Lured by the prospect of high-salary, high-status jobs, college students are rushing in record numbers to study computer science. Now, if only they could get a seat in class. On campuses across the country, from major state universities to small private colleges, the surge in student demand for computer science courses is far outstripping the supply of professors, as the tech industry snaps up talent. At some schools, the shortage is creating an undergraduate divide of computing haves and have-nots — potentially narrowing a path for some minority and female students to an industry that has struggled with diversity.

For the past several years, I've been warning that the tech startup boom (and the surge of interest in "coding") is actually a dangerous bubble that is driven by the U.S. Federal Reserve's ultra-loose monetary policies since the Great Recession.The tech frenzy can be seen in the chart of the monthly count of global VC deals that raised $100 million or more since 2007. According to this chart, a new “unicorn” startup was born every four days in 2018.

IBM conceded Tuesday its artificial intelligence-powered Project Debater lost a competition to a human debate champion but said the experience was an important milestone in efforts to get computers to master human language. In the first live, public debate before a large, in-person and online audience, the declared winner was Harish Natarajan, who holds the record for most debate competition victories, IBM said in a blog post.

Artificial Intelligence (AI) translates documents, suggests treatments for patients, makes purchasing decisions and optimises workflows. But where is its moral compass? A study by the Centre for Cognitive Science at TU Darmstadt shows that AI machines can indeed learn a moral compass from humans. The results of the study have been presented at this year's ACM/AAAI Conference on AI, Ethics, and Society (AIES). AI has an increasing impact on our society. From self-driving cars on public roads, to self-optimising industrial production systems, to health care – AI machines handle increasingly complex human tasks in increasingly autonomous ways.

In the basement of MIT's Building 3, a robot is carefully contemplating its next move. It gently pokes at a tower of blocks, looking for the best block to extract without toppling the tower, in a solitary, slow-moving, yet surprisingly agile game of Jenga. The robot, developed by MIT engineers, is equipped with a soft-pronged gripper, a force-sensing wrist cuff, and an external camera, all of which it uses to see and feel the tower and its individual blocks.

In today's science and business worlds, it's increasingly common to hear that solving big problems requires a big team. But a new analysis of more than 65 million papers, patents and software projects found that smaller teams produce much more disruptive and innovative research. In a new paper published by Nature, University of Chicago researchers examined 60 years of publications and found that smaller teams were far more likely to introduce new ideas to science and technology, while larger teams more often developed and consolidated existing knowledge.

More than 38,000 children were forced to participate in Liberia’s second civil war, which lasted 14 years and claimed some 250,000 lives before ending in 2003. The West African country slowly continues to rebuild basic infrastructures, including education. Toward that end, the United Nations Children’s Fund (UNICEF) has been working with researchers at the San Diego Supercomputer Center (SDSC) as well as other parts of UC San Diego to determine the location of existing Liberian schools so they can provide them with resources and work with policy makers to plan for future schools in the country.

Many areas of science and engineering try to predict how an object will respond to a stimulus — how earthquakes propagate through the Earth or how a tumor will respond to treatment. This is difficult even when you know exactly what the object is made of, but how about when the object's structure is unknown? The class of problems that deal with such cases is known as inverse modeling. Based on information often gleaned at the surface — for instance, from ultrasound devices or seismometers — inverse modeling tries to determine what lies below, whether it is the size of a tumor or a fault in the Earth.

“Data Scientist: The Sexiest Job of the 21st Century.” So proclaimed the Harvard Business Review in 2012. Six years later and the job of a data scientist has only grown sexier. More employers than ever are looking to hire data scientists. Yet while the supply of data science job seekers is growing, it’s far outstripped by the rising demand in postings, meaning there potentially may not be enough skilled applicants. So the bargaining power in data science remains with the job seekers, according to new Indeed research.

Moore’s Law has underwritten a remarkable period of growth and stability for the computer industry. The doubling of transistor density at a predictable cadence has fueled not only five decades of increased processor performance, but also the rise of the general-purpose computing model. However, according to a pair of researchers at MIT and Aachen University, that’s all coming to an end.

Whether it is designing the most effective method for fuel injection in engines, building machinery to water acres of farmland, or painting a car, humans rely on liquid sprays for countless industrial processes that enable and enrich our daily lives. To understand how to make liquid jet spray cleaner and more efficient, though, researchers focus on the little things: Scientists must observe fluids flowing in atomic, microsecond detail in order to begin to understand one of science’s great challenges—turbulent motion in fluids.

The United States is making exciting changes to how computer models will be developed in the future to support the nation’s weather and climate forecast system. NOAA and the National Center for Atmospheric Research (NCAR) have joined forces to help the nation’s weather and climate modeling scientists achieve mutual benefits through more strategic collaboration, shared resources and information.

