News

Sorry Elon Musk, there’s no clear evidence Autopilot saves lives

Performance analysis of cloud applications

Today’s choice gives us an insight into how Google measure and analyse the performance of large user-facing services such as Gmail (from which most of the data in the paper is taken). It’s a paper in two halves. The first part of the paper demonstrates through an analysis of traffic and load patterns why the only real way to analyse production performance is using live production systems.

Biology Will Be the Next Great Computing Platform

Crispr, the powerful gene-editing tool, is revolutionizing the speed and scope with which scientists can modify the DNA of organisms, including human cells. So many people want to use it—from academic researchers to agtech companies to biopharma firms—that new companies are popping up to staunch the demand. Companies like Synthego, which is using a combination of software engineering and hardware automation to become the Amazon of genome engineering.

Technique to manipulate electrons with light could make computers much faster

Yale physicists find signs of a time crystal

Time crystals, first identified in 2016, are different. Their atoms spin periodically, first in one direction and then in another, as a pulsating force is used to flip them. That’s the “ticking.”

Experts say Tesla has repeated car industry mistakes from the 1980s

Mining in Space Could Lead to Conflicts on Earth

Space mining is no longer science fiction. By the 2020s, Planetary Resources and Deep Space Industries—for-profit space-mining companies cooperating with NASA—will be sending out swarms of tiny satellites to assess the composition of hurtling hunks of cosmic debris, identify the most lucrative ones, and harvest them. They’ve already developed prototype spacecraft to do the job.

Announcing PyTorch 1.0 for both research and production

PyTorch 1.0 takes the modular, production-oriented capabilities from Caffe2 and ONNX and combines them with PyTorch’s existing flexible, research-focused design to provide a fast, seamless path from research prototyping to production deployment for a broad range of AI projects. With PyTorch 1.0, AI developers can both experiment rapidly and optimize performance through a hybrid front end that seamlessly transitions between imperative and declarative execution modes. The technology in PyTorch 1.0 has already powered many Facebook products and services at scale, including performing 6 billion text translations per day.

Advancing state-of-the-art image recognition with deep learning on hashtags

Image recognition is one of the pillars of AI research and an area of focus for Facebook. Our researchers and engineers aim to push the boundaries of computer vision and then apply that work to benefit people in the real world — for example, using AI to generate audio captions of photos for visually impaired users. In order to improve these computer vision systems and train them to consistently recognize and classify a wide range of objects, we need data sets with billions of images instead of just millions, as is common today.

Sapienz: Intelligent automated software testing at scale

Shipping code updates to the Facebook app, which is used every day by hundreds of millions of people, requires extensive testing to ensure stability and performance. At Facebook’s scale, this process requires checking hundreds of important interactions across numerous types of devices and operating systems for both correctness and speed. Traditionally, this has largely been a manual test design process, during which engineers devote time and resources to designing test cases.