Presented By O’Reilly and Intel Nervana
Put AI to work
September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA
 
Nob Hill 2 & 3
Add word2vec and friends to your personal schedule
9:00am word2vec and friends Bruno Gonçalves (New York University)
Yosemite A
Add Probabilistic programming to your personal schedule
9:00am Probabilistic programming Vikash Mansinghka (MIT)
Add Training vision models with public transportation datasets to your personal schedule
1:30pm Training vision models with public transportation datasets Mo Patel (Think Big Analytics), Laura Frolich (Think Big Analytics, A Teradata Company)
Yosemite BC
Add Introduction to reinforcement learning to your personal schedule
9:00am Introduction to reinforcement learning Marcos Campos (Bonsai)
Add Building reinforcement learning applications with Ray to your personal schedule
1:30pm Building reinforcement learning applications with Ray Ion Stoica (UC Berkeley), Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley)
Imperial A
Add Getting started with TensorFlow to your personal schedule
9:00am Getting started with TensorFlow Yufeng Guo (Google), Amy Unruh (Google)
Add Convolutional neural networks with Keras to your personal schedule
1:30pm Convolutional neural networks with Keras Nikhil Buduma (Remedy)
Imperial B
Add AI for business to your personal schedule
9:00am AI for business Jana Eggers (Nara Logics)
Add Here and now: Bringing AI into the enterprise to your personal schedule
1:30pm Here and now: Bringing AI into the enterprise Kristian Hammond (Narrative Science)
12:30pm Lunch | Room: Grand Ballroom B
10:30am Morning Break | Room: TBD
3:00pm Afternoon Break | Room: TBD
Add AI 2017 Startup Showcase to your personal schedule
5:00pm AI 2017 Startup Showcase | Room: TBD
Add AI Dine-A-Round to your personal schedule
7:00pm AI Dine-A-Round | Room: Various locations
9:00am-12:30pm (3h 30m) Implementing AI Algorithms, Case studies
word2vec and friends
Bruno Gonçalves (New York University)
Bruno Gonçalves explores word2vec and its variations, discussing the main concepts and algorithms behind the neural network architecture used in word2vec and the word2vec reference implementation in TensorFlow. Bruno then presents a bird's-eye view of the emerging field of 2vec methods that use variations of the word2vec neural network architecture.
1:30pm-5:00pm (3h 30m) Implementing AI Data science and AI, Tools and frameworks
Topological data analysis as a framework for machine intelligence
Gunnar Carlsson (Ayasdi)
Topological data analysis (TDA) is a framework for machine learning that synthesizes and combines machine learning algorithms to identify the shape of data. The technique is responsible for several major breakthroughs in our understanding of science and business. Gunnar Carlsson offers an overview of TDA's mathematical underpinnings and its practical application through software.
9:00am-12:30pm (3h 30m) Implementing AI Data science and AI
Probabilistic programming
Vikash Mansinghka (MIT)
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) Verticals and applications Data and training, Transportation and autonomous vehicles
Training vision models with public transportation datasets
Mo Patel (Think Big Analytics), Laura Frolich (Think Big Analytics, A Teradata Company)
Computer vision is a key component in the artificial intelligence revolution. Assisted by deep learning, object detection allows automotive applications to make key navigation, guidance, and driving decisions to avoid collisions and navigation errors. Ron Bodkin and Mo Patel demonstrate how to train deep learning models for object detection using publicly available transportation datasets.
9:00am-12:30pm (3h 30m) Implementing AI Algorithms, Data science and AI, Transportation and autonomous vehicles
Introduction to reinforcement learning
Marcos Campos (Bonsai)
Marcos Campos offers an overview of reinforcement learning, walking you through the various classes of reinforcement learning algorithms, the types of problems that can be solved with this technique, and how to build and train AI models using reinforcement learning and reward functions.
1:30pm-5:00pm (3h 30m) Implementing AI Algorithms, Open source, Transportation and autonomous vehicles
Building reinforcement learning applications with Ray
Ion Stoica (UC Berkeley), Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley)
Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the art RL algorithms.
9:00am-12:30pm (3h 30m) Implementing AI Deep learning, Tools and frameworks
Getting started with TensorFlow
Yufeng Guo (Google), Amy Unruh (Google)
Yufeng Guo walks you through training and deploying a machine learning system using TensorFlow, a popular open source library. Yufeng takes you from conceptual overviews all the way to building complex classifiers and explains how you can apply deep learning to complex problems in science and industry.
1:30pm-5:00pm (3h 30m) Implementing AI Deep learning
Convolutional neural networks with Keras
Nikhil Buduma (Remedy)
Nikhil Buduma leads a deep dive into modern deep learning methods for image recognition using the Keras library, covering the fundamentals of neural networks, optimizer theory, overfitting and regularization, convolutional layers, and relevant implementation details. Nikhil then walks you through training your own image recognizer.
9:00am-12:30pm (3h 30m) Implementing AI Case studies, Enterprise adoption
AI for business
Jana Eggers (Nara Logics)
Now is the time for us to define roles and capabilities for AI in business. Jana Eggers demonstrates how to deliver on an AI project for business, walking you through defining your project, setting expectations, assembling your team, hunting for data, assessing capabilities, implementing it, and rinsing and repeating.
1:30pm-5:00pm (3h 30m) Implementing AI Enterprise adoption, Tools and frameworks
Here and now: Bringing AI into the enterprise
Kristian Hammond (Narrative Science)
Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Kristian Hammond shares a practical framework for understanding the role of AI technologies in problem solving and decision making.
12:30pm-1:30pm (1h)
Break: Lunch
10:30am-11:00am (30m)
Break: Morning Break
3:00pm-3:30pm (30m)
Break: Afternoon Break
5:00pm-6:30pm (1h 30m)
AI 2017 Startup Showcase
What new companies are at the leading edge of the AI space? Meet some of the best, most innovative founders as they demonstrate their game-changing ideas at the Startup Showcase.
7:00pm-9:00pm (2h)
AI Dine-A-Round
Get to know your fellow attendees over dinner. We've made reservations for you at some of the most sought-after restaurants in town. This is a great chance to make new connections and sample some of the great cuisine San Francisco has to offer.