Augmented Ops - Episodes Tagged with “Technology”

Hacking the Defense Bureaucracy: Software, Speed, and the Industrial Base

Tulip — Thu, 14 May 2026 00:15:00 -0400

Geopolitical pressure is reshaping how the US buys and builds for defense. Nick Sinai of Insight Partners breaks down the shift from cost-plus to commercial procurement, the rise of venture-backed defense tech, and how to move fast without losing safety.

In this episode of Augmented Ops, host Erik Mirandette, Tulip's Chief Business Officer, is joined by Nick Sinai, Managing Director at Insight Partners and co-author of Hack Your Bureaucracy. Before Insight, Nick spent nearly six years inside the Obama administration as US Deputy CTO, where he led the Open Data Initiative and helped stand up the Presidential Innovation Fellows program.

Nick breaks down what's actually changing in defense procurement under the second Trump administration, the rise of venture-backed defense tech now drawing tens of billions of dollars a year, and the shift from cost-plus to commercial-products buying. He explains why the traditional 15-year acquisition cycle no longer matches the pace of technology, and how companies like Anduril, Shield AI, and Palantir are reshaping what it means to be a defense prime.

The conversation also explores the tradeoff every modern supplier into defense has to navigate: how to move faster on cost and speed without junking the safety and compliance requirements that exist for good reason. Nick offers a grounded view from someone who's lived inside both the bureaucracy and the venture world, including what it actually takes for reform to stick across administrations.

The headlines are full of speeches about speed. Nick lays out what's genuinely different this time, what's likely to regress to the mean, and where operations leaders supplying into the defense base should be paying attention.

Watch the full episode on YouTube: https://youtu.be/Qu7bCfNpraA

Augmented Ops is a podcast for industrial leaders, citizen developers, shop floor operators, and anyone who cares about what the future of frontline operations will look like across industries. This show is presented by Tulip, the Frontline Operations Platform. You can find more from us at Tulip.co/podcast or by following the show on LinkedIn. Special Guest: Nick Sinai.

AI for Operations: From Everyday Tools to Agentic Systems

Tulip — Thu, 30 Apr 2026 00:00:00 -0400

Manufacturing is entering a new phase of AI adoption, one where intelligent systems don’t just generate insights but take action in context. In this episode of Augmented Ops, host Mason Glidden, Tulip’s Chief Product Officer, is joined by Olga Stroilova, Group Product Lead, and Pete Hartnett, Group Product Manager, to discuss how agentic AI is redefining what’s possible on the factory floor.

Together, they unpack the evolution from predictive and generative AI to agentic systems capable of autonomous, goal-driven behavior while keeping people firmly in the loop. They examine why many pilots stall before production, how governance and culture shape adoption, and why “human oversight by design” is becoming the new standard for responsible AI in manufacturing.

Drawing from Tulip’s own roadmap and customer experiences, the team highlights how features like AI Composer, Tulip Agents, and context-aware workflows are helping users close the insight-to-action gap, scale AI safely, and unlock new forms of operational leverage.

Rather than imagining a future without people, the episode points to a more realistic vision of AI in manufacturing: one where systems evolve, but human judgment remains the foundation of progress.

Augmented Ops is a podcast for industrial leaders, citizen developers, shop floor operators, and anyone else that cares about what the future of frontline operations will look like across industries. This show is presented by Tulip, the Frontline Operations Platform. You can find more from us at Tulip.co/podcast or by following the show on LinkedIn.
Special Guests: Olga Stroilova and Pete Hartnett.

The Physics Layer: Why AI Needs Real-World Engineering to Unlock Trillion-Dollar Industrial Value

Tulip — Thu, 16 Apr 2026 00:15:00 -0400

What do a floating barge the size of four aircraft carriers, a Shell refinery, and the future of energy resilience have in common? They all depend on knowing, with precision, how much life is left in the steel.

In this episode of Augmented Ops, host Natan Linder sits down with Thomas Leurent, CEO and co-founder of Akselos, to unpack the often-overlooked world of structural performance management and why it might be the most important form of physical AI you've never heard of.

Thomas shares how Akselos helped Shell unlock over half a billion dollars in value on a single FPSO by using physics-based digital twins to skip a costly dry dock. He explains the technology behind it: a proprietary approach that runs structural simulations 100,000x faster than traditional finite element analysis by blending machine learning with physics, built over 20+ years since the technology was pulled out of MIT.

The conversation goes deep on what physical AI actually means in industrial settings, why hallucination is simply not an option in high-stakes environments, the role of humans in process industries (especially in emergency scenarios like what's unfolding in the GCC), and how data sharing — or the lack of it — is holding back offshore wind and the broader energy transition.

Thomas also shares a bold prediction: just as algorithmic efficiency transformed mechanical simulation, it will do the same to AI, making large language models far cheaper to run, potentially leading to an overcapacity of computing infrastructure in the years ahead.

If you think "structural performance management" sounds dry, wait until you hear what a $500M dry-dock skip, 52,000 workers with zero casualties, and the future of energy supply chains have to say about it.

Watch the full episode on YouTube: https://youtu.be/cmhRmhyrU-Y

Modernizing the Industrial Base: Readiness, Resilience, and the Road Ahead

Tulip — Thu, 02 Apr 2026 00:15:00 -0400

Modernizing the U.S. industrial base is no longer a long-term goal. Between geopolitical competition, workforce constraints, and rising cybersecurity demands, manufacturers are under pressure to rethink how production systems are built and operated.

Clark Dressen, CTO of MxD, joins the show to explain how this transformation is taking shape across the defense industrial base and broader manufacturing ecosystem. As a public-private partnership funded in part by the Department of Defense, MxD works to connect emerging technologies with real-world production environments.

The conversation focuses on what modernization actually requires. Not digital transformation for its own sake, but applying technology to solve specific operational problems around quality, productivity, and consistency. Clark shares how tools like sensors, digital twins, and AI are being introduced into legacy environments to reduce reliance on tribal knowledge and create more repeatable processes.

The episode also explores the structure of the industrial base, where small and mid-sized suppliers make up the majority of the defense supply chain but often lack the resources to meet growing cybersecurity and compliance requirements. As workforce transitions accelerate, the focus shifts toward capturing expertise, improving how work is executed, and building more resilient production systems.

Watch the full episode on YouTube: https://youtu.be/T2bkZvyK5kU

Regional Ecosystems of Manufacturing: The Foundation of Industrial Strength

Tulip — Thu, 19 Mar 2026 00:15:00 -0400

What actually makes a region strong in manufacturing?

In this episode, Gillian Catrambone sits down with Beatriz Gutierrez, CEO of CONNSTEP Inc., Connecticut’s Manufacturing Extension Partnership (MEP), to explore how regional ecosystems, through MEPs, workforce programs, and coordinated resources, create the foundation for industrial strength.

Beatriz breaks down how manufacturers are navigating labor constraints, adopting automation incrementally, and rethinking supply chains in a more volatile environment. The conversation also highlights what separates effective regions, including strong talent pipelines, connected institutions, and easier access to capital, training, and support.

She closes with practical guidance for operations leaders. Focus on critical processes, plan for the long term, and approach transformation step by step rather than waiting for perfect conditions.

Watch the full episode on YouTube: https://youtu.be/ZJO0bbYSGII

The State of Reshoring: Supply Chains, Strategy, and the Future of US Manufacturing

Tulip — Thu, 05 Mar 2026 00:15:00 -0500

Global supply chains are being rewired in real time. From tariffs and geopolitics to labor constraints and energy infrastructure, manufacturers are navigating a level of volatility few have experienced before.

Rosemary Coates, Founder and Executive Director of the Reshoring Institute, joins the show to unpack what’s actually happening beneath the headlines. Drawing on recent executive interviews and location studies, she explains why many companies are pausing major decisions, how “China plus one” strategies are evolving, and what reshoring really requires beyond political rhetoric.

For operations leaders, the conversation moves from macro forces to practical considerations: evaluating total landed cost beyond labor, balancing capital intensity with workforce availability, selecting locations with infrastructure in mind, and building resilience through diversified manufacturing footprints. While the path forward is complex, Rosemary outlines why advanced, higher-skilled manufacturing still presents meaningful opportunity for U.S. growth.

