Presentation: Constraints of Building a Modern Drone

Track: AI Meets the Physical World

Location: Cyril Magnin II

Duration: 2:25pm - 2:35pm

Day of week: Tuesday

Share this on:

Abstract

This short talk discusses where we are today in 2018 with drone development. The frame and the auto-piloting are actually the easy part. The real issues that remain include issues like processing power, control/communication over large distances, fleet management, regulatory approvals, and situational awareness. Interested in learning more about the real issues with drone development today, this talk will get you up to speed.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Alexander Harmsen

CEO/Founder @iris_automation

Alexander Harmsen is CEO and Co-Founder of Iris Automation, a high tech start-up building computer vision collision avoidance systems for industrial drones. With backing from Bessemer, Y Combinator, over $10M in private equity investment from other Silicon Valley investors, and operations in multiple countries, Iris Automation is attempting to radically disrupt the industrial drone sector. He also sits on the Board of Directors for Unmanned Systems Canada, a national industry representation organization that has been at the forefront of commercial unmanned systems for more than a decade.
Previously, Alexander was the first Software Developer at Matternet, a medical drone package delivery start-up, and worked on computer vision systems at NASA’s Jet Propulsion Lab in Los Angeles. He is very interested in intersections between drones, autonomous vehicles and real-world applications that will affect billions of people, always excited about meeting other people making big changes in the world!

Find Alexander Harmsen at

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.