Track: AI Meets the Physical World

Location: Embarcadero

Day of week: Wednesday

Artificial intelligence and machine learning algorithms became an essential part of many online services. Lately many companies started bringing AI to the physical world. Mobile phone apps use more and more on-board machine learning algorithms, cars are becoming self-driving, and drones processing their images on the fly!

In this track we explore use-cases of machine learning that touch consumers directly. We will explore algorithms that work on drones and cars, algorithms that use a human in their loop, use where you live as input, or even augment our reality. We will look both at the amazing applications AI is applied to, as well as best practices and tools you should use to bring your own application from the virtual into the physical world.

Track Host: Roland Meertens

Machine Learning Engineer @Autonomous Intelligent Driving

Roland Meertens is Machine Learning Engineer at Autonomous Intelligent Driving. He works on the machine learning side of the perception software stack that will be deployed to the autonomous vehicles that will soon roam urban environments in Germany.

9:00am - 9:40am

From Robot Simulation to the Real-World

Louise Poubel, Software Engineer @OpenRoboticsOrg

10:00am - 10:40am

Deep Learning on Microcontrollers

Pete Warden, Technical Lead of TensorFlow Mobile @Google

11:00am - 11:40am

10 Challenges for Real World Robotics

How developers are using the cloud to tackle top blockers of robotic deployments, and a call to action to solve the rest.

Douglas Fulop, Sr. Product Manager, AWS Robotics and Autonomous Services @Amazon

1:40pm - 2:20pm

Augmented Reality

Diana Hu, Leading AR Engineering @NianticLabs

2:40pm - 3:20pm


Jeremy Edberg, CEO and Founder @MinOpsInc

2019 Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.