Watch the full epsiode on YouTube

For more on this topic, Rosemary hosts The Frictionless Supply Chain podcast, covering supply chain strategy and global production shifts.

From the Classroom to the Shop Floor: Building the Future Industrial Workforce

Tulip — Wed, 18 Feb 2026 00:15:00 -0500

Manufacturing is undergoing a generational shift. As experienced workers retire and automation accelerates, the industry must solve both a workforce shortage and a skills gap — and it must do so simultaneously.

Jacob “MFG Kid” Sanchez is a well-known manufacturing influencer and content creator, and a vocal advocate for bringing new talent into the industry. With hands-on shop floor experience and a growing platform dedicated to promoting automation and modern manufacturing careers, he works to make the industry more visible, accessible, and appealing to the next generation.

Check out Jacob’s newly launched Axis community — a brand-neutral space for automation, robotics, and manufacturing professionals to connect, learn, and collaborate.

In this conversation, Jacob and Natan explore how manufacturers can generate genuine interest in industrial careers, rethink how technical skills are taught and developed, and draw lessons from apprenticeship models in countries that consistently produce highly skilled manufacturing talent.

Watch the full episode on YouTube

The Need for Speed in Life Sciences

Tulip — Thu, 05 Feb 2026 00:15:00 -0500

The life sciences industry has long justified slow digital adoption through regulation. But as technology adoption accelerates faster than guidance, that logic is breaking down.

In this episode, Michelle Vuolo and Gilad Langer discuss why speed has become a defining challenge for pharma and medical device manufacturers. Drawing on experience from ISPE, quality leadership, and decades in regulated operations, they explore validation 4.0, cultural resistance to risk-based thinking, and how AI is reshaping quality and compliance work.

The conversation examines what it really takes for life sciences organizations to move faster without losing control — and why waiting for perfect regulatory clarity is no longer a viable strategy

Watch the full episode on YouTube: https://youtu.be/SPJz8_cFYM4

The Human Infrastructure of Manufacturing with Stacey Weismiller of AMFI

Tulip — Thu, 22 Jan 2026 00:15:00 -0500

Manufacturing is often discussed in terms of technology, productivity, and investment — but rarely in terms of people as infrastructure. In this episode of Augmented Ops, Stacey Weismiller, Founder of the American Manufacturing Futures Institute, joins Natan Linder to reframe the conversation.

Stacey draws on her background spanning manufacturing, economic development, and global policy to explore why people, access, and community must sit at the center of industrial renewal. Together, they discuss workforce participation, civic manufacturing, equitable growth, and how AI can augment human work without eroding trust or dignity.

The conversation spans everything from factory jobs and childcare to resilience, reindustrialization, and why manufacturing needs a new narrative — one that values stewardship as much as efficiency.

Watch the full episode on YouTube.

The Real Problem AI Needs to Solve in Manufacturing

Tulip — Thu, 15 Jan 2026 09:00:00 -0500

Read about Tulip’s $120M Series D 👉 http://tulip.co/press/tulip-secures-120m-series-d/

In this conversation, Chris Luecke, host of the Manufacturing Happy Hour podcast, joins Natan Linder to discuss AI in manufacturing, and how Mitsubishi Electric’s lead investment in Tulip’s $120M Series D helps to accelerate our mission to scale our composable platform, support an open ecosystem for frontline operations, and supports AI-enabled and human-driven innovation.
Key themes from this conversation include:
• Why "software-defined manufacturing" is essential for modern supply chains.
• The rise of the AI process engineer, and real-world implications of AI adoption among frontline process engineers.
• The importance of building a transparent, human-first culture in frontline operations.

AI at the Crossroads of Regulation and Innovation

Tulip — Thu, 08 Jan 2026 12:00:00 -0500

AI is rapidly reshaping life sciences manufacturing—but as intelligent systems move into regulated environments, questions around validation, governance, and trust become unavoidable.

In this episode of Augmented Ops, host Michelle Vuolo, Head of Quality at Tulip, is joined by Bryan Ennis, Chief Quality Officer and Founder of Sware, and Martin Heitmann, of the Triality Group. Together, they explore what it really takes to deploy AI responsibly in pharma, biotech, and medtech operations.

The conversation examines why many AI initiatives stall at the pilot stage, how validation practices must evolve for probabilistic systems, and where organizations are already seeing real value—from predictive maintenance to quality signal detection and validation automation. They also discuss emerging regulatory guidance, including Annex 22, and why regulators are not anti-AI—but deeply skeptical of black-box systems.

Throughout the discussion, a consistent theme emerges: successful AI adoption is less about the technology itself and more about process design, data quality, human oversight, and building evidence that systems are safe, transparent, and fit for purpose.

This episode offers a grounded, experience-driven perspective on how life sciences organizations can move from experimentation to scale—without compromising patient safety or compliance.

Watch the full episode on YouTube: https://youtu.be/7keK_4zDaTg

Giving Robots "Common Sense": Inside RightHand Robotics with Yaro Tenzer

Tulip — Thu, 18 Dec 2025 00:15:00 -0500

Robotics has promised to transform manufacturing and logistics for decades — but turning intelligent machines into reliable, everyday operators remains hard. In this episode of Augmented Ops, Natan Linder sits down with Yaro Tenzer, co-founder and CEO of RightHand Robotics, to talk about what it actually takes to deploy AI-powered robotics in real operational environments.

Yaro shares lessons from building robotic systems that operate in the messiness of the real world — where data is imperfect, edge cases are constant, and reliability matters more than demos. Together, they discuss why so many robotics pilots struggle to reach production, how machine learning improves through real-world feedback, and what operations leaders should understand before investing in automation.

The conversation explores the intersection of robotics, AI, and operations — focusing on practical constraints, system design, and the human decisions that determine whether advanced technology delivers value or stalls on the shop floor.

Watch the full episode on YouTube: https://youtu.be/a06GA7TvI8Y

The Future Process Engineer with Chris Luecke of Manufacturing Happy Hour

Tulip — Thu, 04 Dec 2025 00:15:00 -0500

What does the future of process engineering look like in an era shaped by AI, automation, and rapid operational change? In this episode, Chris Luecke joins Natan Linder to explore how the role is evolving, what still defines great engineering, and why human judgment remains essential on the modern shop floor.

Chris is the host of Manufacturing Happy Hour and one of the most connected voices in the industry. Before stepping behind the microphone, he spent years as a process engineer at Anheuser-Busch and later worked across sectors with Rockwell Automation—giving him a rare vantage point on how factories actually run and how engineering teams solve problems.

Natan and Chris discuss the shift from reactive troubleshooting to systems thinking, how culture shapes the pace and quality of improvement, and why the most effective way to introduce AI is to aim it at the tasks teams collectively find painful. They also examine the idea of “Shenzhen Speed,” how faster design and production cycles influence global competitiveness, and what manufacturers elsewhere can learn from regions that move quickly.

This conversation offers an on-the-ground view of how engineering work is changing and what the next generation of process engineers will need to thrive.

Watch the full episode on YouTube

AI, Industry, and the Human Story with MIT’s David Mindell

Tulip — Thu, 13 Nov 2025 00:15:00 -0500

AI is often described as a revolution, but every technological leap has deep roots in the human story. In this episode of Augmented Ops, MIT Professor and author David Mindell joins Tulip CEO Natan Linder to discuss how history can help us navigate the rise of intelligent systems.

Mindell, a historian, engineer, and entrepreneur, shares insights from his latest book, The New Lunar Society, which traces the origins of the Industrial Revolution and the people who built it. He draws connections between the 18th-century innovators who powered the first era of mechanization and today’s engineers shaping AI. Every tool, he argues, embeds human skill, judgment, and culture; from the earliest steam engines to modern autonomous systems.

Their conversation examines the enduring questions that define manufacturing and technology: How can new tools expand opportunity instead of narrowing it? What does responsible innovation look like in an age of automation? And how can societies balance ambition, governance, and trust while embracing change?

Through stories of invention, work, and rediscovery, Mindell reminds us that progress has always been a human endeavor. Technology evolves, but the drive to create, understand, and improve remains constant.

