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Track: AI Meets the Physical World

Location: Cyril Magnin II

Day of week: Tuesday

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 process their images on the fly!

In this track we explore use-cases of machine learning that touch us 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 the physical world. 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 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.

10:40am - 10:50am

The Basics of ROS Applied to Self-Driving Cars

Anthony Navarro, Product Lead- Robotics and Self-Driving Car @Udacity

11:00am - 11:50am

Self-Racing using Deep Neural Networks: Lap 2

Anthony Navarro, Product Lead- Robotics and Self-Driving Car @Udacity
Jendrik Jördening, Data Scientist @Nooxit

12:50pm - 1:00pm

NVIDIA Jetson

  • Quick Overview of the TX2 hardware and TX2 Dev Kit
  • Software that runs on the TX2 Devkit

Examples: Supervised DL for Classification, Segmentation from the camera and RL projects

Dana Sheahen, Robotics @Udacity

1:10pm - 2:00pm

Liquidity Modeling in Real Estate Using Survival Analysis

David Lundgren, Data Scientist @Opendoor
Xinlu Huang, Data Scientist @Opendoor

2:25pm - 2:35pm

Constraints of Building a Modern Drone

Alexander Harmsen, CEO/Founder @iris_automation

2:45pm - 3:35pm

Dive Deep into Building Computer Vision Systems for Self-flying Drones

Every industrial drone in the world needs to see the world the way a pilot does, an obvious, but non-trivial feat of engineering. In this talk the challenge of building a computer vision/AI "sense and avoid" system is discussed, along with the challenges of a constrained cost/size/weight/power design environment. It’ll dive into deep learning training, simulation, and real-world aircraft testing with near-mid-air collisions, in order to keep these drones safe while they get integrated into the national airspace!

Alexander Harmsen, CEO/Founder @iris_automation

4:20pm - 5:10pm

Deep Learning for Science

How is deep learning affecting the world of science? In this talk, Prabhat (Data and Analytics team lead at NERSC, Berkeley Lab) discusses machine learning's impact on climatology, astronomy, cosmology, neuroscience, genomics, and high-energy physics. The talk will conclude with a list of open challenges in applied Deep Learning, and the future of AI in powering scientific discoveries.

Prabhat , Data and Analytics Group Lead @NERSC

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.