Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY
Discover opportunities for applied AI
Organizations that successfully apply AI innovate and compete more effectively. How is AI transforming your business?
Be a part of the program—apply to speak by October 16.

Schedule: Media, Marketing, Advertising sessions

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1:45pm5:15pm Tuesday, April 16, 2019
AI Business Summit
Location: Mercury Ballroom
Alex Siegman (Dow Jones), Kabir Seth (Wall Street Journal)
This tutorial walks attendees through the steps necessary to appropriately leverage AI in a large organization: This includes ways to identify business opportunities that lend themselves to AI, as well as best practices on everything from data intake and manipulation to model selection, output analysis, development and deployment, all while navigating a complex organizational structure. Read more.
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11:05am11:45am Wednesday, April 17, 2019
AI Business Summit, Case Studies
Location: Sutton North/Center
Lucy Wang (BuzzFeed), Swara Kantaria (BuzzFeed)
As BuzzFeed’s content production and social networks grow, curation becomes increasingly difficult. To this end, we first built publishing tools that let people work more efficiently. Now, we build artificial intelligence tools that let people work more intelligently. During this talk we plan to share this evolution with the audience. Read more.
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1:00pm1:40pm Wednesday, April 17, 2019
Case Studies, Machine Learning
Location: Sutton South
Twitter is a company with massive amounts of data. Thus, it is no wonder that the company applies machine learning in myriad of ways. In this session, we are going to describe, in depth, one of those use cases: Timeline Ranking. From modeling to infrastructure our goal is to share some of the optimizations that this team have made in order to have models that are both expressive and efficient. Read more.
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1:50pm2:30pm Wednesday, April 17, 2019
Implementing AI
Location: Rendezvous
YU DONG (Facebook Inc)
An overview of why, what & how of building a production-scale ML platform based on ongoing ML research trends and industry adoptions. Read more.
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2:40pm3:20pm Wednesday, April 17, 2019
Implementing AI
Location: Rendezvous
Yi Zhuang (Twitter), Nicholas Leonard (Twitter)
Twitter is a 4000+ employee company with many ML use cases. Historically, there are many different ways to productionize ML at Twitter. In this session, we describe the setup and benefits of a unified ML platform for production, and how Twitter Cortex team brings together users of various ML tools. Read more.
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4:05pm4:45pm Wednesday, April 17, 2019
Interacting with AI
Location: Regent Parlor
Kevin He (DEEPMOTION, INC.)
Digital character interaction is hard to fake–whether it’s between two characters, between users and characters, or between a character and its environment. Nevertheless, interaction is central to building immersive XR experiences, robotic simulation, and user-driven entertainment. Kevin He will discuss using physical simulation and deep learning to create interactive character technology. Read more.
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4:05pm4:45pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Marcel Kurovski (inovex GmbH)
Recommender Systems support decision making with personalized suggestions. They have proven useful in e-commerce, entertainment, or social networks. However, sparse data and linear models are a burden. Application of Deep Learning sets new boundaries and constitutes remarkable results. This talk shows its application on vehicle recommendations at Germany's biggest online vehicle market. Read more.
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4:05pm4:45pm Thursday, April 18, 2019
Implementing AI
Location: Rendezvous
Jaewon Lee (LINE Corp.), Sihyeung Han (NAVER & LINE Corp)
"Until when are you going to cluster queries by yourself to manage large data corpus?" "Until when are you going to tune model hyper parameters by yourself?" I would like to introduce how to implement self-trained dialogue model by using AutoML in Chatbot within our Chatbot Builder Framework. Read more.
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4:55pm5:35pm Thursday, April 18, 2019
Models and Methods
Location: Rendezvous
Matthew Reyes (Independent Researcher and Consultant)
This talk considers optimizing preference towards a product on a social network. The model for consumer decision-making is based on the notion of random utility. The contributions of the model are stochastic decisions that will be learned from data, and the inclusion of marketing under the control of individual companies. These contributions enable a reinforcement learning based approach. Read more.