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Author: engineering

Reflections on the Last Two Years of Spotify’s Bug Bounty Program

We recently surpassed the two year anniversary of our bug bounty program on the HackerOne platform. This gave us pause to take a look back at our successes and learnings in engaging with the security community to help improve security at Spotify.  What is a bug bounty program? The Internet was conceived as an open and accessible place for the exchange of ideas and discussion with people from all over the world. It accomplished this by connecting computers and networks…

WebDo Review – A Free Website Design Solution

If you’re looking for a free website design solution which also takes care of all the technical aspects, such as hosting, domains, and more, then Webdo.com might be what you need. Today we’ll take an in-depth look of the WebDo platform – a recently launched Cloud website builder. We’ll analyse its features and see what it has to offer especially for those of you who are just starting their online business and prefer a free hosting solution until the business…

How Castle is Building Codeless Customer Account Protection

This is a guest post by Johanna Larsson, of Castle, who designed and built the Castle Cloudflare app and the supporting infrastructure. Strong security should be easy. Asking your consumers again and again to take responsibility for their security through robust passwords and other security measures doesn’t work. The responsibility of security needs to shift from end users to the companies who serve them. Castle is leading the way for companies to better protect their online accounts with millions of…

Three Approaches to Scaling Machine Learning with Uber Seattle Engineering

Uber’s services require real-world coordination between a wide range of customers, including driver-partners, riders, restaurants, and eaters. Accurately forecasting things like rider demand and ETAs enables this coordination, which makes our services work as seamlessly as possible.  In an effort to constantly optimize our operations, serve our customers, and train our systems to perform better and better, we leverage machine learning (ML). In addition, we make many of our ML tools open source, sharing them with the community to advance…

RJI announces criteria for Student Innovation Competition 2020

  Team Six Flags won the 2019 Reynolds Journalism Institute Student Competition. Deepfake videos, fabricated photos and audio are among the biggest challenges the news industry faces today as it tries to keep the public informed with accurate information. In the U.S., lawmakers have considered legislation against false images, but how long will it take for policies to be implemented and will they be able to solve the problem? This year’s RJI Student Innovation Competition challenge is to create a program, tool or…

Reimagining Experimentation Analysis at Netflix – Netflix TechBlog

Toby Mao, Sri Sri Perangur, Colin McFarland Another day, another custom script to analyze an A/B test. Maybe you’ve done this before and have an old script lying around. If it’s new, it’s probably going to take some time to set up, right? Not at Netflix. ABlaze: The standard view of analyses in the XP UI Suppose you’re running a new video encoding test and theorize that the two new encodes should reduce play delay, a metric describing how long…

Award-winning Mizzou research seeks to detect apnea effects

Omiya Hassan and Samira Shamsir took home the top prize in the student contest at the IEEE International Symposium on Inertial Sensors and Systems for their project, “Smart Infant Monitoring System Capable of Detecting Apnea, Seizure and Other Activities.” Photo courtesy of Omiya Hassan and Samira Shamsir. A novel sensor prototype designed to noninvasively detect sleep apnea in infants recently earned a pair of Mizzou Electrical Engineering & Computer Science graduate students a couple of major accolades from the Institute…

Science at Uber: Powering Machine Learning at Uber

At Uber, we take advanced research work and use it to solve real world problems. In our  Science at Uber video series, Uber employees talk about how we apply data science, artificial intelligence, machine learning, and other innovative technologies in our daily work. Machine learning helps Uber make data-driven decisions which not only enable services such as ridesharing, but also financial planning and other core business needs. Our machine learning platform, Michelangelo, lets teams across the company train, evaluate, and…

Introducing LCA: Loss Change Allocation for Neural Network Training

Neural networks (NNs) have become prolific over the last decade and now power machine learning across the industry. At Uber, we use NNs for a variety of purposes, including detecting and predicting object motion for self-driving vehicles, responding more quickly to customers, and building better maps. While many NNs perform quite well at their tasks, networks are fundamentally complex systems, and their training and operation is still poorly understood. For this reason, efforts to better understand network properties and model…

Introducing Harmonia: Context-Aware Product Recommendation From Room Images

Introduction When looking for a new piece of furniture to add to a room, customers generally consider two major points: the visual appearance of the new item, and its harmony with the existing furniture inside the room. Together, these two considerations define a customer’s style preferences. Wayfair’s vast product catalog offers our customers plenty of options so that they can find the perfect item; but, sifting through millions of products individually would be a very time consuming process.  So, in…