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From building Applied Intuition from YC-era autonomy tooling into a $15B physical AI company, Qasar Younis and Peter Ludwig have spent the last decade living through the full arc of autonomy: from simulation and data infrastructure for robotaxi companies, to operating systems for safety-critical machines, to deploying AI onto cars, trucks, mining equipment, construction vehicles, agriculture, defense systems, and driverless L4 trucks running in Japan today. They join us to explain why “physical AI” is not just LLMs on wheels, why the real bottleneck is no longer model intelligence but deployment onto constrained hardware, and why the future of autonomy may look less like one-off demos and more like Android for every moving machine.We discuss:* Applied Intuition's mission: building physical AI for a safer, more prosperous world, powering cars, trucks, construction and mining equipment, agriculture, defense, and other moving machines* Why physical AI is different from screen-based AI: learned systems can make mistakes in chat or coding, but safety-critical machines like driverless trucks, autonomous vehicles, and robots need much higher reliability* The evolution from autonomy tooling to a broad physical AI platform: starting with simulation and data infrastructure for robotaxi companies, then expanding into 30+ products across simulation, operating systems, autonomy, and AI models* Why tooling companies came back into fashion: Qasar on why developer tooling looked unfashionable in 2016, why Applied Intuition still bet on it, and how the AI boom made workflows and tools central again* The three core buckets of Applied Intuition's technology: simulation and RL infrastructure, true operating systems for vehicles and machines, and fundamental AI models for autonomy and world understanding* Why vehicles need a real AI operating system: real-time control, sensor streaming, latency, memory management, fail-safes, reliable updates, and why “bricking a car” is much worse than bricking an iPad* Physical machines as “phones before Android and iOS”: Peter explains why today's vehicle and machine software stack is fragmented across many operating systems, and why Applied Intuition wants to consolidate the platform layer* Coding agents inside Applied Intuition: Cursor, Claude Code, internal adoption leaderboards, and how AI tools are changing engineering workflows even in embedded systems and safety-critical software* Verification and validation for physical AI: why evals get harder as models improve, how end-to-end autonomy changes simulation requirements, and why neural simulation has to be fast and cheap enough to make RL practical* From deterministic tests to statistical safety: why autonomy validation is shifting from binary pass/fail requirements toward “how many nines” of reliability and mean time between failures* Cruise, Waymo, and public trust: Qasar and Peter discuss why autonomy failures are not just technical issues, how companies interact with regulators, and why Waymo is setting a high bar for the industry* Simulation vs. reality: why no simulator perfectly represents the real world, how sim-to-real validation works, and why real-world testing will never disappear* World models for physical AI: hydroplaning, construction equipment, visual cues, cause-and-effect learning, and where world models help versus where they are not enough* Onboard vs. offboard AI: why data-center models can be huge and slow, but onboard vehicle models need millisecond-level latency, low power, small size, and distillation-like efficiency* Why physical AI is not constrained by model intelligence alone: the hard part is deploying models onto real hardware, under safety, latency, power, cost, and reliability constraints* Legacy autonomy vs. intelligent autonomy: RTK GPS in mining and agriculture, why hand-coded path-following worked for decades, and why modern systems need perception and dynamic intelligence* Planning for physical systems: how “plan mode” applies to robotaxis, mining, defense, and multi-step physical tasks where actions change the state of the world* Why robotics demos are not production: the brittle last 1%, humanoid reliability, DARPA Grand Challenge-style prize policy, and the advanced engineering gap between research and deployment* Applied Intuition's hard-earned lessons: after nearly a decade, Peter says they can look at a robotics demo and predict the next 20 problems the company will hit* Qasar's advice to founders: constrain the commercial problem, avoid copying mature-company strategies too early, and remember that compounding technology only matters if you survive long enough to see it compound* Why 2014 YC advice may not apply in 2026: capital markets, AI company dynamics, and the difference between building in stealth with a deep network versus building as a new founder today* What Applied is hiring for: operating systems, autonomy, dev tooling, model performance, evals, safety-critical systems, hardware/software boundaries, and engineers with deep curiosity about how things workApplied Intuition:* YouTube: https://www.youtube.com/@AppliedIntuitionInc* X: https://x.com/AppliedInt* LinkedIn: https://www.linkedin.com/company/applied-intuition-incQasar Younis:* X: https://x.com/qasar* LinkedIn: https://www.linkedin.com/in/qasar/Peter Ludwig:* LinkedIn: https://www.linkedin.com/in/peterwludwig/Timestamps00:00:00 Introduction: Applied Intuition, Physical AI, and 10 Years of Building00:01:37 Physical AI vs. Screen AI: Why Safety-Critical Changes Everything00:02:51 The Origin Story: Tooling, YC, and the Scale AI Comparison00:05:41 The Three Buckets: Simulation, Operating Systems, and Autonomy Models00:11:10 Hardware, Sensors, and the LiDAR Question00:14:26 The Operating System Layer: Why Vehicles Are Like Pre-Android Phones00:19:13 Customers, Licensing, and the Better-Together Stack00:21:19 AI Coding Adoption: Cursor, Claude Code, and the Bimodal Engineer00:26:41 Verifiable Rewards, Evals, and Neural Simulation00:31:04 Statistical Validation, Regulators, and the Cruise Lesson00:40:25 World Models, Hydroplaning, and Cause-Effect Learning00:43:34 Onboard vs. Offboard: Latency, Embedded ML, and Distillation00:50:57 Plan Mode for Physical Systems and Next-Token Prediction Universally00:53:04 Productionization: The 20 Problems Every Robotics Demo Will Hit00:58:00 Founder Advice: Constraints, Compounding Tech, and Mature-Company Mimicry01:05:41 Hiring Philosophy: Hardware/Software Boundary and Engineering Mindset01:08:50 General Motors Institute, Education, and the Curiosity MindsetTranscriptIntroduction: Applied Intuition, Physical AI, and 10 Years of BuildingAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: And today we're very honored to have the founders of Applied Intuition, Qasar and Peter. Welcome.Qasar [00:00:17]: You guys really know how to turn it on to podcast mode. That was, you guys are real pros at this.Qasar [00:00:23]: They were just joking around right before this, and then they flipped it pretty quick.Alessio [00:00:29]: Oh, yeah, it's good to have you guys. Maybe you just wanna introduce yourself so people know the voice on the mic and they'll know what they're hearing.Peter [00:00:33]: Oh, sure. Yeah, I'm Peter Ludwig. I'm the co-founder and CTO of Applied Intuition.Qasar [00:00:38]: And my name is Qasar Younis. I am the CEO and co-founder with Peter.Alessio [00:00:42]: Nice. Can you guys give the high-level overview of what Applied Intuition is? And I was reading through some of the Congress files, when you went out there, Peter, and eighteen of the top twenty global non-Chinese automakers, you two guys, you have customers in agriculture, defense, construction. I think most people have heard of Applied Intuition tied to YC when it was first started, and then you were kinda in stealth for a long time, so maybe just give people the high-level overview of what it is today, and then we'll dive into the different pieces.Peter [00:01:10]: Yeah. So at Applied Intuition, our mission is to build physical AI for a safer, more prosperous world. And so we work on physical AI for all different types of moving systems, everything from cars to trucks to construction and mining equipment, to defense technologies. And we're a true technology company, so we build and sell the technology, and we sell it to the companies that make the machines. We sell it to the government, really anyone that wants to buy a technology to make machines smart.Physical AI vs. Screen AI: Why Safety-Critical Changes EverythingQasar [00:01:38]: Yeah. And I think in the broader AI landscape, a lot of the focus, rightfully so in the last, three years has been on large language models, and so everything fits in a screen. Like, whether it's code complete products or things like that. And what's different about us is we're deploying intelligence onto a lot of things that don't have screens. they're physical machines. There are sometimes screens within the cabin or for example of a car or a truck or something like that, but most of the value we provide is putting intelligence that is in safety critical environments. So that those two words are really important because learn systems can make mistakes if you're asking for, like, some, so something like, “Tell me about these podcast hostsQasar [00:02:28]: that I'm about to go meet.” But you can't do that obviously when you run, like, as an example, we run driverless trucks in Japan right now, as we speak. We can't have errors. Those are L4 trucks. Yeah.Alessio [00:02:40]: Yeah. Was that always the mission? I remember initially, I think people put you and Scale AI very similarly for some things about being kinda like on the data infrastructure side of things. What was the evolution of the company?The Origin Story: Tooling, YC, and the Scale AI ComparisonPeter [00:02:51]: Well, from the very beginning, we always wanted to, really be a technology company that helped generally push forward the industrial sector. And so we started off working in autonomy. Our very first customers were robotaxi companies. And we started off doing a lot of work in simulation and data infrastructure. And then over the years, we've expanded our portfolios. Now we have, over thirty products, and it's a pretty broad technology play within the landscape of physical AI.Qasar [00:03:19]: Yeah, I think the Scale reason is because we're all YC Universe companies. But it was a very different company. Scale, was, is more of a services company, data labeling company fundamentally. We started and still are, do a lot of tooling. So like, you think developer tooling is now in vogue again, thanks to the AI boom. But honestly, ten years ago, it was out of vogue. It w Like, doing a tooling company in 2016, 2017 was not, like, the thing to do because, I don't know if you remember, the VCs generally, their views was that toolings are They're just workflows, and workflows ultimately are not really interesting. And we've gone and come, full circle with that. But when we started the company, our kind of it's kinda like in the periphery of what the company wants to be. It was like, from our earliest days, like, we wanna deploy software on physical machines, like on cars and on trucks and things like that. And obviously, we didn't know that the transformer boom was gonna happen. We didn't know that autonomy systems would become end-to-end. Those things we didn't know. And why that's important when autonomy systems become end-to-end, it is just now those models can be generalized to, multiple form factors. And so back nine, ten years ago, tooling was a great way, and still is a great way to, build the technology and sell technology to our end customers, a lot of them who wanna build this stuff themselves. And so we just offer like a spectrum of solutions from you can just use like one part of a development suite of tools all the way to buying the full thing. The way to think about the company, or at least the way we think about the company is, as Peter said, a technology provider. It's kinda like, what NVIDIA does or what an AMD, but we just don't do chips.Qasar [00:05:06]: We don't do silicon. But we're a technology provider fundamentally. And I think even, we used to joke when we started the company, like, we're not the guys to build, like, Instagram. Like that was just towards That's not our That's just not us in a most fundamental way. IAlessio [00:05:20]: You have thoughts.Qasar [00:05:21]: Yes.Qasar [00:05:22]: Well, it's, it's I mean, I think it's just like what And I mean, we worked on Maps and stuff, Google Maps. Consumer products are extremely difficult for a lot of different reasons. It just, I think doesn't scratch the itch. I think we're like Michigan guys who are kind of more of that traditional engineering kind of a realm, or lineage. we used to jokeThe Three Buckets: Simulation, Operating Systems, and Autonomy ModelsPeter [00:05:41]: I gotta say, though, what was clear ten years ago was that there was so much more that was possible with software and AI in vehiclesPeter [00:05:47]: and that was generally the space that we started in ten years ago.Peter [00:05:51]: And the precise path that we've taken over the years, I think we've been strategic, and we've adjusted to make sure that we're actually building stuff that's valuable to the market. And like, the technology has changed so much. Like our own technology stack has completely changed, I would say, roughly every two years. And so now we've probably done, let's say, four complete evolutions of our own technology stack. And I sort of see that cadence roughly keeping up.Peter [00:06:13]: And so the way even we think about engineering is almost on this two-year horizon, we're preparing ourselves that, hey, like, we wanna invest the appropriate amount, but then also be very dynamic as the research gets published and as our research team figures out new advancements and adapting to that.Qasar [00:06:27]: Yeah. One thing that has been consistent is the type of people we've, we've recruited. It's engineers who are fall into the sometimes very traditional, like, GoogleQasar [00:06:38]: -gen suite, but way different from, other companies. We are hiring folks who really know the intersection of hardware and software, who know really low-level systems. Obviously, traditional ML researchers and folks who've, actually, put ML systems into production. That's been pretty consistent. I think that, like, you look at the mix of our engineering, eighty-three percent of the company is engineering, so it's, like, a giant list.Qasar [00:07:05]: A lot of engineers.Alessio [00:07:06]: Which, by the way, a thousand engineersQasar [00:07:07]: Yeah. A thousand engineers.Alessio [00:07:08]: that's on your website, so I imagine it's up to date.Qasar [00:07:11]: It is, it is up to date, yes. Yes.Alessio [00:07:12]: okay. And then forty-plus founders.Qasar [00:07:15]: Yeah. We would tend to also, This was more luck than strategy. But we've recruited a lot of ex-founders. It's been a great place for founders, YC and non, ‘cause obviously I know a lot of the YC folks. It's kind of like we recruit a lot of Google people.Qasar [00:07:33]: For them to exercise both their technical and non-technical skills because, we're, we're, we're on the applied side. We have a research team that we do fundamental research, we publish, and we've, we've had great traction there. But fundamentally, the business wants to take this intelligence and deploy it into production and there's, like, a certain type of person that's more interested in that.Alessio [00:07:54]: Yeah. You mentioned the tech stack, Peter, so I just wanted to give you some rein to just go into it. I'm interested in where Wayve Nutrition, starts and ends in some sense, what won't you do? What, do you do that's common among all the verticals that you cover?Peter [00:08:10]: There's a few buckets of work that we do, and we've been at this for almost ten years now, so the technology's pretty broad. But we got startedQasar [00:08:17]: Yeah, with a thousand engineers, like, you could work on lots of things.Peter [00:08:19]: There's lots of stuff, yeah, espe-especially with AI tools to help.Peter [00:08:22]: