Understanding the intersection of AI and simulation – Part 3 Transcript
In the final episode of this special miniseries, host Spencer Acain is joined by Todd Tuthill, Vice President for Aerospace and Defense and Marine Industry at Siemens, Dr. Justin Hodges, Senior AI/ML Technical Specialist at Siemens as well as Fatma Kocer-Poyraz, Vice President of Engineering Data Science at Altair to look both forward and backward at how far we’ve come with AI, simulation and digital transformation, and how far we still have to go.
Check out the full episode here or keep reading for a transcript of that conversation.
Spencer: Hello, welcome to the AI Spectrum podcast. I’m your host, Spencer Acain. In this special episode, we are joined by AI experts from Siemens and Altair to look at the impact that artificial intelligence will have on some world of simulation going forward. Today I’m joined by Todd Tuthell, Vice President of Aerospace and Defense and Marine Industries at Siemens. Justin Hodges, Senior AI/ML Technical Specialist at Siemens and Fatma Kocer, Vice President of Engineering, Data Science at Altair. I think we’ve looked a lot at different ways AI and simulation work together now and both what’s here today and where it’s going into the future. But I think I’d like to take a step back from here and see how this fits into the broader context of the digital transformation. I know we’ve touched on this a little bit already, but Todd, could you just tell us a bit more about how this is all going to slot into the wider world of design simulation, digital transformation?
Todd: Sure. When I think about digital transformation, someone asked, “Okay, haven’t we been doing digital transformation for 20 or 30 years and aren’t we done?” Well, yes, we’ve been doing digital transformation. I’ve been involved in digital transformation aerospace now for decades. I like to talk about not just digital transformation, but digital transformation maturity. We have a framework that you can look up online. I won’t go into the framework now, but I’ll just say that we think at Siemens, we think of digital transformation as a maturity journey. It’s not a place that I’m going to get to next Friday. I’m going to be done and I can stop thinking about it because the reality is your business changes, your business needs change, what your customers are asking your products to do change, geopolitical things happen, and the tools available to you are constantly changing. They’re constantly getting better. That’s why I encourage customers to think about their digital transformation as a maturity journey. As we’ve talked about a lot today, one of the tools in the toolbox as Fatma said, I’ll quote her again, is AI/ML. It’s revolutionized a lot of industries. There are other less technical industries. We can think about that it was really revolutionized first. Now we’re getting into the more complex industries of putting tools and industrial things together. It is revolutionizing and changing our industries. We’re just the cutting edge of it. When I think about digital transformation maturity, we talk specifically in this podcast about simulation. That was the purpose here, but if I think about a broader context, I think of some of the problems facing digital and industrial industries right now. One of them is workforce. Finding people, finding engineers, and people who want to develop and build the tools that the products we build. We want to build them faster. We want to do them more efficiently. If you look around, as I was in aerospace, and I know some of many people who are in this podcast probably were in aerospace too, probably still are, you look at what a lot of the engineers spend their time doing. I know when I was doing practicing engineering, a lot of it was pretty mundane stuff. I wasn’t sitting there in a cubicle thinking big broad thoughts and making big decisions. I was making products and developing products. Many times, that requires a bunch of mundane tasks, a bunch of things. What I’m seeing AI, generative AI, and other types of AI agents do now is to really transform some of that mundane work and take the mundane work off the plates of the human engineers. Human engineers can focus on the more interesting parts of engineering. I see it really making our jobs more interesting. I see it multiplying the impact of engineers. I say a lot. I say, when a lot of us studied engineering a long time ago, we were focused, I think, especially in our undergrad on the how. How do I do the equations? How do I solve this problem? How do I do this? Even in our early careers in engineering, how do I develop this model? I really think what AI/ML is doing in the world of engineering digital transformation is, as engineers, we’re going to focus more on the why. Why do we do it and what are we going to do? The agents and the AIs are going to take more of the how. That’s really one of the big changes I see happening in digital transformation that I’m so excited about because it can really help address time to market, help companies go faster, and address the workforce issues that they’re having now. When it’s applied in augmented ways across the whole portfolio, and that’s one of the big focuses we have here at Siemens is, how can we take those foundation models we talked about, the machine learning agronomies we talked about, and really apply that in really intelligent ways across our portfolio to help customers focus on those things. That’s a bit of what I see coming and how I see it relating to digital transformation maturity in many industries.
