You are viewing content from a past/completed QCon

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.

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

2019 Tracks

  • 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

  • Deep Learning in Practice

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

  • Handling Sequential Data Like an Expert / ML Applied to Operations

    Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.

    Half-day tracks