Bryan discusses what constitutes industrial AI, its applications, and how it differs from standard AI processes. We explore the innovative process of deep reinforcement learning (DRL), replicating human expertise with machines, and the types of AI approaches available. Gain insights into the current trends and the future of generative AI, the existing gaps and opportunities, why DRL is a game-changer and much more! Join us as we unpack the nuances of industrial AI, its vast potential, and how it is shaping the industries of tomorrow. Tune in now!
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 [AI] as you are either making something or you are moving something.” — Bryan DeBois [0:04:36]
“One of the key distinctions with deep reinforcement learning is that it learns by doing and not by data.” — Bryan DeBois [0:10:22]
“Autonomous AI is more of a technique than a technology.” — Bryan DeBois [0:16:00]
“We have to have [AI] systems that we can count on, that work within constraints, and give right answers every time.” — Bryan DeBois [0:29:04]
Links Mentioned in Today’s Episode:
Bryan DeBois on LinkedIn
Bryan DeBois Email
RoviSys
RoviSys AI
Designing Autonomous AI
How AI Happens
Sama
