With American student interest in STEM careers on the decline, a nationwide survey of 16-18 year old students suggests that out-of-the-box teaching, adding humor to the classroom, increasing participation in fun science projects and competitions, and relating math to real-life situations are key ways to draw more U.S. students to STEM subjects.

Media coverage of AI would have us believe that it is being driven at full speed by expert teams according to well-understood practices and policies. Most of you know that the reality is quite different. It’s really still the early days for AI, and in our experience, many companies aren’t yet AI-ready. Note that this isn’t a statement about data scientists nor algorithms. Data scientists know what they are doing, and most organizations have no cause to worry about the soundness of their machine learning (ML) algorithms.

Over the last two decades, hyperscale datacenters have reshaped the computing industry, making possible cloud computing, online retail, and media streaming on vast scales. But today’s centralized model is starting to yield to a much more distributed infrastructure that is being driven by a set of applications that must consume and process data where is generated.

Powered by CINECA’s Marconi supercomputer, EXSCALATE (EXaSCale smArt pLatform Against paThogEns) is accelerating the search for new drugs, setting a new record: a “chemical library” of 500 billion molecules, up to 30 biological targets evaluated simultaneously, with a processing capacity of more than 3 million molecules a second and a screening cost for 1 billion molecules equivalent to 4,000 euro.

Engineers at the University of California, Berkeley have built a new photonic switch that can control the direction of light passing through optical fibers faster and more efficiently than ever. This optical “traffic cop” could one day revolutionize how information travels through data centers and high-performance supercomputers that are used for artificial intelligence and other data-intensive applications.

Computer scientists at the University of California, Davis, and the California Institute of Technology have created DNA molecules that can self-assemble into patterns essentially by running their own program. The work is published March 21 in the journal Nature. “The ultimate goal is to use computation to grow structures and enable more sophisticated molecular engineering,” said David Doty, assistant professor of computer science at UC Davis and co-first author on the paper.

This week I had the opportunity to speak at the Northwest Quantum Nexus Summit, co-sponsored by Microsoft, the University of Washington and Pacific Northwest National Labs. The Summit brought together, for the first time, the large network of quantum researchers, universities and technology companies working in quantum information science (QIS) in our region to share quantum developments and to work together to establish the Pacific Northwest as one of the leading quantum science centers in the world.

Computing has quickly evolved to become the third “pillar” of science. But to reap its true rewards, researchers need software code that is flexible and can be easily adapted to meet new needs, as Benjamin Skuse finds out. Theory and experiment. They are the two pillars of science that for centuries have underpinned our understanding of the world around us. We make measurements and observations, which we then link to theories that describe, explain and predict natural phenomena.

Mobile computing is accelerating beyond the smartphone era. Today, people wear smart glasses, smart watches and fitness devices, and they carry smartphones, tablets and laptops. In a decade, the very same people are likely to wear or carry tens of wireless devices and interact with the internet and computing infrastructure in markedly different ways. Computer scientist Xia Zhou is working to make sure there are no traffic jams with the increased demand.

If you use a smartphone, laptop, or tablet, then you benefit from research in photonics, the study of light. At the University of Delaware, a team led by Tingyi Gu, an assistant professor of electrical and computer engineering, is developing cutting-edge technology for photonics devices that could enable faster communications between devices and thus, the people who use them.

The MIT Institute for Data, Systems, and Society (IDSS) convened professional data scientists, academic researchers, and students from a variety of disciplines for the third annual daylong Women in Data Science (WiDS) conference in Cambridge. WiDS Cambridge is one of many global satellite events of the WiDS conference at Stanford University, where attendees join a global community of data science researchers and practitioners. The conference is open to anyone interested in data science but strives especially to create opportunities for women in the field to showcase their work and network with each other.

A machine-learning model from MIT researchers computationally breaks down how segments of amino acid chains determine a protein's function, which could help researchers design and test new proteins for drug development or biological research.

Nowadays, we are continually facing mobility issues caused by the high number of circulating automobiles and people. These issues are more familiar to large cities, such as metropolis and megalopolis, but we can already note locomotion issues to a lesser extent in countryside cities. Lack of mobility is a problem that usually causes several other sub-problems, such as the lack of street mobility, increasing of prices related to the transportation of people and goods, as well as reducing the efficiency of time-sensitive services.