Watch the full episode on YouTube

AI for Operations: From Everyday Tools to Agentic Systems

Tulip — Thu, 30 Oct 2025 00:15:00 -0400

Rather than imagining a future without people, the episode points to a more realistic vision of AI in manufacturing: one where systems evolve, but human judgment remains the foundation of progress.

Operations Calling 2025 Recap: From AI Hype Into Real World Results

Tulip — Thu, 16 Oct 2025 00:15:00 -0400

Kicking off Season 6, Natan and Tulip CMO Madilynn Castillo reflect on Operations Calling 2025—recorded just after nearly 800 manufacturing leaders, engineers, and frontline pros converged at Tulip HQ for Tulip’s biggest event to date. More than a showcase of technology, this two-day experience blended strategy, execution, and genuine community. Attendees dove into headline keynotes, fireside chats, interactive workshops, and panels, led by senior voices and industry experts driving the new era of manufacturing.

The episode captures how this convergence marked a real inflection point: AI moving from hype to hands-on tools like Tulip Agents, composable systems scaling across teams, and the shift from digital transformation to continuous transformation on the shop floor. Through live demos, open learning, and collaborative problem-solving, participants saw—and built—the next wave of operations-led innovation.

Packed with post-event momentum, Natan and Madi share stories and lessons that reveal how practical AI, human-centered design, and community are reshaping manufacturing’s future.

Check out all the Operations Calling Sessions!
Natan's Keynote
The Next Shift: AI-Driven Transformation of the Connected Factory
Tulip Roadmap Session Special Guest: Madilynn Castillo.

Reindustrializing in 2025 — AI, Scale, and the Future of U.S. Manufacturing with MIT’s Liz Reynolds

Tulip — Thu, 14 Aug 2025 00:15:00 -0400

In this bonus episode, our guest is Liz Reynolds, manufacturing and workforce expert at MIT and strategic advisor to Tulip..

Fresh from Detroit's Reindustrialize conference, Liz and Natan share key insights on America's urgent push to bring manufacturing back home. They explore the "Spring of momentum" in reindustrialization efforts, from AI moving beyond hype to real implementation on the shop floor, and break down the massive scale challenges facing US manufacturers across critical sectors.

Drawing from major industry conferences including Reindustrialize, the Hill and Valley Forum, Industry Studies Association, and MIT's Initiative for New Manufacturing, she explains strategic workforce development approaches to address the 400,000 manufacturing worker shortage and the Department of Defense's $1 trillion budget impact on industrial capacity. Reynolds sheds light on how this Spring's discussions and strategic planning around technology adoption and workforce training are beginning to take concrete shape as the real work accelerates into Fall.

Episode 141: Reindustrializing America with Liz Reynolds

Tulip — Mon, 01 Jul 2024 04:30:00 -0400

In this bonus episode, Elisabeth Reynolds—MIT Professor of the Practice, former White House policymaker, and now Strategic Advisor to Tulip—joins Natan Linder to discuss the building momentum around reindustrialization in the United States.

Liz calls attention to the most important factors shaping the industrial landscape, and the need for a clear national strategy that can direct government coordination with manufacturers. Liz also explores the challenges in introducing software to the frontline workforce, ways manufacturers can address skill gaps, and the role of venture capital in fueling innovation.

Rendustrialize Summit
Reindustrialize Manifesto
SCSP Advanced Manufacturing Report

Augmented Ops is a podcast for industrial leaders, shop floor operators, citizen developers, and anyone else that cares about what the future of frontline operations will look like across industries. This show is presented by Tulip, the Frontline Operations Platform. You can find more from us at Tulip.co/podcast or by following the show on LinkedIn. Special Guest: Elisabeth Reynolds.

Episode 140: A Frontline Perspective on Industry 4.0 – Reflections on Season 1 of Augmented Ops

Tulip — Wed, 26 Jun 2024 07:00:00 -0400

This week marks the final episode of Season 1 of Augmented Ops! Natan Linder and Erik Mirandette sit down to discuss their takeaways from the first season—while trying not to get derailed analogizing frontline operations to the Celtics Championship win.

Natan and Erik highlight the advancements (and stumbles) in industrial AI, and the way that open, interoperable ecosystems have fundamentally changed the way manufacturing tech stacks are built. They also reflect the need to focus on the frontline worker, the power of democratizing advanced technology, and more.

Episode 139: How to Architect Your Digital Strategy with Jeff Kramer

Tulip — Wed, 12 Jun 2024 00:30:00 -0400

This week’s guest is Jeff Kramer, VP Technology & Digital Factory at Kason Industries.

Jeff discusses why manufacturers struggle to develop a cohesive digital strategy, and lays out best practices around governance, data architecture, and bridging the IT/OT divide. He also explains why it’s critical for organizations to empower their frontline personnel by using technology to enable a citizen developer approach.

Episode 138: Democratizing Computer Vision with LandingAI’s Kai Yang

Tulip — Wed, 29 May 2024 00:15:00 -0400

This week’s guest is Kai Yang, VP of Product at LandingAI.

Kai discusses the need for a data-centric approach to AI, why vendors should build tools rather than solutions, and more, sharing lessons learned from his career in machine learning and software development. He also explains how new tools like visual prompting are democratizing computer vision and enabling anyone, regardless of skill level, to develop their own machine learning models.

Episode 137: AI for the Frontline Engineer with Instrumental’s Anna Shedletsky

Tulip — Wed, 15 May 2024 00:30:00 -0400

This week’s guest is Anna Shedletsky, Co-Founder and CEO of Instrumental.

Having started her career as an engineer at Apple, Anna shares lessons around quality management, which manufacturing KPIs actually matter, and how to take an idea from prototype to production. Plus, she lays out why organizations should think about manufacturing as a profit generator rather than a cost center, and why being able to demonstrate ROI is vital for engineers to advocate for the tech they need.

Episode 136: AI Takes Center Stage at Hannover Messe

Tulip — Wed, 01 May 2024 07:30:00 -0400

This week, Natan Linder, Co-Founder and CEO of Tulip sits down with Madilynn Castillo, Head of Marketing for a recap of their experiences at this year’s Hannover Messe — the world’s largest industrial trade fair.

They explore the lackluster state of AI in industrial software, the rise of composable software architectures, and how open technology ecosystems are becoming the norm throughout the industry. Plus, an overview of the latest developments from Tulip that debuted at the show.

Episode 135: Bringing Spatial Intelligence to Operations with Zerokey's Matt Lowe

Tulip — Wed, 10 Apr 2024 00:15:00 -0400

This week’s guest is Matt Lowe, Co-Founder and CEO of ZeroKey.

Not only is he a contributor to major open source projects like Linux and Arduino, Matt is the inventor of Quantum RTLS, a system that uses ultrasound to achieve 3D position tracking of objects with an unmatched level of fidelity. He explains what makes ultrasound-based positioning systems ideal for manufacturing environments, how spatial intelligence offers new ways to solve problems on the shop floor, and how open architecture can eliminate the need for system integrators.

Episode 134: Building Industrial Architectures with MQTT with HiveMQ’s Dominik Obermaier

Tulip — Wed, 27 Mar 2024 00:30:00 -0400

This week’s guest is Dominik Obermaier, Co-Founder and CTO of HiveMQ.

With over 10 years of experience serving on the MQTT technical committee and helping organizations build their data foundations using HiveMQ’s MQTT platform, Dominik shares his deep expertise on the technology. He explains what makes MQTT such an important communications protocol, why the emergence of the Unified Namespace matters for manufacturers, and debates the merits of on-prem vs. cloud solutions.

Episode 133: Rethinking Our Approach to AI with Dr. Jay Lee

Tulip — Wed, 13 Mar 2024 00:15:00 -0400

This week’s guest is Jay Lee, Director of the Industrial AI Center at the University of Maryland.

Dr. Lee shares his experiences from the early days programming machines with punch cards, to eventually developing advanced machine learning applications for industry. He explains how AI and ML are reshaping manufacturing, the workforce, and global supply chains. Plus, he lays out his vision for how our education system needs to change in order to train the next generation of AI practitioners.

Episode 132: Open Source Software for Manufacturing with UMH's Alex Krüger

Tulip — Wed, 28 Feb 2024 00:30:00 -0500

This week’s guest is Alex Krüger, Co-founder and CEO of United Manufacturing Hub, or UMH.

