Handling different materials and transfer learning

From industrial skepticism to breakthrough robotics—why adaptability and real-world data change everything. Discover what’s next for manufacturing AI.

In this episode, we dive into the realities behind industrial AI adoption—both the successes and the setbacks. We share candid conversations with engineers frustrated by unreliable AI and explore why real-world robustness remains the central challenge. Then, I sit down with Andy from Path Robotics, who reveals how their AI-driven welding robots are redefining adaptability and reliability on the shop floor. We unpack what makes their approach unique, from multi-modal sensors to reinforcement learning and massive real-world datasets. If you care about the future of manufacturing, data-driven automation, and the evolution of industrial roles, you won’t want to miss this discussion. Join us as we look ahead to what’s possible when AI finally delivers on its industrial promise.

Siemens

https://new.siemens.com/global/en.html

Path Robotics

https://www.path-robotics.com/

Obsidian (Path Robotics AI Model)

https://www.path-robotics.com/technology/

Yaskawa

https://www.yaskawa.com/

Universal Robots

https://www.universal-robots.com/

ABB

https://global.abb/group/en

KUKA

https://www.kuka.com/

FANUC

https://www.fanuc.eu/

Reinforcement Learning

https://en.wikipedia.org/wiki/Reinforcement_learning

World Models (AI)

https://worldmodels.github.io/

Industrial AI Podcast

https://www.industrial-ai-podcast.com/

Machine Learning Week

https://machinelearningweek.eu/

Frankfurter Allgemeine Zeitung (FAZ)

https://www.faz.net/

Rich Sutton's 'The Bitter Lesson'

http://www.incompleteideas.net/IncIdeas/BitterLesson.html

XLSTM Scaling Laws Paper

https://arxiv.org/abs/2404.07143

Embraceable AI

https://embraceable.ai/

Deloitte Agentic Framework

https://www2.deloitte.com/global/en/pages/about-deloitte/articles/agentic-ai.html

Isaac Asimov's 'Sally'

https://en.wikipedia.org/wiki/Sally(shortstory)