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Category: Construction Engineering

Amenity Detection and Beyond — New Frontiers of Computer Vision at Airbnb

Build highly customized AI technologies into home-sharing products and help our guests belong anywhere. Authors: Shijing Yao, Dapeng Li, Shawn Chen What amenities are there in this image? Can an algorithm detect them all? How can we train the algorithm effectively? If you are interested in the answers, please read on! In 2018, we published a blog post titled Categorizing Listing Photos at Airbnb. In that post, we introduced an image classification model which categorized listing photos into different room…

Study provides framework for assessing historic wrought iron structures

The Institution of Civil Engineers will honor the research of Debra Laefer, a professor of civil and urban engineering at NYU Tandon, who employed terrestrial laser scanning (TLS) to examine Ireland’s historic Guinness Bridge. The research paper, published in the journal Engineering History and Heritage, garnered the 2019 Manby Prize from the institution, which lauded it for its exceptional quality and benefit to the civil engineering, construction and materials science community. Credit: Brigitte Brückner-Uhlenhuth The Institution of Civil Engineers will…

MoneyCon ’19: Uber Hosts its First FinTech Engineering Conference

The domain of payments, finance, and more generally FinTech is a fast-growing industry that reached a record global investment of $111.8B in 2018. The underlying technology that powers this incredible growth is also evolving rapidly. With gross bookings of $50 billion for 2018, a global reach of more than 700 cities across 63 countries on six continents, and an evolving, constantly expanding suite of products, Uber must tackle large-scale technical challenges to best engineer these systems.  Building financial systems at…

An advanced, open source deep learning recommendation model

With the advent of deep learning, neural network-based personalization and recommendation models have emerged as an important tool for building recommendation systems in production environments, including here at Facebook. However, these models differ significantly from other deep learning models because they must be able to work with categorical data, which is used to describe higher-level attributes. It can be challenging for a neural network to work efficiently with this kind of sparse data, and the lack of publicly available details…

The Programmer Mindset: Main Debug Loop

Validating ONLY with tests is basically flying the plane on instrumentation, versus being able to look out the windshield. Flying visually and by muscle-memory is both more efficient and safer, in conjunction with instrumentation. You’re much less likely to hit a mountain by mistake. When you’ve been coding for more than twenty years, it can be difficult to recapture beginner’s mind, and explain how to think like a programmer to someone who is new to it. I remember an incident in…

Third Time’s a Charm (A brief history of a gay marriage)

Happy Pride from Proudflare, Cloudflare’s LGBTQIA+ employee resource group. We wanted to share some stories from our members this month which highlight both the struggles behind the LGBTQIA+ rights movement and its successes. This first story is from Lesley. The moment that crystalised the memory of that day…crystal blue afternoon, bright-coloured autumn leaves, borrowed tables, crockery and cutlery, flowers arranged by a cousin, cake baked by a neighbour, music mixed by a friend… our priest/rabbi a close gay friend with…

Improving End-to-End Testing at Coursera using Puppeteer and Jest

By Ankit Ahuja, Billy Kirk, and Sumit Gogia. Coursera is committed to delivering a high level of product quality for every learning experience. This includes individual courses, and courses included within Degree & MasterTrack™ programs. In the past, we’ve used Selenium and Sauce Labs for running end-to-end tests. Stability issues with our setup and limited continuous integration (CI) tools around test result reporting led to poor adoption. To make end-to-end (e2e) testing an essential part of our culture and help…

Community-Focused Feed Optimization | LinkedIn Engineering

FollowFeed architecture We have updated FollowFeed’s ranking engine to Quasar. Quasar, as part of LinkedIn’s Pro-ML technology, transforms machine learning features and inferences the machine learning model at query time. As a high-performance, multi-threaded ranking engine, Quasar not only optimizes for infrastructure system efficiency but also machine learning productivity. Such productivity improvements have enabled: Cross-system leverage: We can easily port the latest machine learning models and transformers from the second pass layer to FollowFeed. Training and serving consistency: At offline…

ML-based code search using natural language queries

Engineers work best when they can easily find code examples to guide them on particular coding tasks. For some questions — for example, “How to programmatically close or hide the Android soft keyboard?” — information is readily available from popular resources like Stack Overflow. But questions specific to proprietary code or APIs (or code written in less common programming languages) need a different solution, since they are not typically discussed in those forums. To address this need, we’ve developed a…

Move Thoughtfully and Don’t Be Afraid to Change Things

Rebuilding our Support Tech architecture at Lyft On the support tech team at Lyft, we’ve been rebuilding our support systems to better stand the test of time and enable our growth. We chose to “move thoughtfully” and “not be afraid to change things” instead of the popular hacker mentality of “move fast and break things.” This strategy was fueled by a deep understanding of what challenges existed in the past, what is needed now, and what is required for the future.…