Alex shares his journey from working on integration projects in consulting fresh out of college, to founding UMH and building an open source alternative to the offerings from incumbent vendors. He breaks down the role of the open source software movement in manufacturing, how the Unified Namespace architecture compares to the traditional ISA-95 model, and how IT can best enable OT to solve problems. Plus, he shares his vision for how microservice-based MES solutions can disrupt the existing monolithic applications.

Episode 131: MQTT, Unified Namespace, and The New Industrial Data Stack with Litmus’s Vatsal Shah

Tulip — Wed, 14 Feb 2024 00:30:00 -0500

This week’s guest is Vatsal Shah, Founder and CEO of Litmus.

Vatsal discusses his journey from an automation engineer at Rockwell, to building a new industrial data platform from the ground up after becoming frustrated with the limitations of the offerings from established vendors. He discusses manufacturers’ exodus from on-prem to cloud systems, the pros and cons of data protocols like MQTT and Sparkplug B, and why the Unified Namespace architecture is getting so much attention. Plus, he shares his vision for the future of edge computing and how an open ecosystem of interoperable tools is transforming the industry.

Episode 130: Democratization, Gen AI, and the Future of Industrial Analytics with Seeq’s Lisa Graham

Tulip — Wed, 31 Jan 2024 00:30:00 -0500

This week’s guest is Dr. Lisa Graham, CEO of Seeq.

Dr. Graham discusses her journey from process engineer, to using Seeq’s platform as a customer, and now leading the company as CEO. Drawing on her extensive experience in operations, she discusses how advanced analytics, generative AI, and the emergence of an interoperable technology ecosystem are reshaping industries. Plus, she shares best practices for IT/OT collaboration, her vision for the future of historians, and how the democratization of data science is paving the way for a more efficient and sustainable future in operations and manufacturing.

Episode 129: AI and the Human Element in Industry 4.0 with Jeff Winter

Tulip — Wed, 17 Jan 2024 00:30:00 -0500

This week’s guest is Jeff Winter, Sr. Director of Industry Strategy for Manufacturing at Hitachi Solutions.

Jeff offers his insights into the history of the Industry 4.0 movement and how he expects it to evolve in the coming years. His discussion highlights the balance between AI and human ingenuity, the role of frontline workers in an increasingly automated manufacturing environment, and the untapped potential of manufacturing data.

Episode 128: From Tailor to Technologist: A Digital Transformation Journey with Joachim Hensch

Tulip — Wed, 03 Jan 2024 00:15:00 -0500

This week’s guest is Joachim Hensch, Founder of Joachim Hensch Consulting and former Managing Director of the Hugo Boss factory in Izmir, Turkey.

Hensch shares invaluable lessons learned about digital transformation through his over three decades of experience working in the apparel industry in roles from the shop floor all the way to management. His unique journey from tailor to digital transformation leader illustrates the realities of implementing Industry 4.0, challenges in traditional manufacturing, and the pressing need to empower workers with digital tools. Joachim discusses how manufacturers can balance artisanship with mass production by adopting new tools while retaining a deep appreciation of the frontline operators and their critical role in the industry.

Episode 127: Venture Capital's Role in Digital Transformation with Lior Susan

Tulip — Wed, 06 Dec 2023 00:15:00 -0500

This week’s guest is Lior Susan, founder of Eclipse Ventures.

With the digital transformation of critical industries like manufacturing now at the forefront of many nations’ economic priorities, Lior discusses the role that venture capital can play in helping drive this change. He addresses the growing importance of integrating IT and OT in industrial settings, and how technology can be used to augment the global workforce. Plus, key insights on the future of system integration in a world of open, interoperable software ecosystems.

Episode 126: Transforming Manufacturers’ Organizational Strategy with Dr. Jörg Gnamm

Tulip — Wed, 15 Nov 2023 00:15:00 -0500

This week’s guest is Dr. Jörg Gnamm, Senior Partner & Global Head of Manufacturing and Industry 4.0 Practice at Bain & Company.

In order to successfully transform their business, Jörg calls on manufacturers to take a systemic approach to technology adoption by enabling interdisciplinary collaboration, and focusing on use cases that drive value for the business. He draws on his extensive experience with real-world implementation examples, sharing his lessons learned and best practices from successfully implementing the blueprint he describes in our conversation.

Episode 125: Rethinking Quality Control for Pharmaceuticals with Mark Buswell

Tulip — Wed, 01 Nov 2023 00:15:00 -0400

Our guest this week is GSK’s Mark Buswell, VP of Quality Tech. Mark draws on over two decades of experience in pharma manufacturing as we explore the challenges of quality control in the industry.

Our discussion sheds light on the hurdles of adopting emerging technologies in regulated industries as Mark presents his vision of how to enable 'Quality by Design' with new tech and methodologies. He explains what the future of quality labs will look like, and what manufacturers need to do to prepare for this coming paradigm shift in the pharma industry.

Episode 124: Industrial Data Interoperability with Erich Barnstedt

Tulip — Wed, 18 Oct 2023 00:15:00 -0400

Our guest this week is Microsoft’s Erich Barnstedt, Chief Architect Standards, Consortia & Industrial IoT, Azure Edge + Platform.

Erich brings his perspective as we try to get to the bottom of why–despite overtures from some of the biggest vendors in the space–we still have not achieved true data interoperability in the manufacturing industry. We explore what really goes on behind the curtain at standards committees, and why it is so important for vendors to embrace an open technology ecosystem that puts interoperability at the forefront.

Episode 123: Building a Manufacturing Software Marketplace with Diego Tamburini

Tulip — Wed, 04 Oct 2023 00:30:00 -0400

Our guest this week is Diego Tamburini, Category Manager for Manufacturing for the Microsoft Commercial Marketplace.

We explore what the modern manufacturing software landscape looks like from the consumer and vendor perspective, and take a deep dive into what software providers can do to enable an open, interoperable tech stack for manufacturers. We also highlight the importance of collecting data and putting the operator first as manufacturers look to digitally transform their businesses.

Episode 122: Fixing the Failures of Industry 4.0 with Antonio Padovano

Tulip — Wed, 20 Sep 2023 00:30:00 -0400

Our guest this week is Antonio Padovano, Assistant Professor at the University of Calabria.

In this conversation, we discuss the failures of practically implementing Industry 4.0 on the shop floor, and his vision for how we can address these with a new approach that respects both humans and technology.

Episode 121: Looking Back and Looking Ahead

Tulip — Wed, 23 Aug 2023 00:15:00 -0400

In this special episode, Trond introduces Natan Linder, CEO of Tulip and co-author of Augmented Lean, as the new host of Augmented Season 4. Trond and Natan review four great interviews from 2022, and Natan previews what’s to come in 2023–with new episodes that go beyond interviews to include brainstorms, debates, and the occasional stream of consciousness.

Episode 120: Digital Manufacturing in Turkey and Beyond with Efe Erdem

Tulip — Wed, 09 Aug 2023 12:15:00 -0400

Efe Erdem, Executive Director of the MEXT Technology Center takes us on a journey through Turkey's manufacturing landscape and its pivotal role in advancing digitalization across the MENA region. We delve into the motivation behind establishing the MEXT Technology Center, its unique approach in providing end-to-end services to manufacturers, and the impact of their initiatives on digital transformation in various sectors, including automotive, steel, and textiles.

Efe shares valuable insights on the importance of upskilling the workforce to drive innovation on the shop floor, and how technology can augment human capabilities leading to increased efficiency and productivity. As the region embraces sustainability, we discuss how digitalization becomes a critical enabler for achieving decarbonization goals and fostering growth in an increasingly competitive global market.

If you like this show, subscribe at AugmentedPodcast.co. If you found this episode interesting, you might also like Episode 104: A Scandinavian Perspective on Industrial Operator Independence with Johan Stahre, or Episode 40: Israel Meets New England on Industry 4.0. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.
Special Guest: Efe Erdem.

Episode 110: Executing on Manufacturing Technology with Jane Arnold

Tulip — Wed, 22 Mar 2023 00:15:00 -0400

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is "Executing on Manufacturing Technology" and our guest is Jane Arnold, board member at Aperio.ai and former VP of Manufacturing Technology at Stanley Black & Decker. In this conversation, we talk about advanced manufacturing technology, the importance of material flow, transparency, throughput, cost cutting, and captivating users with digital tools.

