Industrial AI Podcast

Richard Rovner, Vice President of Marketing at MathWorks with a 25-year tenure, dives into how AI has transformed engineering. He traces the evolution from 90s neural networks to today's AI copilots and model-based design. Richard shares insights on the significance of generative AI and the crucial human role amid automation. He discusses real-world applications like Coca-Cola's machine learning innovations and the upcoming Simulink Copilot. As engineering roles evolve, he highlights the need for AI literacy and cross-discipline collaboration in this rapidly shifting landscape.

Gabriel Krummenacher, Director of data science at Zühlke, delves into the complexities of industrial AI adoption. He discusses a survey revealing that operational efficiency is the top impactful AI use case. Gabriel emphasizes the necessity of strong data science teams to leverage GenAI, noting that ethical frameworks significantly enhance success rates. He highlights differing regional approaches to AI and stresses the importance of measurable KPIs and leveraging internal data for impactful custom applications. Tune in for insights on how to navigate the AI landscape effectively!

Timo Gessmann, CTO of Schunk, discusses blending mechanical design with AI to enhance robotics and the challenges of recruiting top talent for industrial AI roles. He emphasizes the need for strong European robotic models and outlines Schunk's vision for automation. Phillip Lippe from Google DeepMind shares insights on generative AI and the importance of foundation models in creating domain-specific intelligence. They also explore the future of AI scaling, hybrid architectures, and the potential safety implications in applications across various industries.

Jakub Tomczak, a senior research scientist at the Chan Zuckerberg Initiative and former professor, dives into the fascinating world of generative AI. He discusses how time series analysis represents the next frontier for AI, emphasizing its importance in understanding sequences and making predictions. Jakub challenges traditional machine learning paradigms and advocates for continuous learning systems that can adapt dynamically. Also explored are the implications of LLMs as tool-call interfaces and the critical need for high-quality data in AI applications.

In this engaging discussion, Christian Wagner, Managing Director at Vogt Ultrasonics, and Jan Kutnik, Founder of Evoptima, delve into how AI is transforming spot weld inspection in the automotive industry. They tackle the challenges of massive ultrasonic datasets and share how AI enhances prediction accuracy by 20-30%. The duo explores the integration of AI with next-gen inspection devices and the role of collaborative robots. They also discuss the value of robust partnerships and courageous innovation for mid-sized companies navigating AI adoption.

Andy, the co-founder and CEO of Path Robotics, shares insights from his groundbreaking work in AI-driven welding robots. He discusses the importance of adaptability in manufacturing and how real-world data significantly enhances AI performance. The conversation reveals how Path's Obsidian model integrates multimodal sensors—using everything from sound to vision—for precision. Andy also highlights the challenges of training robots on diverse materials and the impressive outcomes, like a 98% first-pass yield, demonstrating the future of robotics in industry.

In this engaging discussion, Armin Hadzalic, a Senior Software Developer at Siemens, shares his insights on the transformative Model Context Protocol (MCP) for industrial AI. He explains how MCP connects large language models with real-world data and tackles the challenges of integrating multiple systems. Armin delves into the future of seamless production systems powered by agents and AI, while also addressing criticisms of MCP. With practical advice for engineers, he advocates for an early embrace of these technologies to stay ahead in an evolving industry.

From parades to protocols, we explore AI literacy, regulation, and the future of work. Discover how Europe’s AI Act is reshaping industry. 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. DeepMind https://www.deepmind.com/ Ray Kurzweil https://en.wikipedia.org/wiki/Ray_Kurzweil Physics-Informed Neural Networks (PINNs) https://www.nature.com/articles/s42254-021-00314-5 Olga Fink https://people.epfl.ch/olga.fink EPFL (École Polytechnique Fédérale de Lausanne) https://www.epfl.ch/en/ Model Context Protocol (MCP) https://github.com/modelcontext/protocol ABB Robotics https://new.abb.com/products/robotics Landing AI https://landing.ai/ Andrew Ng https://www.andrewng.org/ BMW Neue Klasse https://www.bmw.com/en/innovation/bmw-neue-klasse.html ASML https://www.asml.com/ NVIDIA https://www.nvidia.com/ Intel https://www.intel.com/ AMD https://www.amd.com/ Elements of AI https://www.elementsofai.com/ MinnaLearn https://www.minnalearn.com/ EU AI Act https://artificialintelligenceact.eu/ GDPR (General Data Protection Regulation) https://gdpr.eu/

Jens Stapelfeldt, Global AI Lead for AMD's University Program, discusses AMD's innovative approach to industrial AI. With decades of experience in the semiconductor industry, he outlines the shift from proprietary ecosystems to AMD’s open platforms. Jens highlights the importance of ROCm and strategic partnerships in enhancing edge-to-cloud solutions. He emphasizes how diverse hardware options and open-source principles are reshaping industrial use cases, making AMD a versatile player in the AI landscape.

From Paris to Montreal, we explore the future of generative AI in material science. Innovation, impact, and the Parisian startup spirit await. Thanks for listening. We welcome suggestions for topics, criticism and a few stars on Apple, Spotify and Co. We thank our partner SIEMENS The Course Robert mentioned https://jmtomczak.github.io/crash-course-diffusion/