December 12, 2023
min LISTEN

RoviSys Director of Industrial AI Bryan DeBois

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EPISODE SUMMARY

Jason & Duncan discuss the results of Sama's ML Pulse Report, focusing on how to measure model effectiveness, how to increase confidence when moving models into production, and whether they believe generative AI is worth the hype.

EPISODE NOTES

Joining us today are our panelists, Duncan Curtis, SVP of AI products and technology at Sama, and Jason Corso, a professor of robotics, electrical engineering, and computer science at the University of Michigan. Jason is also the chief science officer at Voxel51, an AI software company specializing in developer tools for machine learning. We use today’s conversation to discuss the findings of the latest Machine Learning (ML) Pulse report, published each year by our friends at Sama. This year’s report focused on the role of generative AI by surveying thousands of practitioners in this space.Key Points From This Episode:

  • Bryan’s professional background and his role in the company.
  • Unpack the concept of “industrial AI” and its various applications.
  • The current state and trends of AI in the industrial landscape.
  • Deep reinforcement learning (DRL) and how it applies to industrial AI.
  • Why deep RL is a game-changer for solving industrial problems.
  • Learn about autonomous AI, machine teaching, and explainable AI.
  • Discover the approach for replicating human expertise with machines.
  • Opportunities and challenges of using machine teaching techniques.
  • Differences between monolithic deep learning and standard deep learning.
  • His perspective on current trends and the future of generative AI.

Quotes:“We typically look at industrial as you are either making something or you are moving something.” — Bryan DeBois “One of the key distinctions with deep reinforcement learning is that it learns by doing and not by data.” — Bryan DeBois “Autonomous AI is more of a technique than a technology.” — Bryan DeBois “We have to have systems that we can count on, that work within constraints, and give right answers every time.” — Bryan DeBois

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