If you like this show, subscribe at augmentedpodcast.co. If you liked this episode, you might also like Episode 100: Innovating Across the Manufacturing Supply Chain. Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Execution is everything in manufacturing. You can have any technology you want, but it's only going to be as good as the execution, both among executives and among managers all along the supply chain and all across the factory. Special Guest: Jane Arnold.

Episode 103: Human-First AI with Christopher Nguyen

Tulip — Wed, 23 Nov 2022 00:00:00 -0500

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers.

In this episode of the podcast, the topic is Human-First AI. Our guest is Christopher Nguyen, CEO, and Co-Founder of Aitomatic. In this conversation, we talk about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame in terms of finding enough high-quality data to label the data correctly. The fix is to use AI to augment existing workflows. We talk about fishermen at Furuno, human operators in battery factories at Panasonic, and energy optimization at Westinghouse.

If you like this show, subscribe at augmentedpodcast.co. If you like this episode, you might also like Episode 80: The Augmenting Power of Operational Data, with Tulip's CTO, Rony Kubat.

Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim and presented by Tulip.

Follow the podcast on Twitter or LinkedIn.

Trond's Takeaway:

Physical AI is much more interesting of a challenge than pure digital AI. Imagine making true improvements to the way workers accomplish their work, helping them be better, faster, and more accurate. This is the way technology is supposed to work, augmenting humans, not replacing them. In manufacturing, we need all the human workers we can find. As for what happens after the year 2100, I agree that we may have to model what that looks like. But AIs might be even more deeply embedded in the process, that's for sure.

Transcript:

TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations in industrial tech. Our vision is a world where technology will restore the agility of frontline workers.

Augmented is a podcast for industrial leaders, process engineers, and for shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip.

Christopher, how are you? And welcome.

CHRISTOPHER: Hi, Trond. How are you?

TROND: I'm doing great. I thought we would jump into a pretty important subject here on human-first AI, which seems like a juxtaposition of two contradictory terms, but it might be one of the most important types of conversations that we are having these days.

I wanted to introduce you quickly before we jump into this. So here's what I've understood, and you correct me if I'm wrong, but you are originally from Vietnam. This is back in the late '70s that you then arrived in the U.S. and have spent many years in Silicon Valley mostly. Berkeley, undergrad engineering, computer science, and then Stanford Ph.D. in electrical engineering. You're a sort of a combination, I guess, of a hacker, professor, builder. Fairly typical up until this point of a very successful, accomplished sort of Silicon Valley immigrant entrepreneur, I would say, and technologist.

And then I guess Google Apps is something to point out. You were one of the first engineering directors and were part of Gmail, and Calendar, and a bunch of different apps there. But now you are the CEO and co-founder of Aitomatic. What we are here to talk about is, I guess, what you have learned even in just the last five years, which I'm thrilled to hear about. But let me ask you this first, what is the most formational and formative experience that you've had in these years? So obviously, immigrant background and then a lot of years in Silicon Valley, what does that give us?

CHRISTOPHER: I guess I can draw from a lot of events. I've always had mentors. I can point out phases of my life and one particular name that was my mentor. But I guess in my formative years, I was kind of unlucky to be a refugee but then lucky to then end up in Silicon Valley at the very beginning of the PC revolution. And my first PC was a TI-99/4A that basically the whole household could afford. And I picked it up, and I have not stopped hacking ever since. So I've been at this for a very long time.

TROND: So you've been at this, which is good because actually, good hacking turns out takes a while. But there's more than that, right? So the story of the last five years that's interesting to me because a lot of people learn or at least think they learn most things early. And you're saying you have learned some really fundamental things in the last five years. And this has to do with Silicon Valley and its potential blindness to certain things. Can you line that up for us? What is it that Silicon Valley does really well, and what is it that you have discovered that might be an opportunity to improve upon?

CHRISTOPHER: Well, I learn new things every four or five years. I actually like to say that every four or five years, I look back, and I say, "I was so stupid five years ago." [laughs] So that's been the case.

TROND: That's a very humbling but perhaps a very smart knowledge acquisition strategy, right?

CHRISTOPHER: Yeah. And in the most recent five years...so before co-founding Aitomatic, which is my latest project and really with the same team...and I can talk a lot more about that. We've worked with each other for about ten years now. But in the intervening time, there's a four-and-a-half-year block when we were part of Panasonic. So we had a company called Arimo that was acquired by Panasonic for our machine learning AI skills and software.

And I would say if you look at my entire history, even though I did start with my degree in semiconductor all the way down to device physics and Intel and so on, but in terms of a professional working career, that was the first time we actually faced the physical world as a Silicon Valley team. And anybody who's observed Silicon Valley in the last 15-20 years, certainly ten years, has seen a marked change in terms of the shift from hardware to software. And my friend Marc Andreessen likes to say, "Software is eating the world."

If you look at education, you know, the degrees people are getting, it has shifted entirely from engineering all the way to computer science. And the punch line, I guess, the observation is that we Silicon Valley people do not get physical. We don't understand the manufacturing world. We don't know how to do HVAC and so on. And so when we build software, we tend to go for the digital stuff.

TROND: Christopher, it's almost surprising given the initial thrust of Silicon Valley was, of course, hardware. So it's not surprising to me, I guess because I've been observing it as well. But it is striking more than surprising that a region goes through paradigms.

CHRISTOPHER: Yeah. Yeah. And it's a global trend. It's the offshoring of low-end, shall we say, low-value manufacturing and so on. And we're discovering that we actually went a little too far. So we don't have the skill set, the expertise anymore. And it's become a geopolitical risk.

TROND: Right. Well, a little bit too far, maybe, or not far enough. Or, I mean, tell us what it is that you're losing when you lose the hardware perspective, particularly in this day and age with the opportunities that we're about to talk about.

CHRISTOPHER: Well, I can talk specifically about the things that touch my immediate spheres. Maybe you can think abstractly about the lack of tooling expertise and manufacturing know-how, and so on. But as part of Panasonic, the acquisition was all about taking a Silicon Valley team and injecting AI, machine learning across the enterprise. And so we were part of that global AI team reporting to the CTO office.

And we found out very quickly that a lot of the software techniques, the machine learning, for example, when you think about people saying data is the fuel for machine learning and specifically labeled data, right? In the digital world, the Google place that I came from, it was very easy to launch a digital experiment and collect labels, decisions made by users. You can launch that in the morning, and by evening you're building examples. You can't do that in the physical world. Atoms move a lot more slowly. And so when you try to do something like predictive maintenance, you don't have enough failure examples to train machine learning models from.

So all of the techniques, all of the algorithms that we say we developed from machine learning that seem to work so well, it turns out it worked so well because the problem space that we worked on has been entirely digital, and they all fail when it comes to manufacturing, the things that you can touch and feel, you know, cars that move and so on.

TROND: I want to ask you this, Christopher, because the first company you helped co-found was, in fact, a contract manufacturer. Do you think that reflecting on this long career of yours and these various experiences, what was it that convinced you before others? I mean, you're not the only one now in the Valley that has started to focus on manufacturing and including hardware again, but it is rare still. What does it require to not just think about manufacturing but actually start to do compute for manufacturing? Is it just a matter of coming up with techniques? Or is it a whole kind of awareness that takes longer? So, in your case, you've been aware of manufacturing, acutely aware of it for decades.

CHRISTOPHER: I would say there are two things, one is obvious, and the other was actually surprising to me. The obvious one is, of course, knowledge and experience. When we work on sonar technology that shoots a beam down an echogram that comes back to detect fish in the ocean, it's very necessary, not just convenient, but necessary for the engineers that work on that to understand the physics of sound waves travel underwater, and so on.

So that education, I have long debates, and it's not just recently. When we were trying to structure a syllabus for a new university, I had long debates with my machine-learning friends, and they said, "We don't need physics." And I said, "We need physics." That's one thing. But you can concretely identify you need to know this. You need to know this. So if you're going to do this, learn the following thing.

