
HM26: Whitelabeled AI and the hope for the API
Apr 29, 2026 - 38:41
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In this episode, Robert sit down with Nick Tebeau from BizLink to explore how AI and machine learning are revolutionizing something as fundamental as cable management in industrial robotics. We dive into why predictive m...
When Cables Meet AI: Reinventing Robot Reliability is an episode from KI in der Industrie by Robert Weber / Peter Seeberg. In this episode, Robert sit down with Nick Tebeau from BizLink to explore how AI and machine learning are revolutioni...
This episode belongs to KI in der Industrie.
Use the player on this page to stream the episode online.
Published Dec 10, 2025, 18:49 long, audio available.
In this episode, Robert sit down with Nick Tebeau from BizLink to explore how AI and machine learning are revolutionizing something as fundamental as cable management in industrial robotics. We dive into why predictive maintenance matters, how edge devices can deliver real-time insights, and what it takes to keep production lines running smoothly without ever touching the robot controller. You'll hear about the challenges of creating adaptable AI models for complex manufacturing environments and how customer needs are shaping the future of smart factories. If you're curious about the ROI of industrial AI, the realities of edge computing, or how even the simplest hardware can become a game-changer, this conversation is packed with practical takeaways and forward-looking ideas.
You can listen to When Cables Meet AI: Reinventing Robot Reliability online on Radio and Podcast. Open the player on this page to stream the available audio.
When Cables Meet AI: Reinventing Robot Reliability is an episode from KI in der Industrie by Robert Weber / Peter Seeberg.
This episode is 18:49 long.
This episode was published on Dec 10, 2025.
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You can listen to When Cables Meet AI: Reinventing Robot Reliability on this page when the episode audio is available from the podcast feed.
When Cables Meet AI: Reinventing Robot Reliability is from KI in der Industrie by Robert Weber / Peter Seeberg.
Published Dec 10, 2025 and 18:49 long