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On this episode, we’re joined by Jean Marc Alkazzi, Applied AI at idealworks. Jean focuses his attention on applied AI, leveraging the use of autonomous mobile robots (AMRs) to improve efficiency within factories and more.
We discuss:
- Use cases for autonomous mobile robots (AMRs) and how to manage a fleet of them.
- How AMRs interact with humans working in warehouses.
- The challenges of building and deploying autonomous robots.
- Computer vision vs. other types of localization technology for robots.
- The purpose and types of simulation environments for robotic testing.
- The importance of aligning a robotic fleet’s workflow with concrete business objectives.
- What the update process looks like for robots.
- The importance of avoiding your own biases when developing and testing AMRs.
- The challenges associated with troubleshooting ML systems.
Resources:
Jean Marc Alkazzi - https://www.linkedin.com/in/jeanmarcjeanazzi/
idealworks |LinkedIn - https://www.linkedin.com/company/idealworks-gmbh/
idealworks | Website - https://idealworks.com/
Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.
#OCR #DeepLearning #AI #Modeling #ML
By Lukas Biewald4.8
6868 ratings
On this episode, we’re joined by Jean Marc Alkazzi, Applied AI at idealworks. Jean focuses his attention on applied AI, leveraging the use of autonomous mobile robots (AMRs) to improve efficiency within factories and more.
We discuss:
- Use cases for autonomous mobile robots (AMRs) and how to manage a fleet of them.
- How AMRs interact with humans working in warehouses.
- The challenges of building and deploying autonomous robots.
- Computer vision vs. other types of localization technology for robots.
- The purpose and types of simulation environments for robotic testing.
- The importance of aligning a robotic fleet’s workflow with concrete business objectives.
- What the update process looks like for robots.
- The importance of avoiding your own biases when developing and testing AMRs.
- The challenges associated with troubleshooting ML systems.
Resources:
Jean Marc Alkazzi - https://www.linkedin.com/in/jeanmarcjeanazzi/
idealworks |LinkedIn - https://www.linkedin.com/company/idealworks-gmbh/
idealworks | Website - https://idealworks.com/
Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.
#OCR #DeepLearning #AI #Modeling #ML

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