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

Simulation is one of the most powerful tools in the robot developer's tool belt. Besides allowing quicker, safer and cheaper iterations, it can be used to prototype before building, run continuous integration, train machine learning algorithms, etc. One popular open source robotics simulator is Gazebo, maintained by Open Robotics, the same foundation that maintains ROS, the Robot Operating System. Combined, ROS and Gazebo are used by an increasing number of developers around the world.

Gazebo's development started over 17 years ago, but its most current form started taking shape in 2012, when DARPA sponsored Open Robotics to run the Virtual Robotics Challenge, the first stage of its Robotics Challenge. A total of 26 teams competed in the virtual competition, controlling an Atlas robot from Boston Dynamics in a simulated disaster scenario. As a result of the virtual competition, the top 7 teams received funding to compete in the final competition with the same robot, but this time a physical one in a physical scenario.

Ever since, Gazebo has continued to be developed and improved to better support various types of robots, spanning ground, water and air, and is being increasingly used by the academia, industries and, sure enough, in other competitions. In this talk, Louise will give an overview of Gazebo's architecture and go over some examples of projects using Gazebo which Open Robotics has been involved with, describing how they bridged virtual robots to their physical counterparts.

Louise Poubel, Software Engineer @OpenRoboticsOrg

10:00am - 10:40am

Deep Learning on Microcontrollers

Learn why deep learning is a great fit for tiny, cheap devices, what you can build with it, and how to get started.

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

12:00pm - 12:40pm

AI Meets the Physical World Panel

Panel details will follow soon.

1:40pm - 2:20pm

Augmented Reality

Presentation details will follow soon.

Diana Hu, Leading AR Engineering @NianticLabs

2:40pm - 3:20pm


Presentation details will follow soon.

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.