The Journal of Computational Science Education is pleased to announce the publication of the special issue on HPC training and education. This issue contains the articles from the First Workshop on Strategies for Enhancing HPC Education and Training (SEHET18) at PEARC18 and the Fifth SC Workshop on Best Practices for HPC Training and Education at SC18. The articles touch on both formal and informal approaches to high performance computing and computational science education and training.

It’s no secret that employers are looking for data scientists. Businesses of all sizes have woken up to the fact that data science has the potential to drive efficiencies, mine new insights from decades of accumulated data sets, and otherwise transform their businesses. From Zillow’s home price predictions to Amazon’s recommendation engines, applications of data science have become increasingly prevalent and high-profile.

It took a very, very long time, but if current conditions persist, we could see a server market that rakes in more than $100 billion next year. That is a crazy amount of infrastructure, and is a reflection not only of the rising cost of machines, which are being jam-packed with expensive processors, memory, flash, and sometimes GPU or FPGA accelerators, but in the tremendous amount of computing that modern applications take and, presumably, the even larger value that organizations are deriving from these vital engines of commerce.

Researchers at the University of Sussex have created the fastest and most energy efficient simulation of part of a rat brain using off-the-shelf computer hardware. Dr James Knight and Prof Thomas Nowotny from the University of Sussex’s School of Engineering and Informatics have beaten a top 50 supercomputer by running brain simulations using their own GeNN software and Graphics Processing Units (GPUs).

The world’s fastest supercomputer is now up and running production workloads at ORNL. "A year-long acceptance process for the 200-petaflop Summit supercomputer at Oak Ridge National Lab is now complete. Acceptance testing ensures that the supercomputer and its file system meet the functionality, performance, and stability requirements agreed upon by the facility and the vendor." To successfully complete acceptance, the Oak Ridge Leadership Computing Facility (OLCF) worked closely with system vendor IBM to test hundreds of system requirements and fix any resulting hardware, software, and network issues.

The Pittsburgh Supercomputing Center (PSC) is now supplying computation for the world’s most powerful particle collider. Fermi National Accelerator Laboratory (Fermilab) scientists working on the Compact Muon Solenoid (CMS) experiment, in collaboration with the Open Science Grid, have begun analyzing data from the Large Hadron Collider (LHC) using PSC’s Bridges supercomputer.

We’ve all heard the expression that ‘data is the new gold,’ and there’s no doubt that customer data in particular can fundamentally transform a business. However, data is rarely stored, located or created in one place—and it’s really hard to know what data does exist to have a holistic and accurate view of a business. Any business executive with accountability to the bottom line will tell you, the success of a strategic initiative is dependent on accurate and timely information.

There are two key barriers to fully exploring the emerging use of Field Programmable Gate Arrays (FPGAs) at the scale found in data centers: capital investment for private equipment, and gaining full, unfettered access to shared equipment. Derek Chiou, and his colleagues at the Texas Advanced Computing Center (TACC), are on a mission to change all that. They hope to spur ground breaking research for applying FPGAs.

The world’s largest neuromorphic supercomputer designed and built to work in the same way a human brain does has been fitted with its landmark one-millionth processor core and is being switched on for the first time. The newly formed million-processor-core ‘Spiking Neural Network Architecture’ or ‘SpiNNaker’ machine is capable of completing more than 200 million million actions per second, with each of its chips having 100 million moving parts.

Researchers and staff from 15 National Labs will showcase DOE’s latest computing and networking innovations and accomplishments at SC18 in Dallas next week. Computational scientists working for DOE laboratories have been involved in the conference since organizing the first meeting in 1988 and this year’s event is no different.

Is computer science all about mathematics? Your question is subtle; so any short answer is probably wrong. Programmers who excelled in math, assert that you must know math to be a good programmer; programmers who were not strong math students, assert equally strongly that you don’t need to know math.

As the University of Washington’s computer science program has grown, so too has the breadth of problems that its students are trying to solve. That variety was on full display Thursday evening on campus, as projects focused on healthcare, cloud computing, augmented and virtual reality and much more were honored. A project called Embarker, which focuses on identifying genes that can be used as markers that could predict Alzheimer’s Disease, took home the 13th annual Madrona Prize for the project with the most commercial potential at the Paul G. Allen School of Computer Science and Engineering’s Poster and Demo Session as part of the 2018 Industry Affiliates Annual Research Day Thursday night.

Expectations for artificial intelligences are very real and very high. An analysis in Forbes projects revenues from A.I. will skyrocket from $1.62 billion in 2018 to $31.2 billion in 2025. The report also included a survey revealing 84 percent of enterprises believe investing in A.I. will lead to competitive advantages.