Fatma: I wouldn’t consider myself a digital transformation expert, but I think through digital transformation by keeping track of the entire product lifecycle of a product from the ideation phase, to the detailed design, to the manufacturing, to its operations, and by collecting that data and using that data to make better design decisions for future products or for the existing product actually, maybe adjusting the settings, adjusting the operations. It allows us to find the patterns, relations in these complex systems that we can no longer really find with just using our common sense. We can validate those findings from the data with our engineering knowledge and expertise, but products are so complex now. Manufacturing is so complex. These processes are so complex that just using our expertise, we are not being able to capture all the relations, all the patterns in the product lifecycle. I think that’s what digital transformation helps us with. It allows us to extract data and use that data to understand better and make better decisions.
Todd: Yeah, Fatma, I completely agree and resonate with the things you said. It helps, it makes everything we do better. I think about the things we offer customers, the products in digital transformation. I don’t know, certainly today, I would say that artificial intelligence and generative AI, machine learning, that’s not a product. Our product is digital transformation and simulation and design and PLM. It’s not AI. What AI is, AI makes every single one of the products we sell better. That’s how we’re applying AI.
Spencer: But I think with that, we are just about out of time here. I’d like to ask you all if there’s any last thoughts you have here, final questions, or comments, or just things you’d like to share with us before we wrap this up. Todd, why don’t you take it away?
Todd: Sure, Spencer. I’m going to leave our listeners with a thought experiment. We’re going to talk about science fiction becoming science fact and when that happens. I’ll date myself. I was in college studying engineering in 1986 when the movie Star Trek IV: The Voyage Home came out. There’s a key scene in that thing where the Star Trek Enterprise comes back to current times and Scottie has to design something. He walks over to a computer, he picks up a mouse, and he says, “Computer,” and he tells it to do something. He’s so frustrated because the computer can’t listen to him. It doesn’t have a clue what he’s saying. I distinctly remember setting in the movie theater thinking, “In my lifetime, will I ever be able to speak to my computer and it’ll understand me and go off and do things?” That’s come true. It’s true in all of our lives. I mean, I talk to my iPhone every day and that capability with the kind of foundation models we’re talking about here just keeps growing and growing. That exists. There’s another key piece of Star Trek technology specifically in the TV episodes that comes later in the holodeck. There’s a scene in the holodeck that I still go back to where they’re designing a starship. They’re designing a spacecraft with their voice. I’m thinking, “When will that be?” In the series, it was about the 24th century. I don’t think it’s going to be the 24th century before we have a real live holodeck. We can speak things and see them in VR, AR, and they’ll appear in front of us. All of these tools will be connected the analysis and the design. I think that’s in the future. My thought experiment is, think about when that is. Will that be in my lifetime? Will that be in my grandchildren’s lifetime? But I think that’s something that’s what we’re all working towards trying to create this world of what I’ll call engineering in a holodeck. If I think about the optimized way I want to do engineering, the way I want these tools to work, how I want AIML to be augmented on top of all of the physics-based stuff that we do, I guess that’s the way I describe it. I want to design the holodeck. That’s my thought experiment. Leave it for the listeners to think about when will that be because I can tell you the 20-year-old kid in 1986 who was sitting in that theater never thought he’d ever talk to his computer and might do it every day. We talked about how fast AI/ML is advancing. It’s going to be here soon.
Spencer: I’d like to thank Todd for that perspective on how far we’ve come and how far we still have to go. But Justin, Fatma, do you have any final comments?
Fatma: Maybe I’ll chime in. Don’t wait until you think you have enough data to start machine learning because you have to learn what to expect from it to understand what enough data means. But at the same time, don’t wait until you have what you think is the best machine learning process to establish your data strategy, start your data strategy and data discipline right now.
Justin: I’ll piggyback off that in a different angle. Same goes to those that want to learn a new skill, an aerodynamicist, a thermal engineer, a mechanical engineer wanting to learn data science and machine learning. Don’t wait to learn things or do a probably typical engineering behavior of analyzing every way to learn and every book and every resource and then picking the very best one. There’s value in starting off with whatever is similar to the projects you’re working on or whatever is interesting to you. Don’t worry too much about picking the exact perfect way to learn. Just start jumping in and getting your hands dirty. At least I’m a big fan of reading the literature as well. I have a on a personal note fun challenge to read a thousand papers this year on CIML. I am obviously a huge advocate of looking at literature, at least the meta studies and the seminal works that kind of indicate trends and things like that just to keep in touch. You don’t have to read everything but don’t wait to start learning. I think numerical scheme solving PDEs is harder than matrix multiplication.
Spencer: On those inspiring words we are out of time for real this time so once again I’ve been your host Spencer Acain on the AI Spectrum podcast. Tune in again next time to keep learning about the exciting world of AI.
Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation.