So we got our start in simulation and simulation tooling and infrastructure. And so generally, if you're trying to build a very complex software system that involves moving machines, you need to test that, and the best way to test it is it's a combination of virtual developments, a simulation, and then also obviously real world testing.Peter [00:08:39]: And then there's a very careful process of that correlation between the simulation results and the real world results and ensuring that the simulator is in fact accurate to that. Simulation's a very deep topic.Peter [00:08:49]: We have a whole suite of products in that, and we could talk for many hours about that specifically. But that is one part of what we do as a company. Reinforcement learning as a subpart of that is also super critical. I think a lot of the a lot of the best advancements happening in a lot of these AI systems right now in some way relate to reinforcement learning, and with now we have lots of compute, and you can do tons of interesting things for reinforcement learning. The second bucket of work that we do is on operating systems technology. true operating systems. Like, think about, schedulers and memory management and middleware and message passing and highly reliable networking and data links. Like, the reality is, if you want to deploy AI onto vehicles, you need a really good operating system. And when we were getting deeper into that space, there wasn't really anything that we were happy with.Peter [00:09:39]: Like, things existed, absolutely, and we were using what was available in the market, and as an engineering organization, we roughly realized these things aren't great. We think we can do this better, and so let's, let's build something. And that was then the that was the moment of inspiration that started our operating systems business, which is now a very real business for us. And in order to write and run great AI, you need a great operating system, and so that-that's what got us into that. And then the third bucket that we work on, it's, it's true fundamental AI technology. Models, we do a lot of work in, as mentioned, the foundational research, but then the also the world models and the actual autonomy models that are running on these physical machines, and that's across cars, trucks, mining, construction, agriculture, and defense, and so that's both land, air, and sea.Qasar [00:10:31]: And also, a smaller subsector of that third bucket is the interaction of humans with those machines.Qasar [00:10:38]: So that's a multimodal, experience. Historically, if you're moving a dirt mover or any of these machines, there are, like, buttons you press, whether they're actual physical tactile buttons or something like a touch screen. That's just That fundamentally is changing to where you're just talking to the machine and the machine and you're teaming with the machine.Alessio [00:10:58]: Voice?Qasar [00:10:59]: Yeah, voice, absolutely, yeah.Alessio [00:11:00]: Oh.Qasar [00:11:00]: And also the machine just being aware of who is in the cabin, what their state is. you can think from a safety systems perspective, the most simple version of this is, like, the driver is tired, right? They're, they're if you get those alerts when you're driving your car and saysHardware, Sensors, and the LiDAR QuestionQasar [00:11:15]: -maybe take a coffee break, that take that times, a couple of order of magnitudes up. But this concept of teaming man and machine is important. When you think about running agents or just running, different instances of, Claude and doing work for you in the background, you can take that analogy out, almost copy and paste and put it into, like, a farm, where you have a farmer who's running a number of machines. So where they interact with the machine is where there's maybe a critical decision or a disengagement or something like that, but generally speaking, the agent on the physical machine is running and making decisions on the behalf of the farmer until there's something maybe critical. And that's also what we work on. So that's not pure autonomy. It's a little bit of a mix, but it falls under, autonomy. In the automotive sense, that's typically defined in SAE levels as an L2++ systemQasar [00:12:05]: -with a human in the loop. But just take that idea, to other verticals.Alessio [00:12:09]: Yeah. You've not mentioned hardware at all, like sensors or obviously we you mentioned you don't do chips. I think even in AV there's, like, a big, cameras versus lidars. Like, what are, like, in your space maybe some of those design decisions that you made, and are they driven by the OEM's ability to put things on the machinery? And like, how much influence do you guys have on co-designing those?Peter [00:12:32]: Yeah. So we don't make sensors. Like, we're, we're not a manufacturer. Obviously, we use a lot of sensors in our autonomy products. in terms of what actually goes on the vehicles, we have a preferred set of sensors that we, let's say fully support, and then our customers, they can sort of choose from those. And obviously if there's a very strong opinion on supporting something else, we'll add that to the platform as well. And the lidar question is at this point sort of the age-old,Peter [00:12:59]: topic in autonomy, and the state of the industry right now is lidar is hands down a useful sensor, specifically for data collection and the R&D phase of autonomy development. if you see, for example, a Tesla R&D vehicle, it actually has lidar on itPeter [00:13:17]: to this day, right? In the Bay Area we see these. you'll see, like, Model Ys or Cybercab that have lidars on them just driving around. So it's, it's useful because it gives you per pixel depth information. So if you can pair a lidar with a camerand you can say that, well, this camera's looking this direction, this lidar's looking this direction, and now for each pixel of the camera I can see how far away is that pixel. you can actually then use that as a part of your model training, and then the that depth information then becomes a learned, a learned state of the camera data. And then when you're doing the production system, you can now remove the lidarPeter [00:13:52]: and now you can actually get depth with just the camera. And so that difference between, like, a highly sensored R&D vehicle and then the down-costed production vehicle, we use that across our whole portfolio of products. And of course the end goal is you want super low cost and super reliable.Peter [00:14:08]: And then in certain use cases you have some more, bespoke things. Like in defense as an example, you do things at night oftentimes, and so you care about sensors like infrared, more so than And you don't, you don't wanna be putting energy out, so you don't wanna use lidar or radar.Peter [00:14:23]: but you still need to be able to see at nighttime. So yeah, we work the whole gamut.The Operating System Layer: Why Vehicles Are Like Pre-Android PhonesAlessio [00:14:27]: Cool. So that's kinda like on the hardware level. Then on the OS level, how does that look like? What is, like, unique? my drive- I drive a Tesla. Whenever I drive some other car that has a screen, it always sucks.Alessio [00:14:38]: It's on, like, cheap Android tablet. It's like, it's laggy and all of that. What does the OS of, like, the autonomy future look like?Peter [00:14:46]: When most people, it's really what you just described. When you think about operating system in a vehicle, you're thinking about the HMI, right? The human machine interface, and absolutely that's a an important part of it, but that's actually only one thin layer on top. So when we talk about operating systems for, like, AI in vehicles, there's many layers that go deep into the CPU critical realm and embedded systems, and you're talking about the real time control ofPeter [00:15:13]: let's say the electric motors or the engine and the actuators, and you have different redundancies for different, let's say, the steering actuation in the vehicle. And all of these things, need very core support in the in the operating system. And then of course for autonomy you have real time sensor data that's streaming in, and the latencies there are really important, right? If you try to Imagine you try to run Microsoft WindowsPeter [00:15:35]: like streaming your sensor data in or controlling the vehicle. Like, the latencies are gonna be absurd. Like, you can never do that. And so what's special about what we do is we really have this system level thinking, right? So we're looking at, we care about every performance characteristics of the entire system, and then we also, because we're doing a lot of the software or all of that software, we can fine-tune and control all of those things. So we can very carefully tune in the latencies for every aspect of the system. We can carefully tune in the memory management. We can have the right, fail-safes and fallbacks, for different things. ‘Cause you have to account for what if, what if there is a critical failure? What if there's a cosmic ray that flipsPeter [00:16:14]: a bit in the middle of the processor that causes some, malfunction? And you have to have a fail-safe to all of that, and so the core operating system is a part of that. And then the one last thing, which is a lot less exciting but is, actually a very big topic, is reliability of updates.Peter [00:16:30]: so the I have a Tesla and you get updates fairly frequently, right?Peter [00:16:36]: Once a month. Most companies that are making vehiclesPeter [00:16:40]: are basically never doing updates, and they're And even if they are doing updates, they're usually only updating maybe one module. Maybe they're updating the HMI module. But they're not able to update, let's say, the CPU critical parts of the system.Peter [00:16:51]: You have to go into the dealer for that. And so with our operating system now we can actually enable highly reliable updates of any system in the vehicle, and that's way easier said than done. Like, there's lots of technical, technically deep stuff, in the tech stack to do that in a way that you're not going to accidentally brick a vehicle.Peter [00:17:08]: And right? If, imagine yourAlessio [00:17:10]: That would be bad.Alessio [00:17:11]: Bad.Peter [00:17:11]: Bricking a car is a very expensivePeter [00:17:13]: and honestly, like across the industry maybe one of the most just pure impactful things that we've done is we've just, we're, we're now enabling the industry to actually do software updates.Alessio [00:17:22]: Just to clarify as well, who is the customer for this? Like, I assume a lot of hardware manufacturers have their own firmware, and I'm sure some of them would just have you write it for them because you're experts. And others would have their own. Like, who pays for this? Who invites you into the house? Is it, is it the end user, or is it, is it the manufacturer?Peter [00:17:41]: Yeah. So let me make an analogy firstly on the on the fragmentation of software. So physical machines today are more akin to the state of the phone market before Android and iOS existed, right? So I worked on Android at Google by the way many years ago, and part of the reason that Larry at Google decided to get into Android was they wanted to run Google products on a bunch of phones, and they bought all of these phones from the industry, and it turned out they had like 50 different operating systems on these phones. And it was virtually impossiblePeter [00:18:17]: for Google to make their app run on all 50 devices equally well. And so the solution was, well, actually what if, what if they created-A really great operating system and made it attractive to all of these phone makers, and that was sort of the genesis for what Android was and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of the physical, industry right now, it's a little bit like that. Like, there's yes, these companies have firmware, but they have so many different operating systems, it's so fragmented, and to actually get a modern AI application to run on these vehicles, you actually, you first have to consolidate the operating system, and so that's, that's why we've done that. And then, your specific question was who are our customers? It's, it's, generally it's the companies that are making these machines.Peter [00:19:06]: And we're, we're, we're selling our technology to them to really simplify the architecture and then enable these AI applications to run on them.Customers, Licensing, and the Better-Together StackSwyx [00:19:13]: How much is reusable across? Like, do you have, like, one OS that is just configured for everything, or is there some more customization that is needed?Peter [00:19:22]: Yeah, highly reusable. So the fundamental technology is quite universal, right? So things that we do have to think about though are, like, chipset support. And so if you're, if you're coding, let's say, an LLM and you have start with an assumption that, “Hey, oh, I'm gonna, I'm gonna use CUDA, and I'm gonna run this, on an NVIDIA chip,” then you don't really have to think about the hardware in that sense. Like, you're just, “Okay, I'm just I'm in the CUDA/NVIDIA ecosystem, and I'm, I'm going to use that.” But the hardware, especially in safety critical systems, it's a lot more diverse. There's not one or one or two players. There's a bunch of different chipsets that we have to support. And so our operating system doesn't just run on, like, the equivalent of X86. It has to, it has to run on a number of different architectures from chips from a bunch of different companies. But again, we've been working on this for a long time now, so we have, we have support for all of those chipsets. And then when you want to then run the AI applications, we can then do that reliably across now a variety of providers.Qasar [00:20:19]: And I think that is, like, heavily inspired by Android, right? Android has a huge suite of testing and it's a reliable operating system that runs on thousands of devices. And we think we can, we can do the same in all these physical moving machines, with the difference that we're really in a safety critical realm. Android isn't.Alessio [00:20:40]: So on Android, I don't need to use Gmail, I can use Superhuman. Like, what about your machinery? Like, can people bring somebody else's automation to it, or is it kinda like all-in-one?Qasar [00:20:50]: You have to use us. No. Yeah. we're If, Yeah. Yeah, it's totally open. Yeah.Peter [00:20:56]: Yeah. our philosophy is that we are a technology company, and so we license our technology to customers to use how they want. And so if a customer wants to If they wanna license our autonomy tech and our operating system, then great, we'll license those. If they just wanna license the operating system and then use different autonomy tech, that's fine also, and we have great documentation andSwyx [00:21:17]: Or if they wanna use developer tooling.Peter [00:21:18]: Yeah, exactly.AI Coding Adoption: Cursor, Claude Code, and the Bimodal EngineerSwyx [00:21:19]: It's, like, a better together if, obviously, if you, if they work together. Is it all C++ I assume is with different compile targets?Peter [00:21:27]: We use a lot of C++.Peter [00:21:28]: Rust is sort of a hot, the new hot kid on the blockPeter [00:21:32]: for a bunch of things as well. But yeah, the lower level you get, especially when you get to real-time constraints, you hit C++ at some point, and at some point maybe you work your way into assembly when needed.Swyx [00:21:44]: Oh, damn.Alessio [00:21:46]: I'm curious about the coding agent adoption, just, like, since you're mentioning more esoteric languages. Like, what's the adoption internally? What have you learned?Peter [00:21:55]: Yeah. We use everything. So Cursor was, I think the hottest tool in the company for a good while. Now Claude Code, I think has taken the reign on that. We have a internal leader, leaderboard that we use just to sort of encourage adoptionPeter [00:22:09]: with-within the company. And yeah, it's, they're phenomenally useful. it's, Honestly, we take inspiration from some of those tools also in how we're adapting some of that mindset of thinking to the physical realm. Like if it's so easy to build an