The push for computer science education and computational thinking in K-12 schools is spreading across the nation, but many districts struggle with equity issues as they ensure economically disadvantaged students and students with special needs have access to the same resources.

With guidance from the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, nearby Adelphi University just added a new minor in scientific computing—the use of computers to solve real-world science problems. Students enrolled in the minor will begin taking classes this fall, and the hope is that they will join the computing workforce of the future on Long Island, New York.

Contrary to current trends, the CPU may get bigger in the future. Yes, the size of CPUs are larger today than they were in the past, but they also pack in more transistors. The future may involve larger CPUs but with a much lower density of transistors. Why? Because of optics.

It’s hard to argue that any area of technology is evolving as rapidly as machine learning (ML) is nowadays. In a previous blog, I theorized that artificial intelligence (AI) was at an inflection point in its maturity, where it could benefit from many of the lessons learned in both the hardware and software realms of high performance computing (HPC). Since then, AI frameworks and systems architecture have come a long way, and much of what I mentioned in my earlier blog is now almost a given.

Artificial intelligence (AI), a branch of computer science that is transforming scientific inquiry and industry, could now speed the development of safe, clean and virtually limitless fusion energy for generating electricity. A major step in this direction is under way at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University, where a team of scientists working with a Harvard graduate student is for the first time applying deep learning.

Artificial intelligence can share our natural ability to make numeric snap judgments. Researchers observed this knack for numbers in a computer model composed of virtual brain cells, or neurons, called an artificial neural network. After being trained merely to identify objects in images — a common task for AI — the network developed virtual neurons that respond to specific quantities.

Only a few years ago, web search was simple. Users typed a few words and waded through pages of results. Today, those same users may instead snap a picture on a phone and drop it into a search box or use an intelligent assistant to ask a question without physically touching a device at all. They may also type a question and expect an actual reply, not a list of pages with likely answers. These tasks challenge traditional search engines, which are based around an inverted index system that relies on keyword matches to produce results.

Synthetic proteins have been created that move in response to their environment in predictable and tunable ways. These motile molecules were designed from scratch on computers, then produced inside living cells. To function, natural proteins often shift their shapes in precise ways. For example, the blood protein hemoglobin must flex as it binds to and releases a molecule of oxygen. Achieving similar molecular movement by design, however, has been a long-standing challenge.

MIT researchers have developed a computational model that could enhance video game simulations designed to facilitate training for teachers and students who might encounter racial issues in the classroom. Credit: screenshot from "Passage Home VR" Video games that use virtual reality to create immersive experiences have become increasingly popular for entertainment and for research. However, the representation of race in these simulations is often shallow—and fails to go beyond physical appearance attributes like skin color.

Most AI agents -- computer systems that could endow robots or other machines with intelligence -- are trained for very specific tasks -- such as to recognize an object or estimate its volume -- in an environment they have experienced before, like a factory. But the agent developed by Grauman and Ramakrishnan is general purpose, gathering visual information that can then be used for a wide range of tasks.

CodePath.org today published early outcomes from its free software development courses, which were designed to close equity gaps in tech by providing access to industry-relevant software development education to students outside of the elite institutions where tech employers typically recruit. Women and students of color who complete CodePath.org courses are, according to their data, as likely to land a technical job or internship as their white, male peers.

In today’s economy, potential employees who know how to create and improve software are highly marketable, making the field of computer science increasingly popular among those hoping to land a well-paying job after graduation.

Don’t assume it will be years before you need to worry about AI in the curriculum you teach. Artificial intelligence already has seeped into nearly every facet of our lives. It’s been permeating the fabric of our world, quite literally.

Smartphones, tablets, laptops and desktops are increasingly commonplace classroom tools, and they're all linked to an array of educational resources thanks to cloud computing. Education-oriented cloud computing, which had an estimated market value of $8 billion in 2016, is projected to hit $25 billion by 2021. Flexible and cost-effective, it's been a boon to teachers and students alike, allowing them to assign and complete classwork over the internet.

As machine learning and AI continue their pattern of growth into virtually every industry, the lines between the human experience and technology become blurry. So where does data fit into human-centered design? How can data scientists work with UX professionals, graphic designers, and content marketers to design an intuitive, responsive site that incorporates creativity and user-focused elements to deliver what customers really want?

It is difficult to talk about AI at scale without invoking the decades of work that has happened in the supercomputing trenches. This is where much of the underlying GPU foundation was built, not to mention various other elements in storage, networks, and systems software to keep beefy CPUs and accelerators fed.

As cosmologists and astrophysicists delve deeper into the darkest recesses of the universe, their need for increasingly powerful observational and computational tools has expanded exponentially. From facilities such as the Dark Energy Spectroscopic Instrument to supercomputers like Lawrence Berkeley National Laboratory’s Cori system at the National Energy Research Scientific Computing (NERSC) facility, they are on a quest to collect, simulate, and analyze increasing amounts of data that can help explain the nature of things we can’t see, as well as those we can.

GCS in Germany has approved the allocation of 1.171 billion core hours of computing time to 13 outstanding national research projects. The researchers awarded computing time allocations on the German Tier 0/1 supercomputing systems Hazel Hen at HLRS, JUWELS at the Julich Supercomputing Centre, and SuperMUC-NG at LRZ for a period of 12 months.

Angela Kearns is a New Mexico State University senior graduating with a degree in computer science and mathematics. Last summer she interned at Nike, where she has a job waiting for her after she receives her diploma Saturday, May 11. She is among the growing number of students who have helped NMSU to rank 22nd among four-year public universities in the United States (which includes more than 200 institutions) for enrolling and graduating women in computer science according to a recent data analysis compiled by “The Chronicle of Higher Education.”

Everyone has experienced feelings of self-doubt or of not belonging at some point. Such impostor feelings are extremely common among those pursuing science, technology, engineering, and mathematics fields because the fast-paced nature of these disciplines fosters comparison and makes it easy to feel behind. Especially at a competitive, STEM-oriented university like UC San Diego, these feelings of impostor syndrome are likely to be more prevalent than many probably think.

Detecting gravitational waves doesn’t have the sort of bombshell panache it did when it first happened three years ago, but that doesn’t make it any less remarkable. These observatories—arguably the most sensitive instruments humans have ever built—keep teaching us about events in the universe that were hidden until very recently. We’ve studied pairs of black holes merging, neutron stars colliding into one-another, and now, we may have finally witnessed signs of a black hole slamming into a neutron star. That’s something scientists were never really sure was even possible.

MIT researchers have devised a method for assessing how robust machine-learning models known as neural networks are for various tasks, by detecting when the models make mistakes they shouldn’t. Convolutional neural networks (CNNs) are designed to process and classify images for computer vision and many other tasks. But slight modifications that are imperceptible to the human eye — say, a few darker pixels within an image — may cause a CNN to produce a drastically different classification. Such modifications are known as “adversarial examples.”

Single-cell RNA sequencing is emerging as a powerful technology in modern medical research, allowing scientists to examine individual cells and their behaviors in diseases like cancer. But the technique, which can't be applied to the vast majority of preserved tissue samples, is expensive and can't be done at the scale required to be part of routine clinical treatment.

New artwork created by artificial intelligence does weird things to the primate brain. When shown to macaques, AI-generated images purposefully caused nerve cells in the monkeys’ brains to fire more than pictures of real-world objects. The AI could also design patterns that activated specific neurons while suppressing others, researchers report in the May 3 Science.

Not everyone fears our machine overlords. In fact, according to Penn State researchers, when it comes to private information and access to financial data, people tend to trust machines more than people, which could lead to both positive and negative online behaviors.