a guest from Land O\u2019Lakes on a prior episode<\/a>. They\u2019re sharing information with farmers. Farmers build things, they have a lot of data about their crops but how they share that data \u2014 I feel like there\u2019s a lot of that going on where we are recognizing that idiosyncratic data is more valuable when combined with other data. At the same time, I\u2019m not naive. People don\u2019t want to share stuff. How do you encourage people to do this? <\/p>\nPeter Koerte:<\/strong> There\u2019s a simple \u2014 not an easy but a simple \u2014 answer to this, and that is the value. So if I\u2019m not able to translate that and say, \u201cYou know what, share the data with me, and then thereby you\u2019re going to improve your availability of the train to stay there, or I [will] improve the efficiency of your building,\u201d then they will not share the data. It\u2019s as simple as that. But if you do, then that\u2019s great. Then they say that\u2019s fine. <\/p>\nSometimes it\u2019s built into your solution. It\u2019s built into the contract where they say, \u201cWell, we don\u2019t care. It\u2019s fine; you can just use it.\u201d Others are saying, \u201cHey, I want to also have a negotiated discount,\u201d which is also possible. But the simple answer is you only share your data if you get some value in return. So that\u2019s a little bit like the model. Depending on the industry, it\u2019s slightly different in terms of the kind of value we\u2019re creating, but still there\u2019s some value in return. <\/p>\n
Sam Ransbotham:<\/strong> You\u2019re describing largely a partnership but sort of between customers or with customers, but you\u2019ve also done some recent connections with industry, like your partnership with Nvidia. Can you describe what you\u2019re thinking there? I think the goal there is an industrial operating system. How does that work? What\u2019s the plan there? What\u2019s the thinking? <\/p>\nPeter Koerte:<\/strong> With Nvidia we have a very, very close relationship for many reasons. One, of course, is you lose a lot of GPUs in order to train some of our models. Second, [for] tools that we\u2019re providing today, Siemens is the leader in industrial software. So we [have] about 10 billion euros of digital sales. People forget about that. We\u2019re among the top 20 software companies in the world, so we have a lot of simulation software, where you can simulate cars, trains, rockets in the digital world. <\/p>\nOf course, all these simulations take an awful long time when you think about computational fluid dynamics, which is very complex. But [it] turns out you really can accelerate them. So what we\u2019re doing together with Nvidia is to say, \u201cWhat if instead of waiting eight hours for a complex computational fluid dynamic simulation, let\u2019s say, of the air drag on a car \u2014 we can reduce that to minutes?\u201d And that\u2019s exactly what we\u2019re looking at. <\/p>\n
So it\u2019s accelerating simulation, accelerating design, when it comes to chip design, which is really interesting as we get to lower nanometers \u2014 two nanometers and less \u2014 the complexity of verifying those chip designs is enormous. It exponentially really rises. So instead of having human engineers going through every circuit and really testing it to every gate array, actually you can start to have an AI go through this and do this over and over and over again. So the chip design verification is one. <\/p>\n
Then, lastly, the design transfer to manufacturing is a key issue because these really hold you up in how fast you can get these chips out there. There again, as you are the designer, we can have the AI in the background verify whether what you\u2019ve designed is correct and whether it can be manufactured. <\/p>\n
These are examples that we have announced also at [the Consumer Electronics Show] earlier this year with Nvidia. We are really excited about [them] because we think we can further accelerate<\/em> \u2014 and this is always the keyword: acceleration of design, acceleration of manufacturing, acceleration of operations. That\u2019s why we are so excited about it.<\/p>\nSam Ransbotham:<\/strong> I get the appeal of switching eight months to eight minutes. It doesn\u2019t take much quantification; we can do that in Fahrenheit or in Celsius, either way that works. But the other thing it makes me think about is that you probably have a lot of processes designed around the idea that it was going to take eight months to do that. And when it takes eight minutes, it feels like, sure, it compresses it, but it also might change the types of things you do, the order that you do them in. It seems like it could just have this ripple of upheaval. How do you manage that? Or maybe am I extrapolating too much? It feels like it could be a mess. <\/p>\nPeter Koerte:<\/strong> That is very true. That\u2019s why I tend to say, always, AI is about 20% technology and 80% is actually transformation. What that means is, we talked a lot about data, that\u2019s one thing, but then it is really changing the processes of how you do things. And, usually, what the AI is now doing is it really changes workflows. So instead of thinking sequentially, where I do one task, let\u2019s say I do the design. The next one is doing the verification. Then the next one is looking at how do I design to transfer, and transfer it to manufacturing. It\u2019s very sequential.