The thing that was more unexpected for me in the last five years as I sort of sound this bell of saying, hey, we need to modify our approach; we need to optimize our algorithms for this world, is a cultural barrier. It's kind of like the story of if you have a hammer, you want to go look for nails. So Silicon Valley today does not want to look for screwdrivers yet for this world.

TROND: So you're saying Silicon Valley has kind of canceled the physical world? If you want to be really sort of parabolic about this, it's like software is eating the world, meaning software is what counts, and it's so efficient. Why go outside this paradigm, basically? If there's a problem that apparently can't be fixed by software, it's not a valuable problem.

CHRISTOPHER: Or I can't solve that problem with my current approach. I just have to squint at it the right way. I have to tweak the problem this way and so on despite the fact that it's sort of an insurmountable challenge if you tried to do so. And concretely, it is like, just give me enough data, and I'll solve it. And if you don't have enough data, you know what? Go back and get more data. [chuckles] That's what I myself literally said. But people don't have the luxury of going back to get more data. They have to go to market in six months, and so on.

TROND: Right. And so manufacturing...and I can think of many use cases where obviously failure, for example, is not something...you don't really want to go looking for more failure than you have or artificially create failure in order to stress test something unless that's a very safe way of doing so. So predictive maintenance then seems like a, I guess, a little bit of a safer space. But what is it about that particular problem that then lends itself to this other approach to automating labeling? Or what exactly is it that you are advocating one should do to bridge to digital and the physical AIs?

CHRISTOPHER: I actually disagree that it is a safer space.

TROND: Oh, it's not a safer space to you.

CHRISTOPHER: That itself there's a story in that, so let's break that down.

TROND: Let's do it.

CHRISTOPHER: So, again, when I say Silicon Valley, it is a symbol for a larger ecosystem that is primarily software and digital. And when I say we, because I've worn many hats, I have multiple wes, including academia; I've been a professor as well. When we approach the predictive maintenance problem, if you approach it as machine learning, you got to say, "Do this with machine learning," the first thing you ask for...let's say I'm a data scientist; I'm an AI engineer.

You have this physical problem. It doesn't matter what it is; just give me the dataset. And the data set must have rows and columns, and the rows are all the input variables. And then there should be some kind of column label. And in this case, it'll be a history of failures of compressors failing, you know, if the variables are such, then it must be a compressor. If the variables are such, it must be the air filter, and so on.

And it turns out when you ask for that kind of data, you get ten rows. [laughs] That's not enough to do machine learning on. So then people, you know, machine learning folks who say they've done predictive maintenance, they actually have not done predictive maintenance. That's the twist. What they have done is anomaly detection, which machine learning can do because, with anomaly detection, I do not need that failure label. It just gives me all the sensor data.

What anomaly detection really does is it learns the normal patterns. If you give it a year's worth of data, it'll say, okay, now I've seen a year's worth of data. If something comes along that is different from the past patterns; I will tell you that it's different. That's only halfway to predictive maintenance. That is detecting that something is different today. That is very different from, and it isn't predicting, hey, that compressor is likely to fail about a month from now.

And that when we were part of Panasonic, it turns out the first way...and we solved it exactly the way I've described. We did it with the anomaly detection. And then we threw it over the wall to the engineer experts and said, "Well, now that you have this alert, go figure out what may be wrong." And half of the time, they came back and said, "Oh, come on, it was just a maintenance event. Why are you bothering me with this?"

TROND: But, Christopher, leveraging human domain expertise sounds like a great idea. But it can't possibly be as scalable as just leveraging software. So how do you work with that? And what are the gains that you're making?

CHRISTOPHER: I can show you the messenger exchange I had with another machine-learning friend of mine who said exactly the same thing yesterday, less than 24 hours ago.

TROND: [laughs]

CHRISTOPHER: He said, "That's too labor-intensive." And I can show you the screen.

TROND: And how do you disprove this?

CHRISTOPHER: Well, [chuckles] it's not so much disproving, but the assumption that involving humans is labor-intensive is only true if you can't automate it. So the key is to figure out a way, and 10-20 years ago, there was limited technology to automate or extract human knowledge, expert systems, and so on. But today, technologies...the understanding of natural language and so on, machine learning itself has enabled that. That turns out to be the easier problem to solve. So you take that new tool, and you apply it to this harder physical problem.

TROND: So let's go to a hard, physical problem. You and I talked about this earlier, and let's share it with people. So I was out fishing in Norway this summer. And I, unfortunately, didn't get very much fish, which obviously was disappointing on many levels. And I was a little surprised, I guess, of the lack of fish, perhaps. But I was using sonar to at least identify different areas where people had claimed that there were various types of fish. But I wasn't, I guess, using it in a very advanced way, and we weren't trained there in the boat.

So we sort of had some sensors, but we were not approaching it the right way. So that helped me...and I know you work with Furuno, and Garmin is the other obviously player in this. So fish identification and detection through sonar technology is now the game, I guess, in fishery and, as it turns out, even for individuals trying to fish these days. What is that all about? And how can that be automated, and what are the processes that you've been able to put in place there?

CHRISTOPHER: By the way, that's a perfect segue into it. I can give a plug perhaps for this conference that I'm on the organizing committee called Knowledge-First World. And Furuno is going to be presenting their work exactly, talking a lot about what you're talking about. This is kind of coming up in November. It is the first conference of its kind because this is AI Silicon Valley meets the physical world.

I think you're talking about the fish-finding technology from companies like Furuno, and they're the world's largest market share in marine navigation and so on. And the human experts in this are actually not even the engineers that build these instruments; it's the fishermen, right? The fishermen who have been using this for a very long time combine it with their local knowledge, you know, warm water, cold water, time of day, and so on. And then, after a while, they recognize patterns that come back in this echogram that match mackerel, or tuna, or sardines, and so on.

And Furuno wants to capture that knowledge somehow and then put that model into the fish-finding machine that you and I would hold. And then, instead of seeing this jumbled mess of the echogram data, we would actually see a video of fish, for example. It's been transformed by this algorithm.

TROND: So, I mean, I do wish that we lived in a world where there was so much fish that we didn't have to do this. But I'm going to join your experiment here. And so what you're telling me is by working with these experts who are indeed fishermen, they're not experts in sonar, or they're not experts in any kind of engineering technology, those are obviously the labelers, but they are themselves giving the first solutions for how they are thinking about the ocean using these technologies. And then somehow, you are turning that into an automatable, an augmented solution, essentially, that then can find fish in the future without those fishermen somehow being involved the next time around because you're building a model around it.

CHRISTOPHER: I'll give you a concrete explanation, a simplified version of how it works, without talking about the more advanced techniques that are proprietary to Furuno. The conceptual approach is very, very easy to understand, and I'll talk about it from the machine learning perspective.

Let's say if I did have a million echograms, and each echogram, each of these things, even 100,000, is well-labeled. Somebody has painstakingly gone through the task of saying, okay, I'm going to circle this, and that is fish. And that is algae, and that's sand, and that's marble. And by the way, this is a fish, and this is mackerel, and so on. If somebody has gone through the trouble of doing that, then I can, from a human point of view, just run an algorithm and train it. And then it'll work for that particular region, for that particular time. Okay, well, we need to go collect more data, one for Japan, the North Coast, and one for Southwestern.

So that's kind of a lot of work to collect essentially what this pixel data is, this raw data. When you present it to an experienced fisherman, he or she would say, "Well, you see these bubbles here, these circles here with a squiggly line..." So they're describing it in terms of human concepts. And then, if you sit with them for a day or two, you begin to pick up these things. You don't need 100,000-pixel images. You need these conceptual descriptions.

TROND: So you're using the most advanced AI there is, which is the human being, and you're using them working with these sonar-type technologies. And you're able to extract very, very advanced models from it.

CHRISTOPHER: The key technology punch line here is if you have a model that understands the word circle and squiggly line, which we didn't before, but more recently, we begin to have models, you know, there are these advances called large language models. You may have heard of GPT-3 and DALL-E and so on, you know, some amazing demonstrations coming out of OpenAI and Google. In a very simplified way, we have models that understand the world now. They don't need raw pixels. These base models are trained from raw pixels, but then these larger models understand concepts. So then, we can give directions at this conceptual level so that they can train other models. That's sort of the magic trick.