app for this or that thing that lives just on a screen, we can We're taking now a lot of the same ideas and applying that to, “Okay, well, if you wanted a physical machine to do something, how easy can we make that, using our own tooling and platform as well?”Alessio [00:22:40]: Are you changing any of, like, the OS architecture, kinda like the way you expose services to, like, be more AI friendly or?Peter [00:22:48]: Yeah, absolutely. The in the early days of our tools infrastructure work, it was a lot about, You had engineers that were experts in certain topics, but the things that you're dealing with, they're oftentimes more mathematical or more abstract, where actually GUI tools are very useful for certain things. Like as an example, we have a product we call Sensor Studio, which is, it helps you design the sensor suite for your autonomous vehicle, whether, again, it could be a car, it could be a drone, could be a mining equipment, could be a robot. And you place sensors in different places. You There's different, There's a library. You can understand what are the trade-offs that you're making in the design of that system, and that was, like, a very, a very GUI intensive, thing ‘cause it's a little more like a CAD tool in that senseSwyx [00:23:37]: YepPeter [00:23:37]: if you've seen CAD tools. Nowadays, though, right, we expose all of the underlying APIs for that and now using, AI agents, you can actually configure a sensor suite with just text and likely reach a better result than you could've through the GUI in the past, and we're taking that thinking now through the whole product portfolio.Swyx [00:23:57]: Another thing I was thinking about is just in terms of, like, AI, adoption, does it change your hiring at least a little bit, or how do you, how do you sort of manage engineers, differently?Peter [00:24:08]: Yeah. absolutely, it does. we, I think like every company in the Valley right now, are evolving our hiring practicesPeter [00:24:16]: because the skills required to be effective are changing so fast, right? you used to really select for just rote implementation ability and now it is more the AI engineer skill set, right? Where it's like, yeah, how to implement, but actually-Just banging out code is no longer the core job, right? It's, it's actually knowing what questions to ask, knowing how to tie, how to tie together these different AI tools. And so the interviews that we give now I think are way harder than they've ever been.Peter [00:24:46]: But we also allow, right, selective use of AI tools to solve the problems. And I think in that you start to see more of a bimodal distribution of engineers, right? You start to see like wow, there's, there's this subset of people that they really get it. Like they're, they're all in and they've, they've clearly invested the hours needed to learn these tools and how to be effective.Peter [00:25:09]: And then there's sort of the group of people that haven't done that, and that the productivity gap is just enormous. And so we're, we're trying to obviously select for the people that are really into this.Qasar [00:25:20]: I first wrote the my AI engineer piece three years ago, and when I first wrote about it, I was like, “Actually, not everyone should be an AI engineer,” ‘cause I think there's a there's an extremist stance where well, every software is an engineer is an AI engineer. And my actual example of people who should not be adopting AI was embedded systems and operating systems, and database people. Are they adopting AI?Peter [00:25:41]: I think it's the classic bitter lesson, topic, which is the Six months ago I would've said the same thing, but it's, it's becoming super useful for every domain.Qasar [00:25:53]: I'm sure.Peter [00:25:54]: Right? Like,Peter [00:25:56]: there was, I think six months ago, or maybe a year ago, if you tried to use, let's say the latest Claude model for writing shaders, GPU shaders, the results were probably underwhelming. And if you use the latest model now to do that kind of task, you're a little bit blown away, like, “Wow, that actually worked. That's amazing.” And we see the same thing in the embedded realm. No question though, especially when you get into safety critical systems, the human validation isPeter [00:26:25]: is 100% key. Like I You're not gonna trust your life to a an AI written software that's, that's not been very carefully, checked by humans. And so I think now the really the challenge is about that appropriate level of human validation for these safety critical systems.Verifiable Rewards, Evals, and Neural SimulationAlessio [00:26:41]: How do you think about, yeah, touching on the simulation side, I think verifiable reward and reinforcement learning is, like, the hottest thing. What have you done internally to build around that? And like, what gives you What makes you sleep at night? Like, if somebody's like, just web coding something or likeAlessio [00:26:57]: wants to try something new, you have like a good enough system. Because I think the opposite is also true, is like if it's super easy to write anythingAlessio [00:27:04]: then it puts a lot of work on like the verifiableAlessio [00:27:07]: side of it. Like, what does that look like for people?Peter [00:27:10]: Yeah. So verifiability, a broader bucket of like evaluations, right? Like how do you evaluate the results that you're, you're getting? I think this is probably the hardest problem right now, because the As the models get better, it can be harder and harder to find the faults on the system.Peter [00:27:29]: And so like the problem of doing proper eval to find those faults, like that problem also keeps getting harder as the models get better. But it's no less important than it's ever been, right? You still there are still going to be edge cases that are not met and whatnot. And so it's, it's a big area of investment for us. On the reinforcement learning topic, the key thing is there's all these new requirements that come to be in the latest generation of these technologies. So for example, end-to-end is the big thing right now in autonomy and physical AI, which is you can now train these models that can effectively take sensor data in and then put control signals out, and get really good results out of that. But the way that you train and improve those models is really different from the previous generations. And so to do reinforcement learning on an end-to-end model, you now need to actually simulate all the sensor data, right? So then this becomes a we call our, work in this neural simulation, but it'sPeter [00:28:26]: think of it like a hybrid of Gaussian, splatting and diffusion methods, and where you really care about performance. Like performance is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are worthwhile, in the end. It also gets to a lot of our work in embedded systems, which is like performance critical work, and that performance optimization, performance criticality, it carries over to a lot of the model training work. because, like, the only way to make it affordable is it has to be really fast.Qasar [00:28:58]: I think it's worth a few minutes talking about our own, evolving thoughts on verification and validation withinQasar [00:29:05]: kind of, traditional simulators, which are, you can think of like vehicle dynamics or something like that, which you're just taking textbooks and taking those formulasQasar [00:29:13]: and putting them into software, to like now this neural sim/world model universe. I think that's an interesting topic.Peter [00:29:20]: Yeah. So in more traditional development, right, you oftentimes would have, more black-and-white answers to questions.Peter [00:29:28]: And so the in Europe as an example, there's, a regulatory, system, it's called Euro NCAP. It's the European New Car Assessment Program, and as part of that, the vehicles have to pass a bunch of tests, and those tests actually, include, safety systems. So automatic emergency braking for a child that runs in front of a carPeter [00:29:51]: or let's say an occluded child that runs out and you hit it. And so you have You end up with sort of these binary answers of like, well, did the car under test pass this specific test? And there's a very well-known set of test casesPeter [00:30:05]: that the vehicle has to pass. And that was how the industry worked, let's say, until 10-ish years ago. But what's changed now is with these models, everything is statistics, right? Like you no longer have a black-and-white answer, but it's like, well, how many orders of magnitude or how many nines of reliability can I get in the system, and how can I, how can I prove that to be true? And the big unlock honestly for physical AI as an industry is that these models are just becoming much more reliable. Right? Things like things actually work a lot better. It's like the number of nines you can get out of these systems are now good enough that it actually becomes cost effective to really deploy these things. And so the big shift in, so verification and validation has been from a little bit more of a Again the past it was strictly requirements, and are you meeting or not? And now it's more of a statistical, verification and validation case where it's all about how many nines of reliability and meantime between failures, that sort of thing.Statistical Validation, Regulators, and the Cruise LessonSwyx [00:31:04]: And is the target audience regulators or even the customers are yeah, if you I imagine the customers are bought in, and it's mostly regulators that need to be satisfied.Peter [00:31:15]: We do work with the US government, we do work of course with the European governments and the government of Japan, and the government is not like an AI lab by any means.Peter [00:31:25]: So Swyx [00:31:26]: They just care about the outcome.Peter [00:31:27]: They care about the outcome.Peter [00:31:28]: And so we do education, in that regard, and like so sort of teaching about, “Hey, this is how we think validation should be done, and this is an approach that we think is reasonable,” and how to think about like when is a driverless system actually safe enough to go on the roads and that sort of thing. But I wouldn't say that the government is asking for it. It's like we're more teaching the government in that, in that sense. It's honestly, it's more so for our own, our own comfort, right? Like, we want to build very safe systems, and then of course our customers care deeply about that as well. But in that context we're also typically educating our customers.Qasar [00:32:01]: Yeah. Our first, our first core value is on round safety. So I think we can't underline enough that, us also verifying and validating that the systems that we're deploying are safe to us is probably as important as, like, some regulator or a customer saying,Swyx [00:32:19]: Of course. Okay. Yeah.Swyx [00:32:20]: You have to satisfy yourselves.Peter [00:32:22]: As I say, as a whole across the world, regulation oftentimes it's like a almost lowest common denominator. But like, you really have to substantially exceed what the regulators are expecting to make good products.Swyx [00:32:33]: Yeah. One thing I often talk about, I think and I try to make this relatable to the audience also, is Cruise, where they had an accident that basically ended the company. I wonder if people overreact to single incidents, because incidents are going to happen regardless, right? ‘Cause it's a statistical thing, but as long I don't know if regulators understand that, you cannot extrapolate from a single incident, but we do because that's all we have to go on. And your sample sizes are necessarily gonna be lower than, I don't knowSwyx [00:33:00]: consumer driving.Qasar [00:33:01]: Yeah. I think the Cruise example wasn't a technology failure. there was The real, compounding issue there was just how did the company talk to the regulators and what was their kind of behavior, and I think that became more of the issue. If you look,Peter [00:33:19]: It isn't It definitely was a technology failure, but it was made much worse by theSwyx [00:33:23]: Put the car back on the woman.Qasar [00:33:25]: Yeah. And let me put it another way. There is a version where Cruise still exists.Swyx [00:33:29]: right. Right.Qasar [00:33:30]: Right. It'sSwyx [00:33:30]: It was like the last strawQasar [00:33:31]: ItSwyx [00:33:31]: in like a long chain ofSwyx [00:33:33]: like issues.Qasar [00:33:33]: So do you feel like ATG had that horrific accident or someone actually dying, because, that was a homeless person crossing the street? So yeah, I think we can't understate enough that ultimately, like, statistical validation of something, that's one part of it, but it's not the only part of it. Like, consumer and let's say, mainstream adoption of these technologies is also gonna be part of that conversation. I think companies like Waymo are doing a lot of service positively to the industry in the sense of they're, they're setting a high benchmark and they're showing, kind of in a very responsible way how to, how to deal with these. There have been Waymo incidences as well. They've just not been as significant as the Cruise one that you mentioned. But yeah, so I think you'll just continue to see that. I think probably the long term question is really gonna be, again, around Like it is very clear humans are way worse drivers statistically.Qasar [00:34:29]: Like, there's no, there's no debate. And so at what point But we're emotional animals.Swyx [00:34:34]: Yeah. So my thing is, like, we have to get to a point as a society where we accept horrific accidents that would never happen by a human because statistically we understand that it is safer overall. In the same way that planes, they're safer, than I think they're the safest mode of transport that we have.Qasar [00:34:50]: Yeah. it's more dangerous to drive to the airport than it is to get on a flight.Qasar [00:34:53]: So if you're everQasar [00:34:54]: if you're ever getting nervous about getting on a plane, just think “I just gotta get to the airport.”Swyx [00:34:58]: Yes, we're flying.Qasar [00:34:59]: If I get to the airportQasar [00:35:00]: I'll be good.Swyx [00:35:00]: But then it's, planes also concentrate the tail risk if planesQasar [00:35:03]: Yeah. AndPeter [00:35:04]: And I was, I don't think we honestly have to worry about there ever being, accidents from these systems that are like much worse than what humans would cause, ‘cause humans do terrible things.Peter [00:35:14]: Like, people fall asleep at the wheel all the time.Swyx [00:35:16]: I have.Swyx [00:35:17]: Like, I'll call, I've been a drowsy driver.Peter [00:35:19]: Kinda drunk drivers, and that'sPeter [00:35:20]: that's the extreme end of the example. But these AI systems, you have redundancies, you have fallbacks. Like, there's many things have to go wrong for there to actually be a something catastrophic because there's, there's so many, fallbacks that these systems have.Alessio [00:35:36]: your simulation is like so vast because there's so many use cases. What are, like, maybe things that worked in a simulation and then you put it out and it's like, “F**k, this isAlessio [00:35:45]: this just did not work at all?”Peter [00:35:47]: Yes.Alessio [00:35:47]: IsPeter [00:35:47]: That's maybe a bit of a misconception, about simulation there. So let me go a little bit, more technical on this. So at first go, no simulation is going to represent the real world. There's always a process of this, sim to real matchingPeter [00:36:02]: where you actually, you need the real world feedback to basically feed into the parameters that are being used in the simulator, and you have to do that, it's like this validation flow, a number of times until you can get some confidence that, like I think the simulator is now accurately representingPeter [00:36:19]: what's gonna happen in the real world. Now, if you have a situation where you've done that full validation and you thought that it was accurate and then there's something different, those are much trickier cases, and that's, that absolutely can happen, but really I think the validation process is a really important part. You can never skip the simulation validation process, like where you're actually