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Put AI to work
September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA

Schedule: IoT (including smart cities, manufacturing, smart homes/buildings) sessions

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1:45pm–2:25pm Tuesday, September 19, 2017
Implementing AI
Location: Yosemite A Level: Beginner
Siddha Ganju (Deep Vision)
Average rating: ***..
(3.33, 3 ratings)
Deep learning is necessary to bring intelligence and autonomy to the edge. Siddha Ganju offers an overview of Deep Vision's solution, which optimizes both the hardware and the software, and discusses the Deep Vision embedded processor, which is optimized for deep learning and computer vision and offers 50x higher performance per watt than existing embedded GPUs without sacrificing programmability. Read more.
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4:50pm–5:30pm Tuesday, September 19, 2017
Verticals and applications
Location: Imperial A Level: Intermediate
Jisheng Wang (Aruba, a Hewlett Packard Enterprise Company)
Average rating: *****
(5.00, 1 rating)
Recently, both deep learning and the IoT have attracted tremendous attention. Jisheng Wang shares firsthand experience in applying deep learning to solving some real-world enterprise IoT problems (e.g., IoT device identification and IoT security) and outlines some challenges for deep learning in enterprise applications, along with suggestions to overcome them. Read more.
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11:55am–12:35pm Wednesday, September 20, 2017
Verticals and applications
Location: Yosemite A Level: Non-technical
David Rogers (Sight Machine)
Average rating: ***..
(3.33, 3 ratings)
Artificial intelligence in manufacturing has been around for a long time, but are you aware of how it can make your operations more efficient and profitable? David Rogers explains how existing technologies like the digital twin approach, advanced decision making, and downtime cause detection have primed manufacturing for a profitable and efficient future. 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.