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Industrial automation, physics AI, synthetic brain, time series data, machine learning, robotics, material properties, quantum computing, energy consumption, neural networks, industrial innovation, storytelling, resilient business, IoT, San Francisco.

00:00

Scott, welcome to the Industrial Talk podcast with Scott MacKenzie. Scott is a passionate industry professional dedicated to transferring cutting-edge industry focused innovations and trends while highlighting the men and women who keep the world moving. So put on your hard hat, grab your work boots,

00:21

and let's go as always. Welcome to Industrial Talk. Thank you very much for joining the number one, numero uno industry related podcasts in the universe that celebrates you industry professionals all around the world. You are bold, you are brave, you dare greatly, you innovate, you collaborate. You're solving problems challenges each and every day. That's why you are the heroes in this story. On Industrial Talk, we celebrate you because you're that cool. All right, this is a paper and pencil conversation. It really is. I was just, I was eating it up. And it's one of these conversations that she's like, Okay, I'm on a roller coaster. I'm gonna strap myself in, I'm going to go for it, and I'm going to raise my arms, and I'm just going to experience this whole conversation, the gentleman in the hot seat, and I have to look over at his name, just because it's not just any name, Massimiliano maruzi. You can call him Max Xaba is the company that's X, A, B, A, it has a meaning. We talked about that, and this is a great conversation. Let's get cracking here. If I ever it's here it is. It's like golf. Here's the analogy. I'm a horrible golf player. I just dim and I, and I can't say that I'm proud or not, it doesn't matter. I'm a lousy golfer. But this is what golf does. This is how golf just sort of says, and I'm gonna suck you in, it says through that 18 holes, right? And you're up against your as a par three. Because anything you can be on a par three, I can't hit it. So you go to a par three and you've been going through sleeves of balls, you know, you're just losing these sleeves of balls, and you're getting like, is this really worth it? You just start doubting it. The grass is green, the trees are green. Everything's green. It looks great. It's fantastic, but you just keep going through it, right? So you had a par three, and then all of a sudden, you address the ball, then you put it down there, and you grab your whatever pitch, or whatever you want to grab, and you hit it. And then all of a sudden it makes it to the green. And not only to that, it comes close to the hole, and you're thinking to yourself, oh my gosh, this game is great. It sucks you in. And it says that all of a sudden you get one right out of the whole lousy effort that you put through the whole round, and then all of a sudden you get one right, and you go, I can do this. Well, here's here's Max. And I'm having this conversation and and I, you know, as well as I do, I get all excited about everything that, anything that deals with industry, I'm excited. I'm all in. I'm buying it because I like the people, I like the stories, I like the solutions I get all in. You gotta, you gotta Max. And it just reminds you, it's like that golf. It's like, this is why I'm in it. This is why I do this. This is why I want to champion industry and and get that message out people like him. Yeah, it's a great conversation. Now on to this. The here i segue. I'm segwaying, and if you're out on the video, I'm doing this arm thing. I don't know what that means, but it says segue. Anyway, I segue.

04:28

We need industry at all levels, all levels, to succeed. We just do we can't have you fail. We can't have you not achieve that dream of what you are doing and how you are helping industry succeed and how you and I call it narrowing how you're not narrowing your when I say narrowing, another analogy, right as we grow older, we. Tell ourselves. We tell ourselves, I'm just getting older. I can't do that. And from my perspective, I rail against it. That's called narrowing, and then all of a sudden, you can't do it. I can't move the lawn. I do it because I want to continue to just push the lawnmower, but I don't want to narrow. The same thing in industry. We can't have industry narrow. We have to have that continuous energy to to motivate and inspire the world that is noble, and you need to tell your story. You need to have a face to that story. That's why Industrial Talk is here. We do marketing, we do storytelling. We want you to succeed. All you have to do. All you have to do is just go out to Industrial Talk. Click, hey, connect. And there we are having a conversation. But more importantly, we want you to succeed, and therefore telling that story and, and being able to communicate that with a human face and, and it's, it's important. It just is, you want to, you want to bring in new talent. You have to tell that story, you got to show that face. You want to inspire the next generation. You want to create a business that is resilient, yeah, okay, you have to tell your story, and in that storytelling, you need to do that whole marketing, right? You just, you just do, Oh, am I on a soapbox? I blame Max. Speaking of Max, here he is. Yeah, you got it. You gotta, you gotta put some paper and pencil on this one, and I, and I guarantee you, you're gonna, you're gonna say, Hold it. Hold on, Rewind. Rewind. We Ryan, what did he say? You're gonna do that just, just foretold right there. You're just gonna do it all right. Xaba is the company. Max is the guy. Enjoy the conversation. Max. Welcome to Industrial Talk. Thank you so much for finding time in your busy schedule and just sort of display all that cool stuff behind you. It's all about you, but I get distracted by the cool stuff behind you too.