TROND: So it's a magic trick, but it is still a difficult world, the world of manufacturing, because it is physical. Give me some other examples. So you worked with Panasonic. You're working with Furuno in marine navigation there and fishermen's knowledge. How does this work in other fields like robotics, or with car manufacturing, or indeed with Panasonic with kind of, I don't know, battery production or anything that they do with electronics?

CHRISTOPHER: So, to give you an example, you mentioned a few things that we worked on, you know, robotics in manufacturing, robotics arm, sort of the manufacturing side, and the consistency of battery sheets coming off the Panasonic manufacturing line in Sparks, Nevada as well as energy optimization at Westinghouse. They supply into data centers, and buildings, and so on.

And so again, in every one of these examples, you've got human expertise. And, of course, this is much more prevalent in Asia because Asia is still building things, but some of that is coming back to the U.S. There are usually a few experts. And by the way, this is not about thousands of manufacturing line personnel. This is about three or four experts that are available in the entire company. And they would be able to give heuristics. –They will be able to describe at the conceptual level how they make their decisions.

And if you have the technology to capture that in a very efficient way, again, coming back to the idea that if you make them do the work or if you automate their work, but in a very painstaking way like thousands of different rules, that's not a good proposition. But if you have some way to automate the automation, automate the capturing of that knowledge, you've got something that can bridge this physical, digital divide.

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TROND: How stable is that kind of model knowledge? Because I'm just thinking about it in the long run here, are these physical domain experts that are giving up a little bit of their superpower are they still needed then in a future scenario when you do have such a model? Or will it never be as advanced as they are? Or is it actually going to be still kind of an interface that's going to jump between machines and human knowledge kind of in a continuous loop here?

CHRISTOPHER: Yeah, in the near term, it turns out we're not working on replacing experts as much as scaling experts. Almost every case we've worked on, companies are in trouble largely because the experts are very, very few and far between, and they're retiring. They're leaving. And that needs to be scaled somehow. In the case of, for example, the cold chain industry all of Japan servicing the supermarkets, you know, there's 7-ELEVEN, there's FamilyMart, and so on, there are three experts who can read the sensor data and infer what's likely to fail in the next month. So in the near term, it's really we need these humans, and we need more of them.

TROND: I'm glad to hear that even that is a bit of a contrarian message. So you're saying physical infrastructure and the physical world matters. You're saying humans matter. [laughs] It's interesting. Yeah, that's contrarian in Silicon Valley, I'll tell you that.

CHRISTOPHER: It is. And, in fact, related to that problem, Hussmann, which is a refrigeration company, commercial refrigeration supplies to supermarkets. It was a subsidiary of Panasonic. It has a really hard time getting enough service personnel, and they have to set up their own universities, if you will, to train them. And these are jobs that pay very well. But everybody wants to be in software these days.

Coming back to the human element, I think that long-term I'm an optimist, not a blind optimist but a rational one. I think we're still going to need humans to direct machines. The machine learning stuff is data that reflects the past, so patterns of the past, and you try to project that in the future. But we're always trying to effect some change to the status quo. Tomorrow should be a better day than today. So is that human intent that is still, at least at present, lacking in machines? And so we need humans to direct that.

TROND: So what is the tomorrow of manufacturing then? How fast are we going to get there? Because you're saying, well, Silicon Valley has a bit of a learning journey. But there is language model technology or progress in language models that now can be implemented in software and, through humans, can be useful in manufacturing already today. And they're scattered examples, and you're putting on an event to show this. What is the path forward here, and how long is this process? And will it be an exponential kind of situation here where you can truly integrate amazing levels of human insight into these machine models? Or will it take a while of tinkering before you're going to make any breakthroughs?

Because one thing is the breakthrough in understanding human language, but what you're saying here is even if you're working only with a few experts, you have to take domain by domain, I'm assuming, and build these models, like you said, painstakingly with each expert in each domain. And then, yes, you can put that picture together. But the question is, how complex of a picture is it that you need to put together? Is it like mapping the DNA, or is it bigger? Or what kind of a process are we looking at here?

CHRISTOPHER: If we look at it from the dimension of, say, knowledge-based automation, in a sense, it is a continuation. I believe everything is like an s-curve. So there's acceleration, and then there's maturity, and so on. But if you look back in the past, which is sort of instructive for the future, we've always had human knowledge-based automation.

I remember the first SMT, the Surface Mount Technology, SMT wave soldering machine back in the early '90s. That was a company that I helped co-found. It was about programming the positioning of these chips that would just come down onto the solder wave. And that was human knowledge for saying, move it up half a millimeter here and half a millimeter there. But of course, the instructions there are very micro and very specific.

What machine learning is doing...I don't mean to sort of bash machine learning too much. I'm just saying culturally, there's this new tool really that has come along, and we just need to apply the tool the right way. Machine learning itself is contributing to what I described earlier, that is, now, finally, machines can understand us at the conceptual level that they don't have to be so, so dumb as to say, move a millimeter here, and if you give them the wrong instruction, they'll do exactly that. But we can communicate with them in terms of circles and lines, and so on.

So the way I see it is that it's still a continuous line. But what we are able to automate, what we're able to ask our machines to do, is accelerating in terms of their understanding of these instructions. So if you can imagine what would happen when this becomes, let's say, ubiquitous, the ability to do this, and I see this happening over the next...Certainly, the base technology is already there, and the application always takes about a decade.

TROND: Well, the application takes a decade. But you told me earlier that humans should at least have this key role in this knowledge-first application approach until 2100, you said, just to throw out a number out there. That's, to some people, really far away. But the question is, what are you saying comes after that? I know you throw that number out.

But if you are going to make a distinction between a laborious process of painful progress that does progress, you know, in each individual context that you have applied to human and labeled it, and understood a little case, what are we looking at, whether it is 2100, 2075, or 2025? What will happen at that moment? And is it really a moment that you're talking about when machines suddenly will grasp something very, very generic, sort of the good old moment of singularity, or are you talking about something different?

CHRISTOPHER: Yeah, I certainly don't think it's a moment. And, again, the HP-11C has always calculated Pi far faster and with more digits than I have. So in that sense, in that particular narrow sense, it's always been more intelligent than I am.

TROND: Yeah. Well, no one was questioning whether a calculator could do better calculations than a human. For a long time --

CHRISTOPHER: Hang on. There's something more profound to think about because we keep saying, well, the minute we do something, it's okay; that's not intelligence. But what I'm getting to is the word that I would refer to is hyper-evolution. So there's not a replacement of humans by machines. There's always been augmentation, and intelligence is not going to be different. It is a little disturbing to think about for some of us, for a lot of us, but it's not any different from wearing my glasses.

Or I was taking a walk earlier this morning listening to your podcast, and I was thinking how a pair of shoes as an augmented device would seem very, very strange to humans living, say, 500 years ago, the pair of shoes that I was walking with. So I think in terms of augmenting human intelligence, there are companies that are working on plugging in to the degree that that seems natural or disturbing. It is inevitable.

TROND: Well, I mean, if you just think about the internet, which nowadays, it has become a trope to think about the internet. I mean, not enough people think about the internet as a revolutionary technology which it, of course, is and has been, but it is changing. But whether you're thinking about shoes, or the steam engine, or nuclear power, or whatever it is, the moment it's introduced, and people think they understand it, which most people don't, and few of us do, it seems trivial because it's there.

CHRISTOPHER: That's right.

TROND: But your point is until it's there, it's not trivial at all. And so the process that you've been describing might sound trivial, or it might sound complex, but the moment it's solved or is apparently solved to people, we all assume that was easy. So there's something unfair about how knowledge progresses, I guess.

CHRISTOPHER: That's right. That's right. We always think, yeah, this thing that you describe or I describe is very, very strange. And then it happens, and you say, "Of course, that's not that interesting. Tell me about the future."

TROND: Well, I guess the same thing has happened to cell phones. They were kind of a strange thing that some people were using. It was like, okay, well, how useful is it to talk to people without sitting by your desk or in the corner of your house?

CHRISTOPHER: I totally remember when we were saying, "Why the hell would I want to be disturbed every moment of the day?" [laughs] I don't want the phone with me, and now I --

TROND: Right. But then we went through the last decade or so where we were saying, "I can't believe my life before the phone." And then maybe now the last two, three years, I would say a lot of people I talk to or even my kids, they're like, "What's the big deal here? It's just a smartphone," because they live with a smartphone. And they've always had it.

