Why More Data Isn't Enough - AI, Parametric CAD, and the Ethics of What We Build

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In this episode, we sit down with Nomi Yu, a researcher who recently graduated from MIT, where she was co-advised between the DeCo Lab and the Mechanosynthesis group, to explore how AI can enable better parametric CAD generation and why the ethical development of these technologies matters just as much as their technical capability. Nomi shares insights from her work on GenCAD 3D and the challenge of training AI models when usable CAD data is scarce. We discuss why simply having more data isn't enough, how synthetic datasets can address critical biases, and the potential of federated learning to let companies collaborate on training models without ever sharing proprietary IP. We also dig into the future of engineering workflows, including why the most successful companies will use AI as a starting point rather than a replacement, and the parallels between "vibe coding" in software and what could become "vibe engineering" in hardware design. In this episode, we cover: * Why data quality and bias correction matter more than data quantity for training CAD generation models * How federated learning could unlock cross-company collaboration without compromising IP * The case for engineers deepening foundational knowledge rather than racing to automate everything Links from the show: https://decode.mit.edu/ [https://decode.mit.edu/] Get in touch: Nomi Yu https://www.linkedin.com/in/nomiyua6175aadf85/ [https://www.linkedin.com/in/nomiyua6175aadf85/] Raihaan Usman https://www.linkedin.com/in/raihaan-usman/ [https://www.linkedin.com/in/raihaan-usman/] Chapters ➡️ 00:00 Introduction to the Blueprint Podcast 00:20 Nomi's Journey in AI and Engineering 03:07 Understanding GenCAD and Parametric Design 05:53 Data Quality and Collaboration in AI 10:27 Challenges in Cross-Domain Learning 12:41 Future of Engineering with AI 16:29 Onshape & Their Dataset 19:26 The Future of Engineering AI 26:39 Verification and Trust in AI Systems 34:52 The Future of Engineering Education 43:48 Responsible AI Development and Ethical Considerations

Right Turns and Unprotected Lefts: Where AI Can (and Can't) Replace Engineers | Mark Fuge

In this episode, we sit down with Mark Fuge, Professor of Mechanical Engineering at ETH Zurich and Chair of Artificial Intelligence in Engineering Design, to explore the gap between what industry needs from AI and what academia is actually delivering, and why closing that gap starts with getting both sides in the same room. Mark shares his journey from writing Perl scripts at GE Aviation to pioneering ML for mechanical engineering back when the idea got you laughed out of job interviews. We discuss his concept of "use-inspired basic research," why the three fundamental challenges for AI in engineering are composition, abstraction, and uncertainty management, and how a helicopter manufacturer's request in 2018 led to an ETH course where students tackle real multi-physics design problems with today's AI tools. We also dig into his "right turns vs. unprotected lefts" analogy for understanding where AI can reliably take over and where human judgment remains essential, why taste and design thinking will matter more as building becomes free, and his vision for AI-driven personalised medical devices that could transform care for children with congenital heart disease. In this episode, we cover: * Why AI in engineering is where FEA was in the 1960s, and what that means for adoption timelines * How engineers are shifting from builders to architects, and why understanding the problem matters more than unlimited compute * The case for bringing industry and academia together to define the real research questions, not just the interesting ones Links from the show: Get in Touch: Mark Fuge https://www.linkedin.com/in/markfuge/ [https://www.linkedin.com/in/markfuge/] Conference Link & Topics https://event.asme.org/IDETC-CIE [https://event.asme.org/IDETC-CIE] https://idetc.secure-platform.com/a/page/tracks_topics [https://idetc.secure-platform.com/a/page/tracks_topics] Martin Bielicki https://www.linkedin.com/in/martin-bielicki/ [https://www.linkedin.com/in/martin-bielicki/] Chapters ➡️ 00:00 Introduction to AI in Engineering 02:25 Mark Fuge's Journey into AI and Engineering Design 04:03 Bridging the Gap: Industry Needs vs. Academic Research 07:29 Understanding Industry Challenges in Engineering 09:32 Emerging Solutions and Startups in Engineering AI 11:50 Innovative Teaching: Preparing Students for the Future 16:21 The Evolving Role of Engineers in the AI Era 21:56 Perceptions of AI in Engineering 25:50 The Future of Human-AI Collaboration 30:23 Vision for Humanity: Engineering a Better Future

30. mar. 202634 min

Why More Data Isn't Enough - AI, Parametric CAD, and the Ethics of What We Build | Nomi Yu

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