ensuring that, hey, the actual, my sim to real gap here is small enough that I can trust these simulation results. And there's, there's so many fun things that you can do when you get into it. Like, I'll, I'll give one fun example that came up recently is like in these humanoid robotics, systemsOverheating actuators is a real problem, right? So obviously phenomenal demos. IPeter [00:37:01]: The most amazingAlessio [00:37:02]: For 10 minutes.Peter [00:37:03]: The most amazing I can get. I love, I love watching robots do acrobatics like everybody but the these systems actually overheat, right? If, like, And one of the ways you can use simulation though is you can actually have that, the temperature of those actuators be one of the parameters that's representedPeter [00:37:18]: in the simulation. And if you're doing reinforcement learning over a certain task, then the robot can actually adjust its motions in the simulation to account for the fact that, oh, it knows that as it's moving, it's actually beginning to overheat this motor. But if you didn't have that parameter of, let's say, the heat of that motor represented in the simulation initially, then your RL policy might It will disregard that. And now you run that on the robot and the robot will overheat and fail.Alessio [00:37:43]: I guess the question is, like, how do you have all of these parameters taken care of while also understanding the deployment environment? Like, temperature is like a great example, right? WellAlessio [00:37:53]: why did you make my robot worse when it runs in like a freezer?Alessio [00:37:57]: So it actually shouldn't worry about that. it's like, yeah, how do you design these simulations?Peter [00:38:02]: This is honestly the This is what makes simulation so hard, right? it's because you Simulation is fundamentally about you're trying to optimize the development of a system, right? Like, how can I build this system faster and better and cheaper and what are all the levers that I have to actually accomplish that? And because simulation's just a software program, you can, you can change it a lot more easily than you can hardware systems. And then what's particularly awesome about the let's say, world models and using that as a part of simulation is now the simulation doesn't just scale with, let's say, adding new math equations inPeter [00:38:36]: but we can actually scale the simulation environment now with additional real world data and that also unlocks a whole new field of robotics.Qasar [00:38:46]: There is a meniscus line where you cross where still doing real world testing is better. there's, in this, sim-to-real gap, you can reproduce reality at exceedingly expensive costs and this So nothing is free. So really you have to you're finding that line where you're getting great performance, you're getting great feedback, whether it's on the training side or on the eval side, but it's way cheaper than doing it in the real world. At some point it, that doesn't make sense. And so even, from our earliest days in autonomy, our view was you're still gonna do real world testing. You There's, there's not, there's not this, magical land where you're not gonna do that. And maybe even like a more nuanced version of this in like traditional software development is, most of your testing for software in a vehicle, 95% of that can be like traditional CI/CD kind of, flows that you would have in traditional web development. But once you have Now you, let's say you have a truck. Well, you can do like 4% of those in like a rig which has all the components, the electrical and electronics of a truck, but doesn't have, it doesn't have the tires and it doesn't have the And then you have the 1%, which is actually the vehicle. There's something There's a similar analogy in terms of using simulation for intelligent systems. You can do a lot in a simulator, but in using world models, but ultimately it's, it's physical AI. So you're gonna deploy it on physical machines andQasar [00:40:17]: the freezer example comes to, comes to light.Alessio [00:40:20]: The world model thing has been to me the hardest thing toAlessio [00:40:22]: wrap my head around. Like we have Faith Eliyon on the podcast.World Models, Hydroplaning, and Cause-Effect LearningQasar [00:40:25]: We've been doing a small series with like another Intuition company, General Intuition as well.Qasar [00:40:31]: yeah, and I mean, lots of, lots of coverage on NeRFs and yes.Alessio [00:40:34]: Yeah. It feels like we talk with about, the heliocentric system, right? It's like in a world model, if you just feed visual data, the model might learn that the sun spins around the Earth. It makes sense, right? And it's like, well, not really. And I think what are like some of these other things that like hydroplaning is one thing I think about, is like can a world model understand hydroplaning and like what amount of water like causes it to happen? And it's like, yeah, to me it's like I don't understand how you guys do it. I guess it's like the real thing is like when you're doing both cars and the highway in Japan versus the excavator in a mine in,Qasar [00:41:13]: ArizonaAlessio [00:41:13]: wherever you're Arizona, wherever you're deploying them.Alessio [00:41:15]: How much of it are you relying on the world models to like generate the simulations for you and then try and close the gap after versus like giving the world models as a tool to your engineers to like curate the simulations if that makes sense?Peter [00:41:28]: Yeah, totally. So yeah, I can say at a pure engineering level, I think if you're hoping to do real world deploys and you're purely relying on a world model approach, you probably won't get to something that works, before you go bankrupt. So there is just a very practical mindset of like, world models are amazing and they're extremely useful for a lot of use cases, but there are a lot of other things that you need to do to actually get something started and something deployed and working. most fundamentally, world models are all about It's understanding the world, but also understanding what's going to happen. It's like the cause-effect relationship.Peter [00:42:01]: Right? And so like it, right, if you have a take some sort of construction tool, and that construction tool is gonna be doing some work on the Earth in some way, it's gonna be moving earth, the world model needs to understand that cause-effect relationship. Like, okay, when I, when I take this material from here and put it over there and now I have things that are over here and not over there anymore and that cause-effect, relationship. data obviously is a is a big problem. The hydroplaningPeter [00:42:26]: one is actually a really great example because it's actually quite non-obvious sometimes. Right? It's like, well, it's, it's raining and well this road, has, let's say the appropriate curvature to it so the water is running off the road and cars are driving faster here and then you approach a road that's very flat and water is now puddling on that road and all of a sudden cars are driving slower because when they were driving faster they were starting to lose control. And there are a lot of visual nuance, very nuanced visual cues in the scene and so I do think in the world model concept there's a good chance that the model actually would learn that you should just drive slower when these visual cues exist, and that's obviously the beautiful-The beauty of, these kinds of models where they just, they learn these non-obvious things.Swyx [00:43:14]: It doesn't need to know about hydroplaning to know that it needs to drive slower.Peter [00:43:17]: Yes.Swyx [00:43:17]: I guess it's Yeah. I wanna ask questions about, also deploying models. I presume, like, you use a lot of these world models for training data and simulation, but what about deploying it onto the systems in production? Presumably you have you have, like, GPUs on deviceOnboard vs. Offboard: Latency, Embedded ML, and DistillationSwyx [00:43:36]: but they're I keep saying on device. What's the what's the right term for that?Peter [00:43:40]: On machine.Swyx [00:43:41]: On machine.Peter [00:43:41]: Or embedded, yeah.Swyx [00:43:42]: Yeah. What is the embedded world like? because for people who are not used to that world, this is very alien.Peter [00:43:49]: Yeah. So it's actually We call it onboard and off board.Peter [00:43:52]: So like, onboard software and off board software.Peter [00:43:54]: And the great thing about off board software is you don't have to care about time, and you can run really large models, right? So you can, you can say, “Well, this model, I don't care if it takes one second for it to give me a result or 10 seconds for it to give me a result, because we have time.” And the models can be really big, and they can run, in a data center or on a on a huge GPU and you can obviously have distribute to compute, et cetera. But onboard you don't have any of those benefits. You're like, “Well, I need I have this many milliseconds where I need an answer from this model.” And so a lot more of the energy then is about, think of it more like distillation and it's like truly efficiency and like, literally every fraction of a millisecond counts. And you can't have a situation where the model takes too long because then the vehicle can't actually function.Peter [00:44:42]: And so you can, you can still use a lot of the same techniques, and the models themselves you can think of as like a derivative of larger models that you can run offline, and then you're, you're trying to just get a model that is still performs really well but it's, it's a it's smaller, small enough version that you can then run on this embedded system where you care about latency and power.Qasar [00:45:03]: Yeah. And I think like, the broader point I think which, maybe is not obvious but it's worth saying is in physical AI world, we're not really constrained right now by, like, the intelligence of the models. It's actually what Peter's talking about, it's actually deploying them inSwyx [00:45:19]: The hardware they give you.Qasar [00:45:21]: Yeah. On the hardware you give you.Qasar [00:45:22]: And so And there's just a reality is of safety critical systems. So those end up being the your limiting factorsQasar [00:45:29]: rather than, let's say, a limiting factor for, a foundation model companyQasar [00:45:34]: is gonna be just capital maybe or researchers.Qasar [00:45:38]: So we're, we're in that way dealing with, for us as people who kind of come in that realm with like a very interesting Those constraints force creativity.Swyx [00:45:47]: And I imagine, nobody was deploying or giving you the hardware for transformers back in 2018, whatever, but now they are. What's the evolution like? just peel back the curtains a little bit.Peter [00:45:59]: Yeah. Transformers first off, I think the paper was originally published in 2017.Swyx [00:46:02]: 2017.Swyx [00:46:02]: So there's no time.Peter [00:46:04]: And ISwyx [00:46:05]: But I'm just saying I guess I'm saying, like, embedded ML systems usually, like, a lot less parameters, a lot less compute, and now, like, orders of magnitude more.Peter [00:46:14]: Yeah. absolutely. what I was gonna say though was I think in the in the original paper in 2017, maybe it's in the last paragraph, somewhere in the paper they talk about, like, “Oh, by the way, this technique might be useful for, like, images and videos as well.”Peter [00:46:30]: These last subjects.Peter [00:46:31]: And it took a few years for that impact to really hit. But like, now, we're seeing transformers are everywhere.Swyx [00:46:39]: Yeah. Vision transformers.Peter [00:46:40]: And then then the compute just keeps getting better and better. But you do have this fundamental trade-off, right? It's like you have power, you have cost, and performance and like, getting the right, getting the right mix of those things in an embedded package that can also be, like, shaken and baked in all thePeter [00:47:00]: conditions that these things have to have to operate in. But yeah, I think that they're only going to keep getting better and so we also try to plan our strategy understanding that, we know the rate of improvements of these systems.Swyx [00:47:11]: Yeah. So like, Google just released the Gemma 2B modelSwyx [00:47:15]: that effective 2B model. Is that useful to you guys or is that too big?Peter [00:47:18]: You can run that model on an embedded system, definitely.Peter [00:47:21]: the So yes, it's, it's useful in that regard. The bigger question is, like, what do you use it for in an embedded system? Like, you actually need to customize it quite a bit to make it useful for something. But yeah, you could run a two billion parameter model, definitely.Swyx [00:47:35]: It also interesting, like, what percent is a custom ML model that only does that thing versus a generalist LLMSwyx [00:47:41]: which probably is not that useful actually for your context.Peter [00:47:46]: Like, you, like, you can imagine different use cases, right?Peter [00:47:48]: So theSwyx [00:47:49]: The voice stuff, yes.Peter [00:47:49]: Yeah, the voice test. Totally, yes.Peter [00:47:51]: So for the actual, autonomy elements, that's 100% in-house. We do every bit of that, the data simulation, the model, everything. But when you get into the more generic use cases like voice or voice assistant kind of thing, that's where these more generalist models like Gemma actually can be quite, can be quite useful.Swyx [00:48:09]: Yeah. And then there's also obviously a trade-off between, like, what percent must you do on machine, versus just call home.Peter [00:48:16]: Yeah. It's all about latency.Swyx [00:48:17]: Latency.Peter [00:48:17]: It's all about latency. Yeah.Swyx [00:48:18]: Yeah. Well, like, I think actually in a lot of contexts, especially in the US, you can just have a connection to the web.Qasar [00:48:26]: Yeah. I think though most of our universe is everything has to be fairly, embedded and local because just the nature of Even in the US there's a lot of likeSwyx [00:48:39]: PatchinessQasar [00:48:40]: don't haveQasar [00:48:41]: have coverage, right? And if you look at, like, the old world of autonomy within mining, which is, like, long before transformers and kind of, neural networks, in the like CNN and kind of a universe, they were really just hand-coded, systems. They were just like, this machine is gonna run to that place with thisPeter [00:49:03]: That was our GPS, like very accurate GPS.Qasar [00:49:05]: Yeah. And so that worked, and that worked for 20 years, so why would we actually need to use transformers or kind of more modern end-to-end systems? Mainly because you can only really run a path and run backwards. That provided a lot of value, but m-Not as much as you get when the machine is actually intelligent. It's, it's seeing, it's perceiving, it's acting in a dynamic world.Alessio [00:49:28]: I looked up RTK, real-time kinematic, one to two-centimeter accuracy.Qasar [00:49:32]: Yeah. Fantastic. But the and fantastic in faraway lands where there's not gonna be cell phone coverage.Peter [00:49:39]: Yeah, so it's widely used on the legacy mining and agricultural autonomy systems today. So like, for example, a combine that can be precise within one or two centimeters as it's driving down the field, they use RTK.Qasar [00:49:53]: Yes.Peter [00:49:53]: But it's, it's expensive.Qasar [00:49:54]: Yeah. And it's, it's, it's autonomy, but it's not intelligent in the way that I think all of usQasar [00:49:58]: if in twenty-six we'd be talking about intelligence.Alessio [00:50:00]: In one of your blog posts, you mentioned research on large scale transformers that are similar to those doing modern generative AI. What are, like, the big differences other than, “You're absolutely right. I should steer the car, so you probably wanna remove that?”Peter [00:50:14]: We have a diversified bet strategy internally, and the reason we've done that is because we operate in now a bunch of industries, a bunch of geographies, and each of the approaches has, obviously a different risk to them.Peter [00:50:27]: And so like, we're not going to put all of our eggs in a single basket for a single approach because that approach may no

