Put AI to Work
April 15-18, 2019
New York, NY

Schedule: Reinforcement Learning sessions

1:45pm5:15pm Tuesday, April 16, 2019
Location: Petit Trianon
Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley), Ion Stoica (UC Berkeley), Eric Liang (UC Berkeley RISELab)
Average rating: *****
(5.00, 1 rating)
Ray is a general purpose framework for programming your cluster. Robert Nishihara, Philipp Moritz, Ion Stoica, and Eric Liang lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms. Read more.
1:00pm1:40pm Wednesday, April 17, 2019
Implementing AI
Location: Trianon Ballroom
JIAN CHANG (Alibaba Group), Sanjian Chen (Alibaba Group)
Average rating: ****.
(4.75, 8 ratings)
Jian Chang and Sanjian Chen outline the design of the AI engine built on Alibaba’s TSDB service, which enables fast and complex analytics of large-scale time series data in many business domains. Join in to see how TSDB empowers companies across various industries to better understand data trends, discover anomalies, manage risks, and boost efficiency. Read more.
1:00pm1:40pm Wednesday, April 17, 2019
Machine Learning, Models and Methods
Location: Regent Parlor
Danny Lange (Unity Technologies)
Average rating: *****
(5.00, 7 ratings)
Join Danny Lange to learn how to create artificially intelligent agents that act in the physical world (through sense perception and some mechanism to take physical actions, such as driving a car). You'll discover how observing emergent behaviors of multiple AI agents in a simulated virtual environment can lead to the most optimal designs and real-world practices. Read more.
2:40pm3:20pm Wednesday, April 17, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Sanjay Krishnan (University of Chicago)
Average rating: ***..
(3.00, 2 ratings)
Drawing on his work building and deploying an RL-based relational query optimizer, a core component of almost every database system, Sanjay Krishnan highlights some of the underappreciated challenges to implementing deep reinforcement learning. Read more.
4:05pm4:45pm Wednesday, April 17, 2019
Interacting with AI
Location: Regent Parlor
Kevin He (DeepMotion)
Average rating: ****.
(4.00, 2 ratings)
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 explains how to use physical simulation and machine learning to create interactive character technology. Read more.
4:55pm5:35pm Wednesday, April 17, 2019
Interacting with AI
Location: Regent Parlor
Paris Buttfield-Addison (Secret Lab), Mars Geldard (University of Tasmania), Tim Nugent (lonely.coffee)
Average rating: *****
(5.00, 9 ratings)
Games are wonderful contained problem spaces, making them great places to explore AI—even if you're not a game developer. Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to use Unity to train, explore, and manipulate intelligent agents that learn. You'll train a quadruped to walk, then train it to explore, fetch, and manipulate the world. Read more.
2:40pm3:20pm Thursday, April 18, 2019
Case Studies, Machine Learning
Location: Sutton South
Alina Matyukhina (Canadian Institute for Cybersecurity)
Average rating: ****.
(4.00, 2 ratings)
Machine learning models are often susceptible to adversarial deception of their input at test time, which leads to poorer performance. Alina Matyukhina investigates the feasibility of deception in source code attribution techniques in real-world environments and explores attack scenarios on users' identities in open source projects—along with possible protection methods. Read more.
4:55pm5:35pm Thursday, April 18, 2019
Models and Methods
Location: Trianon Ballroom
Matthew REYES (Technergetics)
Average rating: *....
(1.00, 1 rating)
Matthew Reyes casts consumer decision making within the framework of random utility and outlines a simplified scenario of optimizing preference on a social network to illustrate the steps in a company’s allocation decision, from learning parameters from data to evaluating the consequences of different marketing allocations. Read more.