It's a big question for many people in traffic-dense cities like Los Angeles: When will self-driving cars arrive? But following a series of high-profile accidents in the United States, safety issues could bring the autonomous dream to a screeching halt.

In a paper published in Nature in February, a team headed by Caltech's Erik Winfree (PhD '98), professor of computer science, computation and neural systems, and bioengineering, showed how the DNA computations could execute six-bit algorithms that perform simple tasks. The system is analogous to a computer, but instead of using transistors and diodes, it uses molecules to represent a six-bit binary number (for example, 011001) as input, during computation, and as output.

When it comes to computer science skills, U.S. students approaching graduation have a significant advantage over their peers in China, India, and Russia. That’s the conclusion of a study published today in the Proceedings of the National Academy of Sciences of the United States of America. The study was put together by a global team of researchers led by Prashant Loyalka, an assistant professor at Stanford University. The team constructed a careful sampling mechanism to select senior (typically fourth year) computer science or equivalent students in each of the four countries, making sure that both the educational institutions and students enrolled at those schools were statistically representative of schools and computer science students throughout the respective nations.

A UC Berkeley undergraduate student’s tweet criticizing electrical engineering and computer sciences, or EECS, professor Anant Sahai recently gained traction among the campus community, raising questions regarding the pressure and high expectations computer science students face. On March 2, Claire Dubin, a UC Berkeley senior studying microbial biology and data science, posted a tweet expressing her frustration with the time commitment required of computer science students, highlighting a culture considered by many to be inconsiderate of student well-being and the time needed to work part-time jobs.

Colleges are investing significant time and money to create active-learning classrooms that support a new generation of pedagogical practices. Although institutions may be taking a cue from K–12 districts — where modern learning environments are thriving — most colleges will need to tweak the model to account for differences in higher education. For example, professors tend to share classrooms, so a single space may be used students in courses ranging from art history to biochemistry.

Teachers are always striving to show more and tell less when introducing students to new information, concepts, and skills. Education researcher Pauline Gibbons tells us, “Rather than trying to simplify information, amplifying the curriculum means finding as many ways as possible to make key information comprehensible.”

Despite how it may seem, artificial intelligence is not as recent of a development in the technology space as you would think. In fact, the idea of creating artificial neural networks that mirror those of a human brain was first introduced in the 1950s, and the topic has been continuously discussed in academia and private industry ever since.

In fact, a more salacious title might have said it will not be threat in most of our career spans, if in our lifetimes at all. For problems of high scientific value, some type of classical computer will be required unless there is a fundamental change in how algorithms are conceived from the ground up. And in traditional supercomputing, that is not going to happen either—again, at least not in the next decade or more. The practical HPC community is nose to the grindstone getting (mostly decades-old) codes prepared for exascale and perhaps an accelerator.

AI workloads are becoming ubiquitous, including running on the world’s fastest computers — thereby changing what we call HPC forever. As every organization plans for the future, AI workloads are on our minds — how do they affect programming, software needs, hardware demands, and training needs? In the upcoming year, specialists and AI experts will continue to come together to create new and innovative solutions. Here are the top ten ways that AI will most impact HPC in 2019.

Today ACM named Yoshua Bengio, Geoffrey Hinton, and Yann LeCun recipients of the 2018 ACM Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing. Bengio is Professor at the University of Montreal and Scientific Director at Mila, Quebec’s Artificial Intelligence Institute; Hinton is VP and Engineering Fellow of Google, Chief Scientific Adviser of the Vector Institute, and University Professor Emeritus at the University of Toronto; and LeCun is Professor at New York University and VP and Chief AI Scientist at Facebook.

Barbara Grosz has a fantasy that every time a computer scientist logs on to write an algorithm or build a system, a message will flash across the screen that asks, “Have you thought about the ethical implications of what you’re doing?” Until that day arrives, Grosz, the Higgins Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), is working to instill in the next generation of computer scientists a mindset that considers the societal impact of their work, and the ethical reasoning and communications skills to do so.

Dr. Nicki Washington doesn’t want to be known as a “unicorn” – someone who’s a rarity in his or her field. As the first Black woman to earn a Ph.D. in computer science from North Carolina State University, Washington feels a responsibility to be a wonderful example for those who will ideally come after her. “Not just because I’m the first, but because I’m one of the few,” said Washington, a Winthrop University associate professor of computer science.