The 1945 General Motors Annual Report serves as a historical record detailing the corporation's transition from intensive military production to peacetime manufacturing at the conclusion of World War II. The documents outline how the company delivered over twelve billion dollars in war materials, such as jet engines and tanks, while simultaneously navigating the complex process of industrial reconversion. Leadership emphasizes a commitment to transparency, providing stockholders with data on financial performance, workforce demographics, and the impacts of nationwide labor strikes. The report also highlights various social initiatives, including veteran reintegration programs, employee safety awards, and technical training through the General Motors Institute. Despite encountering obstacles like material shortages and work stoppages, the text illustrates a massive effort to restore civilian production of automobiles and appliances globally. Overall, these sources capture a pivotal moment of economic rehabilitation and organizational shifting within one of the world's largest industrial entities.

With artificial intelligence (AI), mass customization and shrinking lot sizes changing expectations in manufacturing, companies need new skills and abilities from those who they hire. So, where do you find an entry level engineer with years of experience and training in how to deal with the constantly changing needs of production? In this episode of Great Question: A Manufacturing Podcast, Kettering University President Robert McMahan says getting students experience while in school is key. The former General Motors Institute (a private university, separate from the automaker since the early 1980s) has long used factory floors and design centers as training grounds for student education. Current GM CEO Mary Barra is among the school's alumni who got their first taste of manufacturing while studying engineering there.  In this episode, McMahan talks to IndustryWeek's Anna Smith about the school's educational co-op model and how schools need to collaborate with manufacturers to prepare the next generation of industrial leaders. 

