Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY
 
Beekman
Add Scalable Deep Learning with Microsoft Cognitive Toolkit to your personal schedule
1:30pm Scalable Deep Learning with Microsoft Cognitive Toolkit Anusua Trivedi (Microsoft), Barbara Stortz (Microsoft)
Sutton South/Regent Parlor
Add Scaling machine learning with TensorFlow to your personal schedule
9:00am Scaling machine learning with TensorFlow Yufeng Guo (Google), Amy Unruh (Google)
Add Introduction to Neural Networks with Keras to your personal schedule
1:30pm Introduction to Neural Networks with Keras Laura Graesser (New York University)
Murray Hill E/W
Add Distributed Deep Learning on AWS using MXNet to your personal schedule
9:00am Distributed Deep Learning on AWS using MXNet Joseph Spisak (Amazon), Anima Anandkumar (UC Irvine)
Sutton North
Add Probabilistic Programming to your personal schedule
9:00am Probabilistic Programming Vikash Mansinghka (MIT), Richard Tibbetts (Empirical Systems)
Add Deep Reinforcement Learning Tutorial to your personal schedule
1:30pm Deep Reinforcement Learning Tutorial Arthur Juliani (University of Oregon)
Sutton Center
Add Here and now: Bringing AI into the enterprise to your personal schedule
9:00am Here and now: Bringing AI into the enterprise Kristian Hammond (Narrative Science)
Add AI for Structured Business Data to your personal schedule
1:30pm AI for Structured Business Data Vikash Mansinghka (MIT), Richard Tibbetts (Empirical Systems)
12:30pm Lunch | Room: Rhinelander
10:30am Morning Break | Room: Sutton Complex Foyer
3:00pm Afternoon Break | Room: Sutton Complex Foyer
9:00am-12:30pm (3h 30m) Interacting with AI Natural Language, User interface and experience
Learn how to gain business insights from unstructured data by leveraging NERs, graphs and conversational interfaces
Galiya Warrier (Microsoft), Gary Short (Microsoft)
In this 3 hour tutorial we will teach you how to create a quantitative graph model from qualitative written information. We will then go on to show you how to put a conversational front-end onto this using the Microsoft Bot Framework. A step-by-step lab will be provided for you to work through this tutorial.
1:30pm-5:00pm (3h 30m) Implementing AI Deep Learning, Machine Learning
Scalable Deep Learning with Microsoft Cognitive Toolkit
Anusua Trivedi (Microsoft), Barbara Stortz (Microsoft)
We will be introducing the Cognitive Toolkit from Microsoft, which is native on both Windows and Linux, and offers flexible symbolic graph, friendly Python API, almost linear scalability across multi-GPU and multiple machines. We strongly believe the audiences will benefit learning and using our toolkit to speed up their experiments and find better deep learning algorithms.
9:00am-12:30pm (3h 30m) Implementing AI Cloud, Deep Learning
Scaling machine learning with TensorFlow
Yufeng Guo (Google), Amy Unruh (Google)
TensorFlow is an increasingly popular open source Machine Intelligence library that is especially well-suited for deep learning. Google Cloud Machine Learning (CloudML) lets you do distributed training and serving at scale. In this workshop, we'll give an introduction to TensorFlow concepts and walk through how to use CloudML to do distributed training and scalable serving of your models.
1:30pm-5:00pm (3h 30m) Implementing AI Deep Learning
Introduction to Neural Networks with Keras
Laura Graesser (New York University)
This tutorial is a hands on introduction to neural networks using the popular Python library, Keras. We’ll focus on building intuition for the core components of a neural network and what it means for a network to “learn”. Then attendees will have the opportunity to build and train their own network.
9:00am-12:30pm (3h 30m) Implementing AI Cloud, Deep Learning
Distributed Deep Learning on AWS using MXNet
Joseph Spisak (Amazon), Anima Anandkumar (UC Irvine)
During this workshop, members of the Amazon AI team will provide a short background on Deep Learning focusing on relevant application domains and an introduction to using the powerful and scalable Deep Learning framework, MXNet. At the end of this tutorial you’ll gain hands on experience targeting a variety of applications and be able quickly spin up AWS GPU clusters to train at record speeds!
1:30pm-5:00pm (3h 30m) Implementing AI Deep Learning
BigDL: Distributed Deep Learning on Apache Spark
Yiheng Wang (Intel)
This tutorial will introduce BigDL, a distributed deep learning library on Apache Spark. Based on BigDL, users can easily integrate most advanced deep learning algorithms(CNN, RNN, etc.) into popular big data platform in industry. We will show how to develop with BigDL, and introduce some use cases in practice.
9:00am-12:30pm (3h 30m) Implementing AI Machine Learning
Probabilistic Programming
Vikash Mansinghka (MIT), Richard Tibbetts (Empirical Systems)
Probabilistic inference is a widely-used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain and/or incomplete. It has become central to multiple fields, from big data analytics to robotics and AI. This class will survey the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to non-experts.
1:30pm-5:00pm (3h 30m) Implementing AI Deep Learning, Machine Learning
Deep Reinforcement Learning Tutorial
Arthur Juliani (University of Oregon)
In the past few years computers have been able to learn to play Atari games, Go, and recently First Person Shooters at a superhuman level. Underlying all these accomplishments has been Deep Reinforcement Learning (Deep RL). This tutorial will cover RL from the basics using lookup tables and gridworld all the way to solving complex 3D tasks such as First-Person shooters with deep neural networks.
9:00am-12:30pm (3h 30m) Impact of AI on business and society, Interacting with AI
Here and now: Bringing AI into the enterprise
Kristian Hammond (Narrative Science)
Kristian Hammond shares a practical framework for understanding the role of AI technologies in problem solving and decision making, focusing on how they can be used, the requirements for doing so, and the expectations for their effectiveness.
1:30pm-5:00pm (3h 30m) Impact of AI on business and society Machine Learning
AI for Structured Business Data
Vikash Mansinghka (MIT), Richard Tibbetts (Empirical Systems)
Businesses have spent decades trying to make better decisions by collecting & analyzing structured data. New AI technologies are beginning to transform this process. This talk will focus on AI that (i) guides business analysts to ask statistically sensible questions and (ii) lets junior data scientists answer questions in minutes that previously took hours for trained statisticians.
12:30pm-1:30pm (1h)
Break: Lunch
10:30am-11:00am (30m)
Break: Morning Break
3:00pm-3:30pm (30m)
Break: Afternoon Break