Being an Engineer · 10 Nov 2023 · 57m · Episode 43
Kevin Surace, an AI keynote speaker and founder of Appvance, explains that large language models are statistical tools that amplify human intelligence rather than replace it, comparing them to calculators for language. He argues that AI will increase productivity, potentially leading to shorter workweeks, and helps engineers by generating novel design ideas, writing code, and improving quality assurance through computer vision. Surace emphasizes that while AI is not yet good at designing complex machinery, it excels at processing vast amounts of technical data to suggest solutions and automate routine tasks. He advises engineers to use AI as a co-pilot for communication, coding, and creative problem-solving to overcome skill gaps and accelerate their careers.
Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.
Engineering & simulationCode assistants (e.g., GitHub Copilot)50-60% improvement in productivity
Code assistants are used to write code for manufacturing equipment, resulting in a measured 50-60% improvement in coder productivity.
“All of the code helpers, codecs and others, one from Microsoft, etc. are showing a 50 6050 to 60% improvement in productivity from coders. And this has been measured over the last two years.”
Kevin Surace · Hear it at 19:40
Quality & testComputer vision systems / Deep neural nets
AI-powered vision systems are deployed on production lines to detect defects in real-time, providing accuracy that surpasses human inspection capabilities.
“if a human could do it on the line, a machine could do it, probably much better. Now, it may not be cost effective. But there is there is no human vision system that will outrun a computer vision system today, if properly coded and properly trained…”
Kevin Surace · Hear it at 33:18
DesignLarge Language Models (e.g., ChatGPT)
Engineers use LLMs to generate novel design ideas and manufacturing process suggestions by querying the model with specific constraints and pain points.
“I can ask it, how should I go about designing a part that does x? … it will do is give you five ideas that you never had … it's giving you ideas as if you were writing a novel.”
Kevin Surace · Hear it at 7:33
early-stage companies
Other shows on the same ground, closest first.
Bryan Cantrill, Adam Leventhal, and guests Keith Adams, Ashley Williams, and Jacob discuss the implications of GPT-4 and large language models for software engineering. They analyze the tool's strengths in code generation and debugging versus its hallucinations in niche domains like DTrace, while debating whether AI will replace engineers or serve as a productivity multiplier that lowers barriers to entry for new developers.
Kristen Edwards, CTO and co-founder of Pensa Technologies and MIT PhD candidate, discusses integrating AI into hardware engineering workflows, emphasizing the need to track decision reasoning and manage data governance. She explains that while AI acts as a 'co-pilot' to augment human expertise, high-consequence tasks require human oversight due to liability and explainability concerns. Edwards highlights specific challenges in spatial reasoning, design-for-manufacturing translation, and IP security, suggesting techniques like federated learning and retrieval-augmented generation to protect sensitive engineering data. The conversation covers practical applications in CAD, automated testing, and quality assurance, noting that AI will likely replace repetitive tasks rather than creative engineering roles.
Milan Kocic, Founder and Head of Sixth Sense at Hexagon Manufacturing Intelligence, discusses the role of AI in advanced manufacturing and the challenges of the US workforce. He highlights the Sixth Sense accelerator program, which supports startups like GelSight, and explains how generative AI tools from companies like Rafinex and Caster are augmenting design engineers rather than replacing them. Kocic argues that the future of manufacturing lies in collaborative robotics and AI-driven efficiency to address labor shortages and reshape the industry's public perception.
Jeff Gable, Luca, and Ryan Torvik discuss integrating Large Language Models (LLMs) into embedded systems engineering workflows, emphasizing that AI must augment rather than replace rigorous engineering discipline. They identify 'vibe coding' as a critical anti-pattern where developers lose control of architectural decisions by letting AI generate too much code at once, leading to unmanageable outputs and hallucinations. The hosts advocate for strict context management, test-driven development (TDD), and small, incremental commits to maintain deterministic quality, particularly in safety-critical domains like medical devices and automotive systems. They conclude that while LLMs accelerate prototyping and boilerplate generation, human code review and ownership remain non-negotiable for reliable production software.
John Mitchell, President and CEO of IPC, discusses the intersection of AI, talent shortages, and training in the electronics manufacturing services (EMS) industry at Electronica 2024. He argues that AI should act as an accelerant for new engineers by capturing tribal knowledge from retiring experts, rather than replacing human workers. Mitchell highlights how AI can personalize training by identifying specific skill gaps, drawing parallels to language learning apps and fast fashion supply chain optimization.
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