EPISODE 658 - Jennifer Elwell Comeau - Four Specific Images, One Story, Ireland in 1820 and Untold Family TalesWhat happens when a high-powered engineer walks away from corporate life to follow a dream rooted in ancestral wisdom, music, and the magic of nature? Follow my journey of transformation, where I've aligned my gifts - storytelling, songwriting, speaking, and mindful walks in nature - with the needs of the world.Stepping back in time, I grew up one of eight children just outside Buffalo, New York. Following the dictates of family and societal programming, I jumped into the world of engineering, earning a Masters of Engineering from University at Buffalo, and a Bachelors of Industrial Engineering at General Motors Institute, now Kettering University. After decades working as an executive for manufacturers in the auto industry and then in information systems, I returned to my early love for how words, music, nature, and beautiful places can enliven the whole landscape of a life. I began to write songs, and stories — speaking and performing them throughout the country, and soon, narrowed my focus to align with my reverence for Earth. https://www.jennifercomeau.com/Support the show___https://livingthenextchapter.com/podcast produced by: https://truemediasolutions.ca/Coffee Refills are always appreciated, refill Dave's cup here, and thanks!https://buymeacoffee.com/truemediaca

Part 1: Luke Chennel - John Bond (1912-1989) and his wife Elaine bought the faltering magazine Road & Track in 1949. Over the course of his ownership and editorship, Bond built the magazine into a major cultural force. This presentation examines the dimensions that Bond engaged with his editorial viewpoint from a wholistic cultural lens. Bond built a durable version of car culture, the practices and values of which remain in many forms today, though under challenge from old and new trends in the automotive industry.  Bond's version of car enthusiasm stemmed directly from two sources: his education at the General Motors Institute and his enthusiasm for European racing. Road & Track's coverage of the foreign motorsports scene for some time was the only widely available source material for an American audience. Luke argues that Bond's two decade editorship (1951-1972) of Road & Track created the foundational dimensions of traditional “car guy” culture, with its familiar and clubby atmosphere familiar to those “in the know,” but also acted in an exclusionary way to women, casual automobile and racing enthusiasts, and those who might have appreciated automobiles from other dimensions than their mechanical design or performance on certain tests. Finally, the presentation examines Bond's version of car culture in a contemporary light, considering the roles of the changing nature of racing and its relationship to road vehicles, the renaissance in electric vehicles, and debates about mobility in the contemporary climate. Part 2: Kristie Sojka - will explore the progression of gender representation within the time that John Bond owned and edited Road & Track magazine. It will examine all aspects of the publication between the years of 1951-1972, including cover art, article content, photographs, and advertising. The presentation will compare and contrast the first ten years of Bond's editorship with the last ten years to identify any potential changes in female representation. With the historical perspective of developing gender politics of the time period, the presentation will consider whether these societal shifts had any impact on women's representation within the pages of the publication. Part 3: Ken Yohn - will explore car culture from an anthropological perspective, as a complex whole combining both behavior and the material objects integral to the behavior. This formulation of culture thus includes material artifacts, rituals, customs, language, beliefs, institutions, and techniques, among other elements. This presentation will address two main questions. As presented in Road & Track, what are the essential elements (behavior and artifacts) of car culture? Second, can we learn anything, or draw non-obvious conclusions about car culture by adopting this type of anthropological perspective? ===== (Oo---x---oO) ===== 00:00 Introduction and Overview 00:26 John Bond and the Rise of Road & Track 02:24 Luke Chennel's Personal Journey 03:51 John Bond's Influence on Car Culture 07:13 Bond's Vision for American Sports Cars 10:19 The Role of Racing in Car Development 15:07 Kristie Sojka on Gender Representation 23:56 Advertising and Gender in Road & Track 31:56 Disappointment in Female Representation 32:41 Dedication to John and Elaine 33:01 Ken Yon's Anthropological Perspective on Car Culture 34:12 Exploring Car Culture Through Different Lenses 37:01 Defining and Undefining Car Culture 43:11 Intergenerational Transfer in Car Culture 50:58 Q&A Session and Final Thoughts 57:34 Sponsors and Closing Remarks ==================== The Motoring Podcast Network : Years of racing, wrenching and Motorsports experience brings together a top notch collection of knowledge, stories and information. #everyonehasastory #gtmbreakfix - motoringpodcast.net More Information: https://www.motoringpodcast.net/ Become a VIP at: https://www.patreon.com/ Online Magazine: https://www.gtmotorsports.org/ This episode is part of our HISTORY OF MOTORSPORTS SERIES and is sponsored in part by: The International Motor Racing Research Center (IMRRC), The Society of Automotive Historians (SAH), The Watkins Glen Area Chamber of Commerce, and the Argetsinger Family - and was recorded in front of a live studio audience.

