Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY
 
Beekman
Add Scalable deep learning with the Microsoft Cognitive Toolkit to your personal schedule
1:30pm Scalable deep learning with the Microsoft Cognitive Toolkit Anusua Trivedi (Microsoft), Barbara Stortz (Microsoft), Patrick Buehler (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
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 (Unity Technologies)
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)
Galiya Warrier and Gary Short walk you through creating a quantitative graph model from qualitative written information and demonstrate how to add a conversational frontend using the Microsoft Bot Framework.
1:30pm-5:00pm (3h 30m) Implementing AI Deep Learning, Machine Learning
Scalable deep learning with the Microsoft Cognitive Toolkit
Anusua Trivedi (Microsoft), Barbara Stortz (Microsoft), Patrick Buehler (Microsoft)
Anusua Trivedi, Barbara Stortz, and Patrick Buehler offer an overview of the Microsoft Cognitive Toolkit, which is native on both Windows and Linux and offers a flexible symbolic graph, a friendly Python API, and almost linear scalability across multi-GPU systems and multiple machines.
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. Yufeng Guo and Amy Unruh offer an introduction to TensorFlow concepts and walk you through using 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)
Laura Graesser offers a hands-on introduction to neural networks using the popular Python library Keras, focusing on building intuition for the core components of a neural network and what it means for a network to “learn.” You'll also get the opportunity to build and train your own network.
9:00am-12:30pm (3h 30m) Implementing AI Cloud, Deep Learning
Distributed deep learning on AWS using Apache MXNet
Joseph Spisak (Amazon)
Joseph Spisak and Anima Anandkumar offer an introduction to the powerful and scalable deep learning framework Apache MXNet. You'll gain hands-on experience using Apache MXNet with preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development and leave able to 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)
Yiheng Wang offers an overview of BigDL, a distributed deep learning library on Apache Spark that helps users easily integrate most advanced deep learning algorithms (CNN, RNN, etc.) into popular big data platforms. Yiheng demonstates how to develop with BigDL and shares some practical use cases.
9:00am-12:30pm (3h 30m) Implementing AI Machine Learning
Probabilistic programming
Vikash Mansinghka (MIT), Richard Tibbetts (Empirical Systems)
Probabilistic inference, a widely used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain or incomplete, is central to big data analytics to robotics and AI. Vikash Mansinghka and Richard Tibbetts survey the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to nonexperts.
1:30pm-5:00pm (3h 30m) Implementing AI Deep Learning, Machine Learning
Deep reinforcement learning tutorial
Arthur Juliani (Unity Technologies)
Recently, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments has been deep reinforcement learning. Arthur Juliani covers RL from the basics using lookup tables and GridWorld all the way to solving complex 3D tasks 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 analyzing structured data. New AI technologies are just beginning to transform this process. Vikash Mansinghka and Richard Tibbetts explore AI that guides business analysts to ask statistically sensible questions and lets junior data scientists answer in minutes questions 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