07:16

How you doing today? Great Scott. And thank you for having me and give me the opportunity to talk a little bit more about what we're doing here in my lab in San Francisco. So thank you again, Scott, that's great. It's a great day here in the Bay Area. We're doing cool technology with great weather. So what can you ask for more? Okay, so and thank you again for the opportunity for let me chat a moment about what I enjoy to do during

07:39

the day. Yeah, shut up. It's all on my side, man, because I, if there's anything that I like talking about, well, it's one technology. And you know, what's interesting, Max, is the fact that technology, and I don't, I don't have an answer to this. I being a human being, I don't know how human beings can keep up with the constant change in the technology, and it just seems like it's going faster and faster. I feel like I got a buckle in. I'm on a roller coaster, and it's all it's all exciting. Everything's exciting

08:14

is absolutely the case. Scott, is the case. Again, it's absolutely the case. Like you said. I mean, even if we're going to chat a little bit more during this podcast about really industrial automation in general, and industrial automation has been perceived, you know, for several decades, to be, let's say, more conservative market that, or industry, than a video game in reality, with AI and physics AI. That we will go a little bit more in detail today, the speed of changing the speed that machine can learn and communicate with the human and vice versa, creating this very interesting binomial that enable our brain to finally connect with the physical world is moving at light speed. And with that one, you can actually create a new material, new computational unit, new automotive new flying object, improving the quality of life and all of that. And it's happening really at light speed, simply because we are breaking the barrier that prevented us to really efficiently in the constructively and engagingly talking with, let's say the physical world that we're living in. Okay, see, it's

09:18

just it here. Here's my example. So I've been very fortunate, very fortunate to be a part of many of the digital transformation IoT conversations for many years. And there was this one time, and I was broadcasting from Barcelona, but there was this one time it was like, hey, we need to put some guardrails around whatever AI, you know. And we're talking about, hey, I can get all this data, and it's all fantastic, right? It was, it was just great conversation. Then all of a sudden, with, with chat, GPT, boom, that's it. It's like somebody flipped a switch, and it was, that's it. Let out the horses, and there they go. And it's. Us one thing after another after another, and it was just an amazing just

10:05

amazing time. Absolutely he's absolutely right. The amount of breakthrough new technology, again, new ushering into a new age that is happening in the last few years are unbelievable. Plus, you can actually feel it that we are in the world, especially with industrial automation, to imagine, let's say, shift Okay, from what was perceived as a very rigid environment, really not. And it's not an environment that is friendly with variability, okay, extremely conservative, extremely scripted. Instead is moving to an environment that is extremely capable to adapt, extremely capable to address a new dream, a new vision into that one. You definitely feel all around that we are in the verge of that one. And certainly I have to say, not when we will chat a little bit more about that, not AI in general, but more specific. What I do believe is really the true aspect of the physics AI is the one that enable this breakthrough.

11:10

Okay, yeah, and see, here's the problem Max. Just because you come to Industrial Talk with this wheelbarrow full of incredible experience. And I, me, the host, failed to ask you a little bit about your background. Before we even got it, we just sort of went off. See, I told you, I warned you that I would go off on on just these tangents. Give us for the listeners a little background on Max.

