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Welcome to Episode 71 of The Robotic Report Podcast, which brings conversations with robotics innovators straight to you. Be part of us every week for discussions with main roboticists, progressive robotics firms, and different key members of the robotics neighborhood.
In right now’s episode, Steve and Mike interview Rashed Haq, VP of robotics for Cruise. Rashed discusses the present deployment of Cruise autonomous robotaxis in San Francisco and what the corporate expects to be taught from the expertise because it step by step expands its working scope.
He additionally discusses the assorted challenges in creating and optimizing viable machine studying fashions that make selections within the protected operation of autonomous autos. We discuss the entire use circumstances that need to be thought of for a AV whereas it’s driving, and a few of the ways in which the Cruise robotics workforce is dealing with them.
As an AI professional and writer, we additionally speak to Rashed concerning the distinctive nature of gathering coaching information and coaching new fashions to make use of in machine studying and AI-based algorithms. Rashed shares some beneficial perception into the very best practices outlined in his ebook and put into observe at Cruise.
Lastly, we speak to Rashed about his visible artwork, together with the fascinating utility of synthetic intelligence within the technology of computational pictures.
Steve and Mike additionally speak concerning the newest robotics information tales from the final week.
Hyperlinks from the present this week:
Right here’s a pattern of considered one of Rashed’s computation photography-based, AI-generated pictures:
One picture from a collection of laptop generated pictures created by coaching an AI GAN after which asking it to generate a brand new picture. | Credit score: Rashed Haq
Together with his artwork, Rashed is exploring the rising space of computation pictures. Computational pictures leverages computer systems to mechanically improve, enrich or generate photographic pictures. Rashed leverages his ability as a machine studying engineer to create a brand new mannequin and prepare a generative adversarial community (GAN) based mostly on his prior set of pictures. With the GAN, Rashed can then create an algorithm that makes use of the data of the GAN to generate a model new picture from the set of pictures that had been used to coach the GAN.
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