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

Schedule: Transportation and autonomous vehicles sessions

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9:00am–12:30pm Monday, September 18, 2017
Implementing AI
Location: Yosemite BC Level: Intermediate
Marcos Campos (Bonsai)
Average rating: **...
(2.33, 9 ratings)
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. Read more.
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1:30pm–5:00pm Monday, September 18, 2017
Verticals and applications
Location: Yosemite BC Level: Intermediate
Mo Patel (Independent), Laura Froelich (Think Big Analytics, a Teradata Company)
Average rating: **...
(2.00, 3 ratings)
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. Laura Froelich and Mo Patel demonstrate how to train deep learning models for object detection using publicly available transportation datasets. Read more.
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1:30pm–5:00pm Monday, September 18, 2017
Implementing AI
Location: Imperial A Level: Intermediate
Ion Stoica (UC Berkeley), Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley)
Average rating: ****.
(4.57, 7 ratings)
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. Read more.
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2:35pm–3:15pm Tuesday, September 19, 2017
Implementing AI
Location: Franciscan CD Level: Intermediate
Erran Li (Uber ATG)
Average rating: ***..
(3.80, 5 ratings)
Deep reinforcement learning has enabled artificial agents to achieve human-level performance across many challenging domains (for example, playing Atari games and Go). Li Erran Li shares several important algorithms, discusses major challenges, and explores promising results. Read more.
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4:00pm–4:40pm Tuesday, September 19, 2017
Interacting with AI
Location: Imperial A Level: Beginner
Michael B. Henry (Mythic)
Average rating: **...
(2.67, 3 ratings)
Breakthroughs in deep learning and new analog-domain computation methods to deploy trained neural networks will deliver exciting new capabilities. Michael B. Henry explains why the combination of human-like levels of recognition and massive computation capabilities in a tiny package will enable products with true awareness and understanding of the user and environment. Read more.
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4:50pm–5:30pm Tuesday, September 19, 2017
Location: Grand Ballroom
Timnit Gebru (Microsoft Research)
Average rating: ****.
(4.75, 4 ratings)
Targeted socioeconomic policies require an accurate understanding of a country’s demographics, and the US spends more than $1 billion a year gathering such data. Timnit Gebru shares a solution that leverages Google Street View images and a computer vision pipeline to predict income, carbon emission, crime rates, and other city attributes from a single source of publicly available data. Read more.
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11:55am–12:35pm Wednesday, September 20, 2017
Implementing AI
Location: Imperial B Level: Beginner
Kenneth Stanley (Uber AI Labs | University of Central Florida)
Average rating: ****.
(4.62, 8 ratings)
Kenneth Stanley offers an overview of the field of neuroevolution, an emerging paradigm for training neural networks through evolutionary principles that has grown up alongside more conventional deep learning, highlighting major algorithms such as NEAT, HyperNEAT, and novelty search, the field's emerging synergies with deep learning, and promising application areas. Read more.
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1:45pm–2:25pm Wednesday, September 20, 2017
Implementing AI
Location: Yosemite BC Level: Intermediate
Lukas Biewald (CrowdFlower)
Average rating: ***..
(3.71, 7 ratings)
Making the best possible use of training data is essential for effective machine learning. Active learning can make your training data collection 10x–1,000x more efficient, while transfer learning opens up a world of new training data possibilities. Lukas Biewald explores the state of the art in training data, active learning, and transfer learning, especially as applied to deep learning. Read more.
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1:45pm–2:25pm Wednesday, September 20, 2017
Implementing AI
Location: Imperial A Level: Beginner
Average rating: ****.
(4.00, 1 rating)
Current driving policy models are limited to models trained using homogenous data from a small number of vehicles running in controlled environments. Bruno Fernandez-Ruiz offers an overview of a network of connected devices that is building an end-to-end driving policy to leverage the 10 trillion miles driven every year. Read more.
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4:00pm–4:40pm Wednesday, September 20, 2017
Implementing AI
Location: Imperial B Level: Intermediate
Shaoshan Liu (PerceptIn)
Autonomous cars, like humans, need good eyes and a good brain to drive safely. Shaoshan Liu explains how PerceptIn designed and implemented its high-definition, stereo 360-degree camera sensors targeted for computer-vision-based autonomous driving. Read more.