11:39

No problem. Scott, thank you for that opportunity. So starting from academic, I'm an aerospace engineer from the Polytechnic of Milan in Italy, and then I have two other master degree are in the States, one in AI, from the Northwestern University, and another one from in AI, applied to robotics from the University of Cincinnati. I spent two decades in different kind of very, let's say, challenging industrial automation project, an opportunity, starting from the Dreamliner. The reason why I moved from Italy to the US is the 787, the Dreamliner. So Boeing had an idea to make an airplane in plastic, but that one require a lot of new math, a lot of new automation, that was not available at that time. And so I was immersed into that one because of my background in mathematics, in physics, and combining that with automation. And so that one changed forever. An entire industry. There is no single aerospace industry today that is not leveraging what has been the Awesome, let's say, breakthrough, that the Dreamliner brought into that one, in terms of material, in terms of automation, in terms of scalability, and I can efficiency, and all of that. Then, you know, I had the opportunity to work for the automotive I spend a lot of time in Ferrari in Lamborghini, always applying this new material, new innovation, new business model, with a common denominator, always along the way that it was important to me to establish this communication with the physical world. Otherwise, you can have a very interesting dream. You can have a very new, interesting design, okay, but if you don't have the opportunity to transfer that on into the physical world, and for the physical world to understand what you want, it will be impossible to create that. Okay, so even in the automotive I had the same point to start to empower whatever equipment that they have done, whatever algorithm that I developed. It was important for me to establish this synthetic brain communication between, you know, the design aspect in the physical world, and then had the opportunity to spend a very interesting time with company like General Electric Lockheed Martin and so on, building incredible technology always, you know, with with the common denominator that I told you, combining state of the art of synthetic intelligence versus state of the art of material in state of the art of the automation. That's what I have done for almost two decades. I'm here today in San Francisco. This is my own company called Zaba. The focus of Zab is really to bring physics, AI, truly physics. Ai. We will talk about in a moment in industrial automation. Okay, four different aspects of that. And so I consider Zaba exactly what open AI did for language and structural model. So in Zaba, we are doing that one for for the physical world, in essence,

14:30

yeah, yeah. Well, listeners, you could tell he's got mad skills. Yeah, we've just, we've established, he's done a good job at establishing his mad street cred. All right, Xaba, I have to ask the question, sure, because we're gonna, we're gonna venture into that Xaba. Why that name? What does it mean?

14:50

Oh, that's a very interesting point. The meaning behind the name Zaba is the following. I wanted the name that express exactly the DNA, my DNA. And the DNA of the team that is working with me, which in essence, is the DNA that synthesize, I can synthesize it in this way for you, Scott, start from a vision and move it to reality. That's exactly what we're doing, okay? A very challenging visual, but move it to reality. And that's what we have done before, creating Zabba, and we're doing in zabah. The name Zabba is a Persian word, okay, is a Persian word that means imagining and dreaming, and that's why I call it in that way, because it's from dreaming and imagining and transforming that into reality. So there is a meaning by the word Zaba because it's capturing precisely the DNA of my company, the DNA of the team, the DNA of what I've done to the case before creating the company itself. So I like that man, See, I knew it had a meeting. Yeah, yeah, it's not, it's not just by chance.

15:51

All right. So here we are. I've got this form you filled out. It's all that good stuff. I need to understand the solution called X cognition, yes.

16:07

So as condition, in a short word, is really a synthetic brain. No. So I'm a huge passion beside the automation of neuroscience, okay, and the opportunity to collaborate with some neuroscientists here in the in the US, and so I architected explanation, exactly like a human is a human brain is architected. So as condition has a combination of three core layer, there is a perception layer, which is like, similar to the cortex of any human brain, the possibility for ex condition to connect with sensor, sensor that empower out my our synthetic brain has conditioned to capture the world around the machine, around the factory, whatever it may be, okay. So, so there is a layer that is, we call it perception layer, that can connect with vision system. You can connect with the temperature probe, we can connect with the accelerometer, and can connect with the load cell, you know, pressure gages any kind of sensing, like a human, or with the hands, with the acoustic, with the eye and so on. So that's the perception layer of explanation. Then there is a deep side layer, which is the back part of your brain, okay, so that one is the part in charge of understanding the capacity of the body, the muscle, the skeleton, okay, how they move, and all of that. That's, that's the part of the brain that is in charge of that, say,

17:27

number two. Again, what was that?

17:29

The number two is called the deep side of the brain, and that one is the breath of your brain, okay? And so that one is the one in charge of controlling your muscle, your skeleton, your action, in essence, you are running, you're moving, okay, that's the part of the brain that is in charge to take a task and execute that. One is like the motor is like the actuator is like the cable inside to a machine, okay, so the nervous system, the muscle, the skeleton, and so on. Then there is the core part of the brain, okay, which is the third layer of explanation in the human brain. That one is called mainly by two organoid. One is called hippocampus, which basically store all your experiences and memory. The other one is called the amygdala. The amygdala is the one that really formulate the new knowledge that the human later on, express by solving a complex problem or by taking the dream and transforming that into reality. The same is explanation. So in psychos condition, the perception layer is this interface that we build that can connect with all the different sensor and acquire and that's will be important later on in our discussion, not just pixel, because what we're building is not another augmented reality on steroid, okay, pixel and video are not enough to capture the physical world, okay, absolutely not enough. And so that's why I empower the brain to capture time series of different kind, okay, and so that's the perception layer. Then I have a deep side, deep side of the Oh, go ahead, Scott.