Mary Barra grew up in the suburbs of Detroit, aka: the heart of the auto industry. Her father worked at General Motors for 40 years, and Mary became a second generation GM'er. She worked her way up the company, studying at the General Motors Institute (yes, it was a thing) to eventually becoming the CEO in 2014. Mary claimed GOAT status in the auto industry for steering the company through several crises – and for being a mentor to other women in the biz.  In this episode of 9 to 5ish, Mary shares:  Who would make the cut on her cross-country road trip roster Why she's never blinked twice at the rarity of being a woman in the auto industry The most difficult crisis she had to navigate a CEO Advice to her daughter and other women on when it's time to leave a job Why she views herself as a caretaker to General Motors and what that responsibility means 

About Aditi Sharma: Aditi Sharma is the co-founder of Grow Commerce. Aditi started her career as a chip designer, did her MBA, and joined McKinsey. Then, she chose to join Grab. When Aditi joined Grab, not only was she expecting her first child, but Grab was in overdrive to enter and grow across South East Asian markets while competing against formidable competitors such as Uber and gojek. It would have been a super caustic high-pressure environment to drive and deliver consistently. Eventually, Grab acquired Uber's South East Asian business in 2018. So Aditi took a leap of faith by not joining Google, but Grab, which at the time did not even have maternity coverage insurance. That was a bold move, especially for an expecting mother. Women often hesitate to take such steps because they feel unsure about managing their personal and professional lives. Therefore, Aditi's journey matters to all who must make bold moves and walk the unchartered path. I recently listened to an interview by Ginni Rometty about her journey to becoming IBM's first female CEO in 2012. She is another corporate leader who exemplifies bold and strategic decision-making. She was born in 1957 in Chicago. Ginni grew up in a family that struggled financially, but through her perseverance, she attended Northwestern University and earned a degree in computer science and electrical engineering in 1979. After graduation, she started her career as a systems engineer at General Motors Institute (now Kettering University). In 1981, Ginni joined IBM as a systems engineer. She quickly rose through the ranks and held several leadership positions in IBM's consulting, services, and sales organizations. In 2009, she was named senior vice president and group executive for IBM's sales, marketing, and strategy division. In 2012, Ginni was appointed CEO of IBM, becoming the first woman to lead the company. She oversaw IBM's transformation from a hardware-focused company to services and software-focused company. Ginni stepped down as the CEO of IBM in April 2020 and was succeeded by Arvind Krishna. Today, she serves on the board of directors of several companies, including JPMorgan Chase and the Mayo Clinic. I recommend reading her latest book, Good Power: Leading Positive Change in Our Lives, Work, and World. Key Take Aways & Transcript: ⁠⁠https://bit.ly/TOP_Career_Aditi Follow & Subscribe: WhatsApp:⁠⁠ https://bit.ly/TOP_WA2⁠⁠ YouTube:⁠⁠ https://bit.ly/TOP_Youtube⁠⁠ LinkedIn:⁠⁠ https://bit.ly/TOP_LinkedIn⁠⁠ Twitter:⁠⁠ https://bit.ly/TOP_Twitter1⁠⁠ Instagram:⁠⁠ https://bit.ly/TOP_Insta

To those well familiar with the career milestones that typically mark the path to the CFO office, Lou Arcudi's resume at first may appear to be upside down. Or at least it could be said that the same operational projects and roles that frequently populate the tops of the resumes of aspiring CFOs are instead found at the bottom of Arcudi's. To put it another way: Arcudi acquired his operations experience early. Arcudi spent his college summers working at a General Motors chemical plant in Framingham, Mass., where he was encouraged to apply to a training program offered by the General Motors Institute of Technology (now Kettering University). The school accepted Arcudi's application, and after 6 months of training, the young recruit was offered a position at one GM's many plants.      “It was kind of like the military, where you usually get to choose your posting and specialty, so I picked the Framingham plant and manufacturing accounting and inventory control as my discipline,” recalls Arcudi, whose GM experience soon helped to advance him into a divisional controllership role at chemical company Millipore. At the time, Arcudi was responsible for consolidating the financials for two chemical plants within the United States and two others in Japan and Ireland. “The role helped me to understand what really happens out in the field—it wasn't about keeping a balance sheet but about being P&L-driven, and it became foundational for my career,” observes Arcudi, as he flags the origins of an operations mind-set that would help to propel him upward and accompany him as he served in a subsequent succession of CFO roles. –Jack Sweeney

Gary Hertzler currently lives in Mesa Arizona but grew up in St. Louis, Missouri, and attended college at the General Motors Institute of Technology earning his Bachelor of Mechanical Engineering degree in 1966. Following graduation he had a short stint with McDonnell-Douglas doing design work on the F4 Phantom. While at McDonnell-Douglas he obtained his private pilot's license. In 1967 the Vietnam war was raging on in Southeast Asia. The U.S. military draft was in effect. Gary was facing the draft or join the US Air Force. He served 4 years in the Air Force as a project officer at Kirtland AFB, in Albuquerque, NM. While in the Air Force he joined the base aero club and obtained his instrument and commercial ratings. Gary started work for Garrett AiReseach jet engine company immediately following his discharge in 1971. He hired on as a designer in the engine valve group, but quickly moved up to the TFE731 turbofan jet engine project. From there he was promoted to Project Design Manager for two different engine projects, the military TFE1042 jet engine and the CFE738 commercial engine. Gary finished his career as Master Design Manager for the 907 jet engine with the company known then as Honeywell in the year 2000. Back in 1976 after being shown an article in Popular Mechanics on the Burt Rutan VariEze his interest in building his own airplane was renewed. (He had previously started a Mustang II project that he ultimately sold.) Following completion of his VariEze in 1980 efficiency became his obsession. This included achieving two non-refueled distance worlds records as well as several wins at the CAFÉ 400 competition. Gary is the present holder of the CAFÉ Challenge award. His record is 169.3 mph achieving 48 Miles Per Gallon while hauling a 400 lb. payload flown in his VariEze, November 99 Victor Echo. Following retirement in 2000, Gary started his own business manufacturing propellers for homebuilt airplanes and has produced nearly 600 propellers primarily for canard aircraft. Besides building and flying his own airplane for the last 41 years he has accumulated over 48 hundred flight hours and has even restored a 1917 Buick touring car and a 1929 Buick coupe. --- Support this podcast: https://anchor.fm/rutancoba/support

BULLDOG DIARIES: Historian and author Tim Troupe Noonan relates the little-known story of the vital role Major Al Sobey's wife, Bess Penoyar Sobey, who was also an educator, played in shaping the early General Motors Institute.

Walter Donald "Don" Drover was born in Oklahoma and is a self proclaimed "Baby Boomer".   He studied at General Motors Institute on their 5 year engineering program and then went on to get his MBA at Indiana University.  He has many great qualities including making everyone feel welcome, liked, and appreciated.  After a long career in business and as a business owner, Don retired and kept busy.  He currently volunteers as AARP tax aid, a station captain and regular at the local Sleep in Heavenly Peace chapter, and helping those in need.  We really enjoyed the stories of camping, road trips, almost running out of gas, and more.  Listen in to learn more to Don's story. 

Benedito cursà estudis de Dret a la Universitat de Barcelona i completà els seus estudis amb la realització, als Estats Units d'Amèrica, del Curs de Gestió de Concessionaris al General Motors Institute de Michigan i el Màster en Direcció d'Àrees Funcionals i Finances de l'Escola Superior d'Administració i Direcció d'Empreses de Barcelona.[1] Benedito s'incorporà a l'empresa familiar el 1986 i el 1992 ocupà el càrrec de director general del grup d'automoció Benedito, una xarxa de concessionaris i tallers d'automòbil amb més de 50.000 clients.[1] Posteriorment la família Benedito vengué part de les empreses i des d'aleshores es dedica a la gestió del patrimoni i a l'assessorament d'empreses de capital familiar.

[vc_row][vc_column width="4/5"][vc_single_image image="2780" img_size="1000x563"][vc_column_text] With so many unknowns about the future, it's a gamble to run a business today based on what may happen tomorrow. In less than two months, the world as we know it has turned upside down, and more than ever, dealerships are scrambling to find creative ways to stay afloat. In this episode, host Dennis Wisco welcomes a leading authority on dealership management strategies, Dale Pollak, to discuss insights on the current financial landscape, and ways that businesses can reshape their sales operations by engaging with the volatile used car market during and  through the aftermath of the COVID-19 crisis. Dale Pollak is a demonstrated entrepreneur and a graduate of Indiana University's Kelley School of Business, where he earned his B.S. in Business Administration. Shortly after undergraduate school, Dale continued to DePaul University's College of Law to earn his law degree and also graduated from the General Motors Institute of Automotive Development. He is a four-time winner of the American Jurisprudence Award for top performance in his class. In 2005, Dale founded vAuto, which would later be acquired by Cox Automotive, the company that he now serves as Executive Vice President. Dale has authored multiple automotive books including his latest title, “Like I See It: Obstacles and Opportunities Shaping the Future of Retail Automotive” which was released in 2017. Tune in as Dennis and Dale discuss supply chains, the mantra that cash is king, and how businesses can redefine success in an ever-changing market to make smarter decisions that ensure their survival in a time of financial crisis. In such an unprecedented time, it is critical for dealerships and other small businesses to reflect and adapt to the business models for the mobility of people and goods. [/vc_column_text][/vc_column][vc_column width="1/5"][vc_row_inner css=".vc_custom_1551208959067{border-left-width: 2px !important;padding-right: px !important;padding-left: 30px !important;border-left-color: #d3d3d3 !important;border-left-style: solid !important;}"][vc_column_inner css=".vc_custom_1551209510648{margin-top: -100px !important;}"][vc_custom_heading text="Related Episodes" font_container="tag:h4|text_align:left|color:%23000000" use_theme_fonts="yes" css=".vc_custom_1551212450918{margin-right: 5px !important;margin-left: 5px !important;border-bottom-width: 1px !important;padding-right: 5px !important;padding-left: 5px !important;border-bottom-color: #d3d3d3 !important;border-bottom-style: solid !important;}"][vc_column_text] Re-opening the economy using location data Managing credit during an economic crisis Ridesharing has plateaued. Agree or disagree. 2020 update on why the car biz still has a future [/vc_column_text][vc_facebook css_animation="flipInX"][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column width="4/5"][vc_separator align="align_left" style="shadow" border_width="2" el_width="70"][vc_column_text] Notes  |  Resources Dale Pollak  |  Blog  |  Email Cox Automotive  |  Webinar Replay [/vc_column_text][/vc_column][vc_column width="1/5" css=".vc_custom_157979

