Years in the making, a proposal to mandate the installation of fiber conduits during federally funded highway projects might be gaining some new momentum.
If the US adopts a “dig once” policy, construction workers would install conduits just about any time they build new roads and sidewalks or upgrade existing ones. These conduits are plastic pipes that can house fiber cables. The conduits might be empty when installed, but their presence makes it a lot cheaper and easier to install fiber later, after the road construction is finished.
Ambulances aren’t magical, self-maintaining machines. They must be stocked with bandages, defibrillators, and burn kits. Like any other car, they must have air in their tires, plenty of fuel, and working headlights. They are frequently inspected, and repaired or replaced when they break down. They are upgraded when new techniques for treating patients are developed. Ambulances cannot do these things for themselves; people are responsible for taking care of these important tools. When there are several ambulances in a fleet, there is usually a person who manages that fleet, ensuring that all of them are ready at a moment’s notice to help save lives.
Companies have now vacuumed up most of the available talent—and they need more. Until recently, deep learning was a fringe pursuit even in the academic world. Relatively few people are formally trained in these techniques, which require a very different kind of thinking than traditional software engineering. So, Facebook is now organizing formal classes and longterm research internships in an effort to build new deep learning talent and spread it across the company. “We have incredibly smart people here,” Zitnick says. “They just need the tools.”
AI researchers are among the most prized talent in the modern tech world. A few years ago, Peter Lee, a vice president inside Microsoft Research, said that the cost of acquiring a top AI researcher was comparable to the cost of signing a quarterback in the NFL. Since then, the market for talent has only gotten hotter. Elon Musk nabbed several researchers out from under Google and Facebook in founding a new lab called OpenAI, and the big players are now buying up AI startups before they get off the ground.
New York City is bringing residents into the smart city planning process with the announcement that Brownsville, in Brooklyn, will be the first to have its own Neighborhood Innovation Lab.
The purpose of the lab is to gather residents, educators, tech companies, government officials and other stakeholders to solve local problems through data analysis, apps, sensors that monitor neighborhood resources and Internet of Things devices.
A Jacobs Institute-incubated startup has launched a global platform to address a challenging topic in scientific research — the crisis of reproducibility and transparency.
Code Ocean is a cloud-based platform that makes the computational code used in research both accessible and usable. Researchers and software engineers across the planet can now share and run code with a single click.
OpenAI, a nonprofit research institute cofounded and funded by Elon Musk, says it has discovered an easier-to-use alternative to reinforcement learning that gets rival results when it plays games and performs other tasks. At MIT Technology Review’s EmTech Digital conference in San Francisco on Monday, OpenAI’s research director, Ilya Sutskever, said that could allow researchers to make progress in machine learning faster.
“It’s competitive with today’s reinforcement-learning algorithms on standard benchmarks,” said Sutskever. “It is surprising that something so simple actually works.”
Today’s computer science and engineering students have a wonderful opportunity to put their skills and expertise to use solving the world’s biggest problems. The computer programs of today are really only constrained by the user’s imagination.
Today’s announcement that the University of Washington’s Department of Computer Science & Engineering will be elevated to a school and will bear my name is truly an honor.
UW has always felt like home to me for several reasons.
At the Usenix Symposium on Networked Systems Design and Implementation later this month, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory will present a system for testing new traffic management protocols that requires no alteration to network hardware but still works at realistic speeds — 20 times as fast as networks of software-controlled routers.
Computer scientists at Nanyang Technological University in Singapore have developed a new intelligent routing algorithm that attempts to minimize the occurrence of spontaneous traffic jams—those sudden snarls caused by greedy merges and other isolated disruptions—throughout a roadway network. It’s both computationally distributed and fast, requirements for any real-world traffic management system. Their work is described in the April issue of IEEE Transactions on Emerging Topics in Computational Intelligence.
Earth’s atmosphere is dusted with tiny particles known as aerosols, which include windblown ash, sea salt, pollution, and other natural and human-produced materials. Aerosols can absorb or scatter sunlight, affecting how much light reflects back into space or stays trapped in the atmosphere.
Despite aerosols’ known impact on Earth’s temperature, major uncertainties plague current estimates of their overall effects, which in turn limit the certainty of climate change models. In an effort to reduce this uncertainty, Lacagnina et al. have combined new satellite data, providing, for the first time, data on aerosols’ ability to absorb or reflect light globally, through model simulations
Leading up to the Machine Intelligence Summit in San Francisco this week, RE•WORK sat down with Minjoon Seo for a chat about his recent work that will be showcased at the summit. Minjoon is a 4th year PhD student in computer science at the University of Washington who is currently focusing on natural language understanding and QA research.
The fields for K2 Campaigns 17 and beyond have not yet been set. Although these future Campaigns are at risk of not being executed due to Kepler running low on fuel, there is a significant chance that the spacecraft will continue to function well into 2018 and the project must be ready to observe fields accordingly. Deadline for requests for field placements is April 11.
RepEval 2017 features a shared task meant to evaluate natural language understanding models based on sentence encoders—that is, models that transform sentences into fixed-length vector representations and reason using those representations. The task will be natural language inference (also known as recognizing textual entailment, or RTE) in the style of SNLI—a three-class balanced classification problem over sentence pairs. The shared task will feature a new, dedicated dataset that spans several genres of text.
Kaggle evaluation site opens on June 1. Evaluation ends June 14.
“Next.js is a very slim yet powerful framework. Place React components in a pages directory and running next, and you’ll get automatic code splitting, routing, hot code reloading and universal (server-side and client-side) rendering.”