News

Radiology and Deep Learning

Radiology and DeepLearningDetecting pneumonia opacities from chest X-Ray images using deep learning. One day back in August, I was catching up with my best friend from high school who is now a radiology resident. One thing led to another, and we started talking about our interests in artificial intelligence and machine learning and its possible applications in radiology.

Inside the plans for Chinese mega-collider that will dwarf the LHC

Getting started with Jenkins X

Jenkins X is an open source system that offers software developers continuous integration, automated testing, and continuous delivery, known as CI/CD, in Kubernetes. Jenkins X-managed projects get a complete CI/CD process with a Jenkins pipeline that builds and packages project code for deployment to Kubernetes and access to pipelines for promoting projects to staging and production environments.

Ion drive meets drone, as small plane flies with no moving parts

Universal Quantum Phenomenon Found in Superconductors

A ubiquitous quantum phenomenon has been detected in a large class of superconducting materials, fueling a growing belief among physicists that an unknown organizing principle governs the collective behavior of particles and determines how they spread energy and information. Understanding this organizing principle could be a key into “quantum strangeness at its deepest level,” said Subir Sachdev, a theorist at Harvard University who was not involved with the new experiments. The findings, reported today inNature Physicsby a team working at the University of Sherbrooke in Canada and the National Laboratory for Intense Magnetic Fields (LNCMI) in France, indicate that electrons inside a variety of ceramic crystals called “cuprates” seem to dissipate energy as quickly as possible, apparently bumping up against a fundamental quantum speed limit.

Predictive Scaling for EC2, Powered by Machine Learning

When I look back on the history of AWS and think about the launches that truly signify the fundamentally dynamic, on-demand nature of the cloud, two stand out in my memory: the launch of Amazon EC2 in 2006 and the concurrent launch of CloudWatch Metrics, Auto Scaling, and Elastic Load Balancing in 2009. The first launch provided access to compute power; the second made it possible to use that access to rapidly respond to changes in demand. We have added a multitude of features to all of these services since then, but as far as I am concerned they are still central and fundamental!

CDC: Outbreak of E. coli Infections Linked to Romaine Lettuce

Observability at Scale: Building Uber’s Alerting Ecosystem

Uber’s software architectures consists of thousands of microservices that empower teams to iterate quickly and support our company’s global growth. These microservices support a variety of solutions, such as mobile applications, internal and infrastructure services, and products along with complex configurations that affect these products at city and sub-city levels. To maintain our growth and architecture, Uber’s Observability team built a robust, scalable metrics and alerting pipeline responsible for detecting, mitigating, and notifying engineers of issues with their services as soon as they occur.

Scaling Spark Streaming for Logging Event Ingestion

Walking over a stream during an Airbnb Experience in Kuala Lumpur. Searching, viewing, and booking such Experiences will all produce logging events that will be processed by our stream processing framework. Logging events are emitted from clients (such as mobile apps and web browser) and online services with key information and context about the actions or operations.

The dark side of YouTube

The YouTube algorithm that I helped build in 2011 still recommends the flat earth theory by the hundreds of millions. This investigation by @RawStory shows some of the real-life consequences of this badly designed AI.