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: AI Meets the Physical World
Location: Embarcadero
Day of week: Wednesday
Track Host: Roland Meertens
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
10:00am - 10:40am
Deep Learning on Microcontrollers
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.
12:00pm - 12:40pm
AI Meets the Physical World Panel
2:40pm - 3:20pm
AI Meets the Physical World Presentation
2019 Tracks
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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.
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Deep Learning in Practice
Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.
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AI Meets the Physical World
Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.
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Papers in Production: Modern CS in the Real World
Groundbreaking papers make real-world impact.
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Solving Software Engineering Problems with Machine Learning
Interesting machine learning use cases changing how we develop software today, including planned topics touching on infrastructure optimization, developer experience, security, and more.
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Predictive Architectures in the Real World
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.