[vc_row][vc_column width="4/5"][vc_single_image image="2780" img_size="1000x563"][vc_column_text] With so many unknowns about the future, it’s a gamble to run a business today based on what may happen tomorrow. In less than two months, the world as we know it has turned upside down, and more than ever, dealerships are scrambling to find creative ways to stay afloat. In this episode, host Dennis Wisco welcomes a leading authority on dealership management strategies, Dale Pollak, to discuss insights on the current financial landscape, and ways that businesses can reshape their sales operations by engaging with the volatile used car market during and  through the aftermath of the COVID-19 crisis. Dale Pollak is a demonstrated entrepreneur and a graduate of Indiana University’s Kelley School of Business, where he earned his B.S. in Business Administration. Shortly after undergraduate school, Dale continued to DePaul University’s College of Law to earn his law degree and also graduated from the General Motors Institute of Automotive Development. He is a four-time winner of the American Jurisprudence Award for top performance in his class. In 2005, Dale founded vAuto, which would later be acquired by Cox Automotive, the company that he now serves as Executive Vice President. Dale has authored multiple automotive books including his latest title, “Like I See It: Obstacles and Opportunities Shaping the Future of Retail Automotive” which was released in 2017. Tune in as Dennis and Dale discuss supply chains, the mantra that cash is king, and how businesses can redefine success in an ever-changing market to make smarter decisions that ensure their survival in a time of financial crisis. In such an unprecedented time, it is critical for dealerships and other small businesses to reflect and adapt to the business models for the mobility of people and goods. [/vc_column_text][/vc_column][vc_column width="1/5"][vc_row_inner css=".vc_custom_1551208959067{border-left-width: 2px !important;padding-right: px !important;padding-left: 30px !important;border-left-color: #d3d3d3 !important;border-left-style: solid !important;}"][vc_column_inner css=".vc_custom_1551209510648{margin-top: -100px !important;}"][vc_custom_heading text="Related Episodes" font_container="tag:h4|text_align:left|color:%23000000" use_theme_fonts="yes" css=".vc_custom_1551212450918{margin-right: 5px !important;margin-left: 5px !important;border-bottom-width: 1px !important;padding-right: 5px !important;padding-left: 5px !important;border-bottom-color: #d3d3d3 !important;border-bottom-style: solid !important;}"][vc_column_text] Re-opening the economy using location data Managing credit during an economic crisis Ridesharing has plateaued. Agree or disagree. 2020 update on why the car biz still has a future [/vc_column_text][vc_facebook css_animation="flipInX"][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column width="4/5"][vc_separator align="align_left" style="shadow" border_width="2" el_width="70"][vc_column_text] Notes  |  Resources Dale Pollak  |  Blog  |  Email Cox Automotive  |  Webinar Replay [/vc_column_text][/vc_column][vc_column width="1/5" css=".vc_custom_157979

On Time for Success - Business Owner Moms Edition This week we are interviewing Molly Nesham founder of PersnickeTea Company Get to know Molly Molly Nesham, co-founder of the St. Peters Chess Club, has been teaching and tutoring for more than 30 years. She graduated from General Motors Institute, which is now Kettering University, with a Bachelors of Electrical Engineering in 1981 and worked as an engineer for several years, implementing robots and automation systems and training people to use them; her accomplishments include: inventing a sprue-picker robot for unloading injection molding machines, a robot that makes Nerf balls for Parker Brothers, and an automation system to make Lunchables at Oscar Mayer. In 2006, Molly and her husband opened a Mathnasium Learning Center but left the franchise in 2008 to expand the offerings and form BEST Tutoring, helping more than 200 students to understand Math. Molly has taught high school Math and Science, Logic, Geography and Chess classes, as an advocate for the “Thomas Jefferson” model of classical education for the raising up of leaders and lifelong learners Molly started blending tea for health reasons and organized PersnickeTea to help others deal with similar issues. You can purchase her “Made in Missouri” organic and known-source tea blends at Not Jaded Boutique and Ellbee’s General Store in Wentzville, Sugar Cubed and Kickin It with Kava in St Charles, Farm Table in O’Fallon, and online atPersnickeTea.com and on our Tea Cupboard app. Currently, Molly teaches Math and Personal Finance at Trinity Christian Academy, where she is also the Chess Coach. She is also a Substitute Teacher in the Ft Zumwalt School District. She has fun teaching Tea-ography, Number Sense, and other classes for Homeschooler Link, when she is not writing, designing software, tutoring Math, inventing something, memorizing poetry, or creating new blends of tea. Her plans include hosting more tea tastings and opening a tea processing facility here in Missouri.

Dan Hollifield was born in November of 1957 at almost the same moment that Sputnik II was launched. However, the two events are not, in fact, related. His father was a machinist, graduating from the General Motors Institute in the mid 1950s. After fleeing the harsh Michigan winters for a return to the families ancestral home, Dan's father began working in Oak Ridge, TN making toys for the physicists to play with. Dan's mother is an artist specializing in oils, china painting, glass painting, and multimedia home-crafts. David Ulnar Slew Author and Editor Founder and Editor-in-Chief of Cheapjack Pulp Magazine Plank owner in the Illiterati Writer's Collective Flash Fiction Editor Emeritus of Aphelion-The Webzine of Science Fiction and Fantasy Editor at Large of Aphelion-The Webzine of Science Fiction and Fantasy  

Dan Hollifield was born in November of 1957 at almost the same moment that Sputnik II was launched. However, the two events are not, in fact, related. His father was a machinist, graduating from the General Motors Institute in the mid 1950s. After fleeing the harsh Michigan winters for a return to the families ancestral home, Dan's father began working in Oak Ridge, TN making toys for the physicists to play with. Dan's mother is an artist specializing in oils, china painting, glass painting, and multimedia home-crafts.  Few can claim the varied background of Stephanie Osborn, the Interstellar Woman of Mystery. Veteran of more than 20 years in the civilian space program, as well as various military space defense programs, she worked on numerous space shuttle flights and the International Space Station, and counts the training of astronauts on her resumé. Her space experience also includes Spacelab and ISS operations, variable star astrophysics, Martian aeolian geophysics, radiation physics, and nuclear, biological, and chemical weapons effects.Her travels have taken her to the top of Pikes Peak, across the world’s highest suspension bridge, down gold mines, in the footsteps of dinosaurs, through groves of giant Sequoias, and even to the volcanoes of the Cascade Range in the Pacific Northwest, where she was present for several phreatic eruptions of Mount St. Helens.Now retired from space work, Stephanie has trained her sights on writing   The Mystery continues.

Dan Hollifield was born in November of 1957 at almost the same moment that Sputnik II was launched. However, the two events are not, in fact, related. His father was a machinist, graduating from the General Motors Institute in the mid 1950s. After fleeing the harsh Michigan winters for a return to the families ancestral home, Dan's father began working in Oak Ridge, TN making toys for the physicists to play with. Dan's mother is an artist specializing in oils, china painting, glass painting, and multimedia home-crafts.   When the Standardized Aptitude Tests were introduced in Tennessee elementary schools, Dan was revealed to have the reading level of a tenth grade student. Unfortunately, he was only in the third grade at the time. In the fourth grade he was allowed to go to the school library for the first time, where he checked out two books. Jules Verne's Journey To The Center Of The Earth and An H G Wells Omnibus which contained five of Wells' most famous novels. One more Standardized Test occurred later that year, and fourth grader Dan had risen to a twelfth grade reading level, with a timed reading speed of 400 words per minute. However, he has never been good at any sports, other than dodgeball. Even later than that, his family moved yet again, this time to Athens, GA. He has lived in or near Athens for the remainder of his life.

My guest for podcast #175 is Mike Taubitz of the firm Sustainable Lean and FDR Safety. Mike is a retired GM employee (including a stint as Global Safety Director) and we met at the Michigan Lean Consortium conference in 2011. We quickly discovered our shared interest in Dr. Deming, Lean, and, most importantly, safety improvement. I hope you enjoy our chat about his background and lessons from his career, the integration of Lean practices and safety improvement, lessons from Paul O'Neill and other great topics. Like my dad, Mike is a graduate of the then General Motors Institute (now Kettering University). To point others to this, use the simple URL: www.leanblog.org/175. You can find links to posts related to this podcast there, as well. Please leave a comment and join the discussion about the podcast episode. For earlier episodes of the Lean Blog Podcast, visit the main Podcast page at www.leanpodcast.org, which includes information on how to subscribe via RSS or via Apple iTunes. You can also listen to streaming episodes of the podcast via Stitcher: http://landing.stitcher.com/?vurl=leanblog If you have feedback on the podcast, or any questions for me or my guests, you can email me at leanpodcast@gmail.com or you can call and leave a voicemail by calling the "Lean Line" at (817) 776-LEAN (817-776-5326) or contact me via Skype id "mgraban". Please give your location and your first name. Any comments (email or voicemail) might be used in follow ups to the podcast.