News

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.

Embodied Question Answering: A goal-driven approach to autonomous agents

Facebook AI Research (FAIR) has developed a collection of virtual environments for training and testing autonomous agents, as well as novel AI agents that learn to intelligently explore those environments. To test this goal-driven approach, FAIR are collaborating Georgia Tech on a multistep AI task called Embodied Question Answering, or EmbodiedQA.

ONNX expansion speeds AI development

Facebook helped develop the Open Neural Network Exchange (ONNX) format to allow AI engineers to more easily move models between frameworks without having to do resource-intensive custom engineering. Today, we’re sharing that ONNX is adding support for additional AI tools, including Apple Core ML converter technology, Baidu’s PaddlePaddle platform, and Qualcomm SNPE.

Google releases open source framework for building “enclaved” apps for cloud

Researchers find a gene that lets bacteria kill all male flies

Massive E. coli outbreak linked to romaine lettuce turns deadly

Linux System Monitoring with eBPF

The Linux kernel is an abundant component of modern IT systems. It provides the critical services of hardware abstraction and time-sharing to applications. The classical metrics for monitoring Linux are among the most well known metrics in monitoring: CPU utilization, memory usage, disk utilization, and network throughput.