19:03

Just so that, because I'm going to go through these three, three elements, yeah, what I hear and correct me if I'm wrong. So here I have an asset, yeah, this automated asset, whatever it is, an asset. And on that asset, I have devices, call it IIoT. So I'm collecting data. I'm just pulling in data. I am trying to constantly evaluate and pull data on the health of that asset. That's sort of it. Is that? What I'm saying you're just like putting all this information and saying, Yep, that's what that So,

19:40

in essence, the explanation is coded and architected in that way. So what you call, what you just described, Scott, is the first layer again, which is the perception that enable the brain to capture everything is happening around it, okay, a material, a geometry, a variation in a process parameter, whatever it may be, a volt. Is an emperor, okay, whatever is the case, okay? So the brain is capturing that one because it's like a human, he has to understand what's going on in order to adapt, okay, or in order to formulate an equation. Listen, at the end of the day, I'm a mathematician, not so I love that, but at the end of the day, when you go back to people like, I don't know, Gauss, Euler, LaGrange, okay, some of these giant Okay, how did they do it? You know, even Faraday, Faraday come up with the idea of fields in mathematics, you know, because he started to do this perception of the space around him, and he noticed that something was happening there, and then it was Maxwell that transformed that one in an actual equation. Nevertheless, it was important for Faraday to understand what these data were actually telling him, and so it's been the same for Euler, for Gauss, for LaGrange, for any one of these big mathematicians, physicists, and so the brain is in power with that one. That's why I'm saying pixel and video cannot create a physics AI, okay, that's just a small portion, and you cannot teach a synthetic brain just with a pixel or with a video. No freaking way.

21:10

Okay. So here I am. Let's say I'm like an artist, right? One of the big challengers for an artist painting doing what they need to do. We talked about all the beautiful art that exists in Italy. Yes, I love all that is when to pull away, when to say, Yep, it's done. I me, Scott would say I could kill I could still get more data off of that. I can still pull more information, I can still do it, and I will probably never get to the point of being able to pull or extract the information that I need. How do I know that? Let's say I'm 99% there, and I'll just say I'm good because all that. How do you

21:58

25:17

a topic of my frustration when it deals with. I've seen it over the time. Hey, yeah, we can grab that data. Well, great, you can grab that data. Fantastic. What does it mean? Is that

25:29

nothing it means. Let's say it now. It means nothing. Okay, let me clarify this one. Yes, there are, there are some set of AI that are in charge to create these very interesting neural network that, in essence, tend to best fit, or want to call it regression, call it regression or tokenization, if you want to call an LLM or BLM, but again, that system has absolutely no cognition about what is modeling in that one is extremely stochastic. So that mean that tomorrow can give you something that has absolutely no meaning for what you ask there, because of the math, mathematics, the mathematics behind that one is extremely unexplainable, because then ultimately you have this huge sort of correlation over there that nobody can explain. And so now tell me, is that the way that the people that created the pillar of the society that we're working with. So the guy that discovered the atoms, the guys that discovered the law of electromagnetic the other one that discovered infinitesimal calculus that open up for us, incredible opportunity. Did they work in that way? The answer is simply no, okay. They did not okay, and so what they did, they actually come up with, I give you an example here, Riemann. Riemann on the hypothesis of the prime number by reading and by doing experiment, okay, in order to create the is zeta function that describe exactly all the cryptography and all the security that today, after almost two centuries, still govern all the security around the world. It was done by him with a pencil, okay? And experiment on that. And so he reversed the problem. Rather than say, I'm trying to fit a neural network, no, I come up with an equation that is very deterministic, that is very consistent, that is very scalable, that is very explainable, okay? And that's what physics AI that we're working does, is using this time series data to really deduce physical model that mean equation that are sounded okay, that are explainable, that are deterministic, that can really create an airplane that you feel safe to fly on, or car to drive around, or a nuclear reactor that you know that you're not going to explode because somebody makes a pixel somewhere, or he thought it was enough just to record the video. And then you can actually teach a machine how to do it, which is completely wrong, because the machine will never know if he's doing that. And at that point, right or wrong.

