In this episode, I sit down with Richard Rovner, Vice President of Marketing at MathWorks, to explore the fascinating evolution of AI in engineering. We trace the technology’s journey from its early days in the 1980s, through transformative milestones like deep learning and code generation, all the way to today's AI-driven design tools and copilots.

Richard shares insider perspectives on how AI is changing the engineer’s workflow, what generative AI means for the future, and why the human touch remains essential in a world of accelerating automation. If you’re curious about where engineering and AI are headed—or how to stay ahead in this rapidly shifting landscape—this conversation is packed with insights you won’t want to miss.

https://new.siemens.com/global/en/company/topic-areas/artificial-intelligence.html

https://www.mathworks.com/

https://www.mathworks.com/products/matlab.html

https://www.mathworks.com/products/simulink.html

https://www.mathworks.com/products/neural-network.html

https://www.mathworks.com/products/deep-learning.html

https://www.mathworks.com/products/statistics.html

https://www.mathworks.com/products/text-analytics.html

https://www.mathworks.com/products/matlab-copilot.html

https://www.sas.com/

https://www.faw-ulm.de/

https://www.coca-colafreestyle.com/

https://www.arm.com/products/silicon-ip-cpu/cortex-m

https://www.mpg.de/en

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

https://www.mathworks.com/solutions/model-based-design.html

In this episode, we dive deep into the real-world state of AI adoption across industries, unpacking the findings of a major new survey with Gabriel Krummenacher. We explore which AI use cases actually move the needle—hint: operational efficiency tops the list—and why simply chasing the latest GenAI trends won't deliver results without a solid data science foundation.

You'll hear our honest takes on what separates hype from impact, why ethical frameworks and governance drive innovation (not stifle it), and how US and European companies are taking very different paths with AI. If you're wondering where AI is truly transforming business—and what it really takes to succeed—this episode is for you.

https://www.quantum-systems.com/

https://www.kaggle.com/

https://www.anthropic.com/

https://www.deepmind.com/research/highlighted-research/alphafold

https://www.trumpf.com/

https://www.asml.com/

https://www.qualcomm.com/

https://ethz.ch/en.html

https://www.zuehlke.com/

https://openai.com/

https://www.cse.unsw.edu.au/~tw/

In this episode, I sit down with Schunk CTO Timo Gessmann and Google DeepMind’s Phillip Lippe to explore how AI is transforming the world of robotics and industrial automation. We kick things off with Timo, who shares his vision for merging mechanical expertise with AI, the challenges of attracting top talent, and why Europe needs its own world-class robotics models.

Then, I’m joined by Phillip Lippe from the Gemini team, who dives into the latest advances in generative AI, the power of foundation models, and what the future holds for scalable, domain-specific intelligence. Whether you’re fascinated by embodied AI, curious about industry trends, or want a peek into the minds shaping tomorrow’s robots, this episode is packed with insights you won’t want to miss.

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

https://schunk.com/

https://deepmind.google/technologies/gemini/

https://deepmind.google/

https://www.nvidia.com/

https://www.ros.org/

https://automatica-munich.com/en/

https://www.bosch.com/

https://www.european-processor-initiative.eu/

https://www.nvidia.com/gtc/

https://www.nxai.io/

https://research.samsung.com/artificial-intelligence

https://nips.cc/

We spent time at our event in the Black Forest, where innovation met inspiration at our latest industrial AI gathering. In this episode, we share stories—from empowering autistic talent in data annotation to the technical leaps in foundation models for time series. You’ll hear from leading minds like Jakub Tomczak, who flew in from San Francisco to challenge what’s possible in generative AI, and explore how the landscape is shifting from traditional machine learning to adaptive, context-rich automation.

Join us as we question what makes AI truly intelligent, debate the role of human factors, and look ahead to the next wave of industrial transformation. If you’re curious about how AI is evolving in real factories, how new job profiles are emerging, or why continuous learning and time series matter more than ever, this conversation is for you.

Thanks for listening. We welcome suggestions for topics, criticism and a few stars on Apple, Spotify and Co.

We thank our partner SIEMENS

In this episode, I dive into the fascinating world of non-destructive material testing with Christian Wagner from Vogt Ultrasonics and Jan Kutnik from Evoptima. Together, we explore how AI is revolutionizing spot weld inspection in the automotive industry, making quality control smarter and more precise than ever before.

We discuss the challenges of processing massive ultrasonic datasets, adapting AI models to real-world manufacturing, and the journey from legacy systems to cutting-edge solutions. If you’re curious about how collaborative robots, vision systems, and robust AI are reshaping factory floors—and what it takes for mid-sized companies to lead the charge—this is an episode you won’t want to miss.

https://new.siemens.com/global/en/products/automation/topic-areas/industrial-ai.html

https://www.vogt-ultrasonics.de/en/

https://evoptima.com/

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

https://en.wikipedia.org/wiki/Longshort-termmemory

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

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

https://www.cvut.cz/en

https://en.ids-imaging.com/

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.