<\/p>\nNow what if you could do this all in one step because the AI is doing it? Obviously you\u2019re disrupting a very well-established workflow process. The first question that comes is, who is doing this? Is that the designer from the very end [or] from the beginning? Is it somebody completely else? Who\u2019s the persona that you\u2019re actually talking to? Some very interesting questions. <\/p>\n
Second, how is that process then going to go? And who is verifying that whatever the AI is doing is really correct? Then a third question is, where do I do this? Where is the AI sitting? Is that a new application? Is that embedded into an existing application? Is it talking to all applications? All of these interesting questions arise, and they are not usually all technical. Very often, we find this is very much about the people [who] use it every day and involve them, and then start to think \u2014 rethink \u2014 what wasn\u2019t possible before and thereby addressing also some anxieties, because many would then argue, \u201cThe AI is going to take my job away.\u201d So then you have a lot of resistance. Then all of a sudden a technology conversation becomes a cultural-change transformation conversation. We find this time and again. <\/p>\n
Sam Ransbotham:<\/strong> Now, the natural follow-up is free for me to ask about workflow and these types of issues. They\u2019re all important, and I don\u2019t want to discount those or whatever, but you\u2019re pretty fired up about smart glasses and workers wearing smart glasses. What\u2019s next for them? How do you see them in the industrial world? <\/p>\nPeter Koerte:<\/strong> I\u2019m very excited about smart glasses. If you think about, in particular, U.S. manufacturing: I just spoke to a major new electrical vehicle car manufacturer, and they told me in their manufacturing, their churn rate \u2014 so the attrition of their blue-collar workers \u2014 is 35%. What that means is you constantly have to retrain your employees. And it\u2019s not just retraining them, but also the other question is, \u201cHow do you capture that knowledge?\u201d What if you can take your glasses, you have that camera, and, let\u2019s say, you are a specialist in operations and you are a maintenance engineer for a specific machine. <\/p>\nThat camera and that AI is [looking over] your shoulders, literally, and really checking off what you\u2019re doing. Maybe you\u2019re narrating it even. You record this. You do this over and over again, thereby, you\u2019re democratizing that knowledge, actually. You can capture this for future people coming in. But even better than for the new worker, working the night shift, 2 a.m. in the morning, a machine breaks down, usually people are just tinkering around having no idea. But what if you had those glasses on now, and those glasses are saying, \u201cThis is a CNC machine. Usually the failure code of E345 means actually it is a Jam 2. Check that lid and open this one, two, three, four, five,\u201d and off you are. How amazing is that? <\/p>\n
I really think in terms of the keyword augmentation<\/em>. So augmenting the workers, the blue-collar workers, but also white-collar workers on the shop floor and, of course, capturing that knowledge as they are exiting. Isn\u2019t that amazing? I think it\u2019s going to make us all much more productive and much more enjoyable, because you get faster time to results, and thereby you get the factory running, and so on and so on. And you reduce a lot of anxiety and fear, because very often people don\u2019t know what to do. Now all of a sudden they have a companion. They have a copilot, colleague, whatever you want to call it, that helps them, and that is there for them 24-7, as opposed to calling somebody who\u2019s probably somewhere home and sleeping. <\/p>\nSam Ransbotham:<\/strong> That makes a lot of sense. I want to draw a little contrast though. Earlier we were talking about data, and you were talking about a need for deep expertise and deep domain knowledge. But it sounds like this is maybe a push against, or you\u2019re not needing to know that the E345 error code means this, that, or the other. Is it deeper? Is it more specialized? Those seem in conflict to me in some ways.