to eradicate cancer are often compared to a "moonshot"—the successful effort that sent the first astronauts to the moon. But imagine if, instead of Newton's second law of motion, which describes the relationship between an object's mass and the amount of force needed to accelerate it, we only had reams of data related to throwing various objects into the air. This, says Thomas Yankeelov, approximates the current state of cancer research: data-rich, but lacking governing laws and models.

Researchers at the National Institute of Standards and Technology (NIST) have demonstrated that quantum physics might enable communications and mapping in locations where GPS and ordinary cellphones and radios don't work reliably or even at all, such as indoors, in urban canyons, underwater and underground.

A visit to a scientific-research center usually begins at a star professor’s laboratory that is abuzz with a dozen postdocs collaborating on various experiments. But when I recently toured the Flatiron Institute, which formally opened in September, in lower Manhattan, I was taken straight to a computer room. The only sound came from a susurrating climate-control system. I was surrounded by rows of black metal cages outfitted, from floor to ceiling, with black metal shelves filled with black server nodes: boxes with small, twinkling lights and protruding multicolored wires.

You might not suspect that the success of the emerging field of precision medicine depends heavily on the couriers who push carts down hospital halls. But samples taken during surgery may end up in poor shape by the time they get to the pathology lab — and that has serious implications for patients as well as for scientists who want to use that material to develop personalized tests and treatments that are safer and more effective.

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Exascale Computing Project leadership shuffling? AMD’s return from the dead in the data center? Scandal at PEZY? Aurora’s stumble? Trump? There’s lots to choose from. Whether you’re thinking ‘good riddance’ or ‘stay a little longer’ about 2017 – it feels like a year where there’s not a lot in between. It’s probably best focus on the future. Here are a few 2018 predictions mostly accenting the positive; indeed there is quite a bit to be positive about amid ever present dark clouds.

SCOTT GORDON HAD just arrived in his job as provost of Eastern Washington University when an alumnus approached him at a meet-and-greet in the Skyline Ballroom of Spokane’s Hotel RL. The event was new, too. Called the Eagle Summit after the public university’s athletics mascot, it was meant to build enthusiasm among the school’s supporters. That has become increasingly crucial at a time when Americans’ faith in higher education is declining, governments are investing less money in it, and employers complain it’s producing too few graduates with skills they need.

A group of researchers at Osaka University has developed a new method for dialogue systems*1. This new method, lexical acquisition through implicit confirmation, is a method for a computer to acquire the category of an unknown word over multiple dialogues by confirming whether or not its predictions are correct in the flow of conversation.

Increasing the number of women in engineering is a problem without clear boundary conditions. Although we know that no single solution can help address the challenges women face in navigating their studies and careers, the understanding we’ve gained in recent years can point the way to seeing real change.

A new type of neural network made with memristors can dramatically improve the efficiency of teaching machines to think like humans. The network, called a reservoir computing system, could predict words before they are said during conversation, and help predict future outcomes based on the present. The research team that created the reservoir computing system, led by Wei Lu, professor of electrical engineering and computer science at the University of Michigan, recently published their work in Nature Communications.

Despite much anticipation of valleytronics being a candidate for 'beyond CMOS' technology and to continue the legacy of Moore's law, its progress is severely hindered by the lack of practical designs for a valleytronic-based information processing unit. One major challenge in valleytronic is the construction of a "valley filter." Valley filter can produce electrical current composed dominantly of electrons from only one specific "valley." It serves as a fundamental building block in valleytronics.

Despite the massive advantages and benefits big data, machine learning and predictive analytics have to offer, data science is still a touchy subject for businesses of all sizes. Not only are many reluctant to adopt the related systems and hardware, but when they do make the leap, they lag when it comes to properly using the information collected. Poor data across businesses, organizations and the government contribute costs of up to $3.1 trillion a yearto the U.S. economy. To make matters worse, 14.9 percent of marketers claim they do not know what big data is, let alone how to use it. Both these stats show a general lack of knowledge when it comes to big data and data science.

Artificial intelligence and machine learning, which found solid footing among the hyperscalers and is now expanding into the HPC community, are at the top of the list of new technologies that enterprises want to embrace for all kinds of reasons. But it all boils down to the same problem: Sorting through the increasing amounts of data coming into their environments and finding patterns that will help them to run their businesses more efficiently, to make better businesses decisions, and ultimately to make more money.

Most scientific computing facilities, such us HPC or grid infrastructures, are shared among different research disciplines, and thus the system software environment needs to be generic enough to accommodate different user and applications profiles; they are multi-user environments. Because of managerial and technical constraints, such infrastructures cannot afford offering every research project a tailored environment in their machines. Therefore the interest of exploring the applicability of containers technology on such systems is rather evident from the end-user point of view.

Inquisitive minds want to know what causes the universe to expand, how M-theory binds the smallest of the small particles or how social dynamics can lead to revolutions. In recent centuries, developments in science and technology brought us closer to explore the expanding universe, discover unknown particles like bosons or find out how and why a society interacts and reacts. To explain the fascinating phenomena of nature, Natural scientists develop complex ‘mechanistic models’ of deterministic or stochastic nature. But the hard question is how to choose the best model for our data or how to calibrate the model given the data.