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

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

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

https://www.yaskawa.com/

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

https://global.abb/group/en

https://www.kuka.com/

https://www.fanuc.eu/

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

https://worldmodels.github.io/

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

https://machinelearningweek.eu/

https://www.faz.net/

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

https://arxiv.org/abs/2404.07143

https://embraceable.ai/

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

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

Thanks for listening. We welcome suggestions for topics, criticism and a few stars on Apple, Spotify and Co.

We thank our partner SIEMENS

More Links

https://modelcontextprotocol.io/specification/2025-06-18

In this episode, I sit down with Armin Hadzalic to tackle the burning question: will the Model Context Protocol (MCP) truly transform industrial AI? We dive deep into how MCP connects large language models and agents to real-world data, what makes it different from protocols like OPC UA and MQTT, and why the biggest players in tech are rallying behind it. Armin shares practical examples, addresses skeptics, and explains what MCP means for engineers, technicians, and anyone working in industrial automation. If you're curious about the rise of agents, the future of dashboards, and what it takes to stay ahead in the evolving world of industrial AI, you won't want to miss this conversation. Join me as we explore where MCP is headed and what it could mean for your job and your factory.

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

https://github.com/anthropics/mcp

https://www.anthropic.com/

https://opcfoundation.org/about/opc-technologies/opc-ua/

https://mqtt.org/

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

https://aws.amazon.com/

https://www.microsoft.com/

https://www.google.com/

https://openai.com/

https://github.com/agent-protocol/agent-protocol

https://github.com/agent-protocol/agent-network-protocol

https://github.com/anthropics/a2a

https://github.com/google/ap-ii

https://www.viad.com/

https://www.viad.com/insights/living-factories-driven-by-agents/

https://www.bmwgroup.com/en/company/production.html

Thanks for listening. We welcome suggestions for topics, criticism and a few stars on Apple, Spotify and Co.

We thank our partner SIEMENS

In this episode, we dive into the evolving landscape of industrial AI, starting with a lively Oktoberfest recap before shifting gears to the latest breakthroughs in physics-informed neural networks and user interfaces. We discuss the real-world impact of Europe's AI Act, featuring insights from industry leaders and an in-depth interview with Sampo Lino of MinnaLearn on building AI literacy for enterprises. As we unpack strategic investments, robotics trends, and the challenges of compliance, we question what it means to use AI safely and competitively. Throughout the conversation, we keep it grounded in everyday experience—how regulation, technology, and practical learning are shaping the factories and workplaces of tomorrow. Tune in to hear how we’re navigating this complex, fast-moving frontier and what it means for anyone working with AI today.

https://www.deepmind.com/

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

https://www.nature.com/articles/s42254-021-00314-5

https://people.epfl.ch/olga.fink

https://www.epfl.ch/en/

https://github.com/modelcontext/protocol

https://new.abb.com/products/robotics

https://landing.ai/

https://www.andrewng.org/

https://www.bmw.com/en/innovation/bmw-neue-klasse.html

https://www.asml.com/

https://www.nvidia.com/

https://www.intel.com/

https://www.amd.com/

https://www.elementsofai.com/

https://www.minnalearn.com/

https://artificialintelligenceact.eu/

https://gdpr.eu/

In this episode, I sit down with Jens Stapelfeldt, Global AI Lead at AMD's University Program, to explore how AMD is challenging the status quo in AI hardware. We dive into the evolution from proprietary ecosystems to AMD's open, enterprise-ready platforms, discussing what this means for industrial AI applications and decision makers. Jens shares insights on AMD’s broad AI portfolio, from GPUs to edge solutions, and how strategic collaborations are accelerating innovation. We also examine the significance of open-source principles and the impact of diverse hardware options on industrial use cases. Join me as we uncover why AMD positions itself as the most versatile player in the AI space, from edge devices all the way to the cloud.

Thanks for listening. We welcome suggestions for topics, criticism and a few stars on Apple, Spotify and Co.

We thank our partner SIEMENS

https://www.amd.com/en/technologies/artificial-intelligence

https://www.xilinx.com/applications/artificial-intelligence.html

https://en.wikipedia.org/wiki/Field-programmablegatearray

https://developer.nvidia.com/cuda-zone

https://rocm.docs.amd.com/en/latest/

https://pytorch.org/

https://vllm.ai/

https://www.lamini.ai/

https://silo.ai/

https://ai.facebook.com/

https://www.microsoft.com/en-us/ai

https://www.oracle.com/artificial-intelligence/

https://www.amd.com/en/products/ryzen-ai

https://www.amd.com/en/products/instinct-accelerators

https://www.xilinx.com/products/silicon-devices/fpga/virtex.html