I’m not boring, I’m optimal. This is what computational cognitive scientist Tom Griffiths would say about my decision to eat the same thing at the same restaurant every time I dine out. In his TED talk on how humans can make better decisions by thinking like a computer, Griffiths explains that we can apply computer science to everyday life to make the best possible choices.

Walking among the rows of supercomputer cabinets in Argonne National Laboratory’s Leadership Computing Facility, located about 25 miles from Chicago, is kind of like wandering through a high-tech version of “The Shining’s” Overlook Maze — minus the axe-wielding madman.

A little over half a century ago, chaos started spilling out of a famous experiment. It came not from a petri dish, a beaker or an astronomical observatory, but from the vacuum tubes and diodes of a Royal McBee LGP-30. This “desk” computer — it was the size of a desk — weighed some 800 pounds and sounded like a passing propeller plane. It was so loud that it even got its own office on the fifth floor in Building 24, a drab structure near the center of the Massachusetts Institute of Technology.

When he first received grant funding from the National Science Foundation for a STEM-related after-school project, Assistant Professor Patrick Enderle planned to find pre-service teachers in math and science education programs at Georgia State to participate. But he found that pre-service teachers across the CEHD’s teacher education programs wanted to be part of the project, which was created to teach them about multicultural education and culturally-relevant teaching practices through instruction focused on computational thinking.

PhD students in India will no longer be required to publish articles in academic journals before they are awarded their doctorates, if the country’s higher-education regulator adopts recommendations from a committee of researchers.

Computational thinking is one of the biggest buzzwords in education—it’s even been called the ‘5th C’ of 21st century skills. While it got its start as a way to help computer scientists think more logically about data analysis, lately it’s been catching on with instructors in a diverse number of subjects—from science to math to social studies.

Anyone who says you can’t apply computational thinking and digital learning strategies to strengthen students’ writing skills is wrong. To the contrary, this innovative learning style helps students hone critical-thinking skills across every discipline.

Data science and related fields, including artificial intelligence, business intelligence, and big data, are seeing tremendous growth. Data is important in numerous industries, from healthcare to transportation, making data scientists a must-have role in most companies. As more technology emerges, even more data can be collected, which only increases the need for experts.

When the Square Kilometer Array (SKA) is deployed in the next decade, alongside it will be a supercomputer tasked to process the deluge of astronomical data that the instrument will collect from the skies. And even though the SKA will be the world’s most powerful radio telescope, it won’t quite need the world’s most powerful supercomputer to fulfill its mission.

If you’ve spent much time cruising employment ads lately, you’ve probably noticed that certain research computing specializations are in high demand. Some university-based centers have had positions open for months; others years. It’s the same in densely-populated communities that compete with regional industries as it is elsewhere.

In this special guest feature from the SC19 Blog, Dan Jacobson and Wayne Joubert from ORNL describes how the Summit supercomputer is helping untangle how genetic variants, gleaned from vast datasets, can impact whether an individual is susceptible (or not) to disease, including chronic pain and opioid addiction.

At their core, data scientists have a math and statistics background. Out of this math background, they’re creating advanced analytics. On the extreme end of this applied math, they’re creating machine learning models and artificial intelligence. Just like their software engineering counterparts, data scientists will have to interact with the business side.

Imagine someone came along and told you that they had an oracle, and that this oracle could reveal the deep secrets of the universe. While you might be intrigued, you’d have a hard time trusting it. You’d want some way to verify that what the oracle told you was true.

As developing nations around the world become more industrialized, the thirst for oil is growing. Some experts forecast that the global demand for oil will exceed 100 million barrels per day by next year. Meeting this demand is a major challenge for oil companies, especially with many of the most readily available reserves of oil already tapped.

It was a marvel of engineering, a harbinger of a future of unimaginable computational power. It also bore a striking resemblance to a garbage can. Q System One was a quantum computer. The machine was the culmination of a year—or decades, depending on how one measures—of labor and ingenuity from IBM scientists.

Virtual Reality and the virtual world are taking over more and more areas of our lives. This means that it is really important that virtual worlds are user-friendly and offer a high usability. Up until now, the only way to check this was to conduct manual tests with volunteers. This can be both time-consuming and cost-intensive.

Physicists at the University of Basel are able to show for the first time how a single electron looks in an artificial atom. A newly developed method enables them to show the probability of an electron being present in a space. This allows improved control of electron spins, which could serve as the smallest information unit in a future quantum computer.

With aims of bringing more human-like reasoning to autonomous vehicles, MIT researchers have created a system that uses only simple maps and visual data to enable driverless cars to navigate routes in new, complex environments.

McMaster researchers have developed a simple and highly novel form of computing by shining patterned bands of light and shadow through different facets of a polymer cube and reading the combined results that emerge. The material in the cube reads and reacts intuitively to the light in much the same way a plant would turn to the sun, or a cuttlefish would change the color of its skin.

Most organizations are fully invested in AI but more than half don’t have the required in-house skilled talent to execute their strategy, according to new research from SnapLogic. The study found that 93% of US and UK organizations consider AI to be a business priority and have projects planned or already in production. However, more than half of them (51%) acknowledge that they don’t have the right mix of skilled AI talent in-house to bring their strategies to life.

The commercial realization of artificial intelligence has companies scrambling to develop the next big hardware technology breakthrough for this multi-billion dollar market. While research into areas like neuromorphic and quantum computing have captured most of the attention for future AI systems, the idea of using optical processors has slowly been gaining traction.

The University of Texas at Austin team that led a twin satellite system launched in 2002 to take detailed measurements of the Earth, called the Gravity Recovery and Climate Experiment (GRACE), reports in the most recent issue of the journal Nature Climate Change on the contributions that their nearly two decades of data have made to our understanding of global climate patterns.

Two years ago, ground-breaking research by Shen et al. at MIT proposed an intriguing path towards both lower latency and higher energy efficiency: optical neural networks (ONNs). At last week’s CLEO conference, and in a longer form paper in Optics Express, we and our collaborators at UC Berkeley presented new findings around ONNs, including a proposal for how that original work could be extended in the face of real-world manufacturing constraints to bring nanophotonic neural network circuits one step closer to a practical reality.

Nathaniel Throckmorton was ruminating on the zero lower bound and had reached a point at which he needed William & Mary’s giant abacus. The zero lower bound, or ZLB, is the state at which short-term interest rates offered by the U.S. Federal Reserve or other central bank hit or come near to rock bottom — zero. Throckmorton and some colleagues at the Federal Reserve Bank of Dallas were working with a ZLB model, and the model came with a conundrum.

More than 1,800 teen researchers came to the Valley of the Sun this week. Those who shone brightest took home big prizes from the Intel International Science and Engineering Fair, or ISEF. Top winner Krithik Ramesh earned $75,000. He developed a system to help spinal surgeons perform operations more accurately and more quickly. At its heart is a Pokémon Go–like augmented-reality system.