<\/p>\nPeter Koerte:<\/strong> Obviously, we need both. But, actually, the example is pretty comparable if you think of it. So yes, I can tell you the door is going to break down, and this is now preventative maintenance. The other case was more as a reaction. But in both cases it\u2019s maintenance. So the preventive maintenance means that still a worker has to go out there and replace the motor. Now, on the other hand, in our case here, it\u2019s the same thing. It just gives you the intelligence of what to do. And the doing itself still has to be done by somebody who\u2019s operating that machine. So I think it\u2019s pretty comparable. <\/p>\nThe interesting thing about this is because it still requires humans, could we at some point automate that through the whole conversation about robotics and humanoids and everything? This is certainly then also a big push right now that we\u2019re seeing in the market. Whether this is going to come soon or not, we don\u2019t know, but for sure we\u2019re missing at least 2 million people in the workforce in the United States already today \u2026 on the shop floor. So the only way to stay productive is by automation. This is where Siemens helps many companies to automate their processes in the factories. <\/p>\n
Sam Ransbotham:<\/strong> Maybe I\u2019m reading too much into it, but I read something you\u2019d written about humanoid robots and some skepticism about the actual humanoid shape, and you were kind of hinting at that right there. For one, I\u2019m totally with you. The human shape is not anything magical, and there are a lot better shapes for industrial machinery in particular. Are things going to look like humans, or are they going to look like machines, or different? <\/p>\nPeter Koerte:<\/strong> Well, that\u2019s the big debate. To be honest, it\u2019s too early to tell. I\u2019ve seen both. As a matter of fact, today I just had two conversations of that sort. One of them [was] going in the direction of we need to have humanoids, the other one [was] saying \u201cNo, no, no.\u201d I think in the end it comes down to the ROI and the value, again, that we\u2019re creating. <\/p>\nLet\u2019s take a very simple example. Let\u2019s say material handling is a big one in a factory. You have to always make sure that there\u2019s an ample supply of material. Let\u2019s say, in particular, if you\u2019re in a stamping plant, it\u2019s metal sheets, and so it\u2019s heavy. Taking a humanoid is probably not a good idea, although there [are] use cases; I\u2019ve seen them. And there [are] many reasons. One, the payload is very, very, very limited. Number two, humanoids are quite slow if you look at them, at least today. The question is, can you accelerate them? But today they are slow. And then lastly, up to 30% of the energy consumed in a humanoid is just to make sure that you\u2019re standing upright. What if you actually had different form factors that would give you higher payload, faster speed, less energy consumed, and then it becomes an ROI conversation? It depends. It\u2019s very hard to generalize. <\/p>\n
In this case, though, I almost would bet probably a different form factor to a humanoid is a better one. But there [are] others where you could argue a humanoid could do a better job, for example, wiring harnesses, clipping them together, where you need to learn dexterity and versatility and all of it. Maybe, but that\u2019s exactly why it\u2019s a fascinating field. I think anybody who claims [to] know it, I think it\u2019s too premature, but it\u2019s a fascinating field. <\/p>\n
Sam Ransbotham:<\/strong> Actually, I like that because I think so many things are increasingly \u201cit depends,\u201d because we don\u2019t have these one-size-fits-all models that are going to work. And you know that defeats our ability to make some sort of prognostications here. <\/p>\nThanks for taking the time to talk with us and sharing your insights about industrial AI, which is probably a different idea for some people, and also data sharing in the future of work. And listeners, thanks for joining us on Yo, yo mismo y la IA<\/cite>. <\/p>\nPeter Koerte:<\/strong> Thank you, Sam. It was great. <\/p>\nSam Ransbotham:<\/strong> Thanks again for listening today. Next time, Vineet Khosla, CTO at The Washington Post<\/cite> joins us for a conversation about AI innovation in publishing. Please join us then.<\/p>\nAllison Ryder:<\/strong> Gracias por escuchar Yo, yo mismo y la IA<\/cite>. Nuestro programa puede continuar, en gran parte, gracias al apoyo de los oyentes. Sus transmisiones y descargas marcan una gran diferencia. Si tienes un momento, puedes dejarnos una rese\u00f1a en Apple Podcasts o una valoraci\u00f3n en Spotify. Y comparte nuestro programa con otras personas que creas que pueden encontrarlo interesante y \u00fatil.<\/p>\n\n<\/aside>\n\n<\/aside>\n\n ME, MYSELF, AND AI\u00ae<\/sup> is a federally registered trademark of Massachusetts Institute of Technology. All rights reserved.\n <\/p>\n