CHRISTOPHER: They say, "How did you get around without Google Maps?" And then somebody says, "We used maps." And I said, "Before Google Maps."

[laughter]

TROND: Yeah. So I guess the future here is an elusive concept. But I just want to challenge you one more time then on manufacturing because manufacturing, for now, is a highly physical exercise. And, of course, there's virtual manufacturing as well, and it builds on a lot of these techniques and machine learning and other things. How do you see manufacturing as an industry evolve? Is it, like you said, for 75 years, it's going to be largely very recognizable? Is it going to look the same? Is it going to feel the same?

Is the management structure the way engineers are approaching it, and the way workers are working? Are we going to recognize all these things? Or is it going to be a little bit like the cell phone, and we're like, well, of course, it's different. But it's not that different, and it's not really a big deal to most people.

CHRISTOPHER: Did you say five years or 50 years?

TROND: Well, I mean, you give me the timeframe.

CHRISTOPHER: Well, in 5 years, we will definitely recognize it, but in 50 years, we will not

TROND: In 50 years, it's going to be completely different, look different, feel different; factories are all going to be different.

CHRISTOPHER: Right, right. I mean, the cliché is that we always overestimate what happens in 5 and underestimate what happens in 50. But the trend, though, is there's this recurring bundling and unbundling of industries; it's a cycle. Some people think it's just, you know, they live ten years, and they say it's a trend, but it actually goes back and forth. But they're sort of increasing specialization of expertise.

So, for example, the supply chain over the last 30 years, we got in trouble because of that because it has become so discrete if you want to use one friendly word, but you can also say fragmented in another word. Like, everybody has been focused on just one specialization, and then something like COVID happens and then oh my God, that was all built very precisely for a particular way of living. And nobody's in the office anymore, and we live at home, and that disrupts the supply chain.

I think if you project 50 years out, we will learn to essentially matrix the whole industry. You talked about the management of these things. The whole supply chain, from branding all the way down to raw materials, is it better to be completely vertically integrated to be part of this whole mesh network? I think the future is going to be far more distributed. But there'll be fits and starts.

TROND: So then my last question is, let's say I buy into that. Okay, let's talk about that for a second; the future is distributed or decentralized, whatever that means. Does that lessen or make globalization even more important and global standardization, I guess, across all geographical territories? I'm just trying to bring us back to where you started with, which was in the U.S., Silicon Valley optimized for software and started thinking that software was eating the world. But then, by outsourcing all of the manufacturing to Asia, it forgot some essential learning, which is that when manufacturing evolves, the next wave looks slightly different. And in order to learn that, you actually need to do it.

So does that lesson tell you anything about how the next wave of matrix or decentralization is going to occur? Is it going to be...so one thought would be that it is physically distributed, but a lot of the insights are still shared. So, in other words, you still need global insight sharing, and all of that is happening. If you don't have that, you're going to have pockets that are...they might be very decentralized and could even be super advanced, but they're not going to be the same. They're going to be different, and they're going to be different paths and trajectories in different parts of the world.

How do you see this? Do you think that our technology paradigms are necessarily converging along the path of some sort of global master technology and manufacturing? Or are we looking at scattered different pictures that are all decentralized, but yet, I don't know, from a bird's eye view, it kind of looks like a matrix?

CHRISTOPHER: I think your question is broader than just manufacturing, although manufacturing is a significant example of that, right?

TROND: It's maybe a key example and certainly under-communicated. And on this podcast, we want to emphasize manufacturing, but you're right, yes.

CHRISTOPHER: The word globalization is very loaded. There's the supposedly positive effect in the long run. But who is it that said...is it Keynes that said, "In the long run, we're all dead?" [laughs] In the short run, the dislocations are very real. A skill set of a single human being can't just shift from hardware to software, from manufacturing to AI, within a few months.

But I think your question is, let's take it seriously on a scale of, say, decades. I think about it in terms of value creation. There will always be some kind of disparity. Nature does not like uniformity. Uniformity is coldness; it is death. There have to be some gradients. You're very good at something; I'm very good at something else. And that happens at the scale of cities and nations as well.

TROND: And that's what triggers trade, too, right?

CHRISTOPHER: Exactly.

TROND: Because if we weren't different, then there would be no incentive to trade.

CHRISTOPHER: So when we think about manufacturing coming back to the U.S., and we can use the word...it is correct in one sense, but it's incorrect in another sense. We're not going back to manufacturing that I did. We're not going back to surface mount technology. In other words, the value creation...if we follow the trajectory of manufacturing alone and try to learn that history, what happens is that manufacturing has gotten better and better. Before, we were outsourcing the cheap stuff. We don't want to do that. But then that cheap stuff, you know, people over there build automation and skills, and so on. And so that becomes actually advanced technology.

So in a sense, what we're really doing is we're saying, hey, let's go advanced at this layer. I think it's going to be that give and take of where value creation takes place, of course, layered with geopolitical issues and so on.

TROND: I guess I'm just throwing in there the wedge that you don't really know beforehand. And it was Keynes, the economist, that said that the only thing that matters is the short term because, in the end, we are all dead eventually. But the point is you don't really know. Ultimately, what China learned from manufacturing pretty pedestrian stuff turned out to be really fundamental in the second wave.

So I'm just wondering, is it possible to preempt that because you say, oh, well, the U.S. is just going to manufacture advanced things, and then you pick a few things, and you start manufacturing them. But if you're missing part of the production process, what if that was the real advancement? I guess that is what happened.

CHRISTOPHER: Okay. So when I say that, I think about the example of my friend who spent, you know, again, we were a Ph.D. group at Stanford together. And whereas I went off to academia and did startups and so on, he stayed at Intel for like 32 years. He's one of the world's foremost experts in semiconductor process optimization. So that's another example where human expertise, even though semiconductor manufacturing is highly automated, you still need these experts to actually optimize these things. He's gone off to TSMC after three decades of being very happy at one place.

So what I'm getting to is it is actually knowable what are the secret recipes, where the choke points are, what matters, and so on. And interestingly, it does reside in the human brain. But when I say manufacturing coming back to the U.S. and advanced manufacturing, we are picking and choosing. We're doing battery manufacturing. We're doing semiconductor, and we're not doing wave soldering.

So I think it is possible to also see this trend that anybody who's done something and going through four or five iterations of that for a long time will become the world's expert at it. I think that is inevitable. You talk of construction, for example; interestingly, this company in Malaysia that is called Renong that is going throughout Southeast Asia; they are the construction company of the region because they've been doing it for so long. I think that is very, very predictable, but it does require the express investment in that direction. And that's something that Asia has done pretty well.

TROND: Well, these are fascinating things. We're not going to solve them all on this podcast. But definitely, becoming an expert in something is important, whether you're an individual, or a company, or a country for sure. What that means keeps changing. So just stay alert, and stay in touch with both AI and humans and manufacturing to boot. It's a mix of those three, I guess. In our conversation, that's the secret to unlocking parts of the future. Thank you, Christopher, for enlightening us on these matters. I appreciate it.

CHRISTOPHER: It's my pleasure.

TROND: You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Human-First AI. Our guest was Christopher Nguyen, CEO, and Co-Founder of Aitomatic. In this conversation, we talked about the why and the how of human-first AI because it seems that digital AI is one thing, but physical AI is a whole other ballgame.

My takeaway is that physical AI is much more interesting of a challenge than pure digital AI. Imagine making true improvements to the way workers accomplish their work, helping them be better, faster, and more accurate. This is the way technology is supposed to work, augmenting humans, not replacing them. In manufacturing, we need all the human workers we can find. As for what happens after the year 2100, I agree that we may have to model what that looks like. But AIs might be even more deeply embedded in the process, that's for sure.

Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 80: The Augmenting Power of Operational Data, with Tulip's CTO, Rony Kubat as our guest. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners.

The augmented podcast is created in association with Tulip, the frontline operation platform that connects the people, machines, devices, and systems used in a production and logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at tulip.co.

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Augmented — industrial conversations that matter. See you next time. Special Guest: Christopher Nguyen.