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Track: Modern CS in the Real World

Location: Pacific LMNO

Day of week: Monday

The most groundbreaking, controversial, and innovative technical choices that drive our industry forward center around computer science. From the fundamental topics in CS to the most recent research, computer science has a very real and present role in how software grows and evolves. It also directly impacts how we interact with technology.

The Modern CS in the Real World track centers around the subtler aspects of how computer science affects our lives as both consumers and creators of software. This track will fuse together the human side of computer science with the technical choices that are made along the way. These technical talks will cover a broad range of topics, highlighting some of the toughest technical problems in our field, and how they have been approach to impact fields including ethics, security, education, and the arts.

Together, we'll investigate not just what problems in computer science are the hardest and most interesting, but also which ones are currently impacting and transforming the world around us.

Track Host: Vaidehi Joshi

Staff Engineer at Tilde

Vaidehi is an engineer at Tilde, in Portland, Oregon, where she works on Skylight. She enjoys building and breaking code, but loves creating empathetic engineering teams a whole lot more. In her spare time, she runs basecs, a weekly writing series that explores the fundamentals of computer science, and is co-host of the Base.cs Podcast, as well as a producer of the BaseCS video series.

CASE STUDY TALK (50 MIN)

10:35am - 11:25am

Artwork Personalization @Netflix

For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.

Justin Basilico, Machine Learning Research/Engineering Director @Netflix
CASE STUDY TALK (50 MIN)

11:50am - 12:40pm

Modern CS Open Space

CASE STUDY TALK (50 MIN)

1:40pm - 2:30pm

Zero to Production in Five Months @ ThirdLove

At ThirdLove, we believe that every woman should be comfortable and confident every day. For half of the world’s population that means a bra whose straps stay put, their cup mold to their shape and is the perfect style for the occasion. In this talk, we will discuss how we built our first machine learning recommendation algorithm that predicts bra size and style. We will cover two broad topics - the first being the challenge of working with real-world data where there is no truth flag. Then we will talk about the tradeoffs associated with key decisions we made around design, implementation and testing.

Megan Cartwright, Head of Data Science @ThirdLove
CASE STUDY TALK (50 MIN)

2:55pm - 3:45pm

Algorithms Behind Modern Storage Systems

In the world of Big Data, it’s important to know how the Storage Systems work in order to be able to pick a right tool right job. The talk covers modern storage system approaches, discussing storage internals, and evaluation techniques to choose a database with with the optimal read, write or memory overhead, best suitable for your data.

Oleksandr Petrov, Apache Cassandra Committer, Distributed Systems Engineer
CASE STUDY TALK (50 MIN)

4:10pm - 5:00pm

Building a Voice Assistant for Enterprise

Einstein Assistant is an AI Voice assistant for enterprises that enables users to "Talk to Salesforce". Users can dictate memos, update Salesforce records and create tasks using natural language. Einstein Assistant pioneers the use of Voice and Natural Language Processing (NLP) to enhance the user experience by reducing manual entry and increasing the timeliness and volume of data capture.

In this talk, we will go through the high-level architecture and workflow starting from Automatic Speech Recognition (ASR) on device to using NLP for identifying entities and intents in a single dialog conversation text.

Come to learn our practical approach to implementing a Voice Assistant and the unique challenges involved in integrating with enterprise data. We will discuss further opportunities to improve on our approach. For example, we look at adopting a more general multi-task learning NLP model (see decaNLP.com) instead of a single task model to enhance NLP performance.

 

Manju Vijayakumar, Lead Software Engineer @salesforce
CASE STUDY TALK (50 MIN)

5:25pm - 6:15pm

Machine Learning for Handwriting and Sketch Recognition

The terms “big data”, “machine learning”, “neural networks”, and “deep learning” have appeared in many attention-grabbing headlines over the years, but what do they really mean? This presentation will describe some concrete examples of how they have impacted a variety of products, by enabling computers to interact with the world as people do, rather than the other way around. We then take a deeper dive into how machine learning and neural networks are used in two particular products, recognition of handwriting and sketches. Finally, we look at how the “big data” obtained from a sketch recognition game can not only be used for machine learning, but to learn more about how people around the world understand and draw everyday objects.

Li-Lun Wang, Software Engineer @Google
Henry Rowley, Staff Research Scientist @Google

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