27:59

Okay, you see this is what always my, my, I stubbed my toe on this. Here's the deal. I'm collecting data, and then I sort of apply some sort of algorithm. Hey, it's starting to do this, or escalating at that, or whatever. Does it really? Does it? Does it produce the insights that I need to really improve and optimize, or is it just spitting out information precisely?

28:33

It's precisely. Scott, listen first, let me give you this very direct example that I'm assisting on a daily basis in my lab here. Okay, so about that Kool Aid. I call it the Kool Aid of the I call it the age of the pixel, and the Kool Aid of the pixel, okay, in the video, which is the following, you are empowering a robot with a vision system. No, you are mounting a camera on top of the robot, and then you are enabling the robot to go around is working space in order to collect data, like you said, and then you want to use this data to, in essence, later on, synthesize a sort of learning. Then the robot can do. There is already few huge mistake over there, okay, which are this one? The robot itself, the arm itself, okay? Is, is not an a mathematical abstraction, okay? He has elasticity, friction, okay, backlash, and you name it the physical world. So if you don't consider this one, and you're just moving around the camera, and you think that later on is just enough to do what is called hand to eye or or high to end calibration, and then you have the data, and then you can collect that one. That's a humongous overseeing a mistake, because, in reality, all the data that you are collected are contaminated by the fact that you have enough idea what is the uncertainty of that machine put in each one of the point. Now, so when you are asking later on to your neural network now, dry media robot to do that particular I don't know trajectory with the consistency that you want. For example, in my case, we are doing installation of in fabrication, of server in data center, cable connection, all of that. I can show you every day of the week, every hour of the day, every minutes of the hour, every second of of the minutes that the DLM is failing constantly, constantly is failing because it cannot understand is that's that's the point. You see Scott collecting data in that way means nothing. Okay? It means absolutely nothing because it's bogus in the first place, and you don't even understand it, that you are not in a CAD model, okay, that there is no friction, there is no elasticity. So the people perceive, oh, it's connected. The camera down, then I move it up. No, it's not like that, because the machine has uncertainty. You need to understand the physics of what you're doing, then you can do the job. And that's what we have done. And by the way, we just received a patent on that one. I couldn't believe it that we got granted a patent for an algorithm that, in essence, understand and recreate the physical principle model of robotics on the machine in general.

31:13

So So congratulations.

31:15

Congratulations. Very happy for that.

31:19

That's very cool. So here's

31:23

correct me, if I'm wrong, sure I've got this robot arm. It's right behind you. It's doing and, and, and in many cases, the data that's collected is just, hey, yeah, yeah, data, data, data, throw it through some algorithms. That's fine. Yeah. I want something. I want something I want a solution to say that's beyond static. I want something dynamic. I want something that says the arm was here, but now it needs to be here, and you're just constantly through that solution, optimizing this. Correct? Yep, yeah.

32:04

Welcome to the age. Welcome what you described there is exactly. Welcome to the age of physics. Ai, okay, so Well, at that point, the data cannot be collected static anymore. That's why it has to be a time series. Okay? Because you need to understand, in essence, what is the evolution of that particular process that you're capturing in time in a way that the model has the capacity to truly recreate the equation or the physics that describe that? And because you can evaluate as well what is derivative, is variation, which is fundamental for anybody that want to understand okay, so why that physical model is correct for that process, and now the process will evolve in time. Okay, so that's exactly the point of physics AI. And so that's exactly why physics AI is not another neural network that, in essence, says I have a dense matrix. I'm going to do some multiplication, I'm going to do some transportation of that one, and then I'm extracting some sort of data. First of all, is significantly different. The matrix is not dense anymore. Is past, mathematically speaking, okay, and so in that direction, that's what, what is fundamentally different, which will impose, as well, new algorithm. Part of that, when we are developing here, the result is going to be a set of mathematical equations, not another neural network as a result of the network actually learning that one. And the other aspect that I want to conclude is this one for you, Scott, the other aspect that is going to give you is a humongous opportunity to influence as well, the chip industry, the silicon industry, because the silicon that today we are using 99% of us, that is purely based in GPU, okay, which require the metrics to be extremely dense, okay? Because it's been built for static pixel data, in essence, okay, to actually change and go to hybridization of that chip, where the chip is not just any more GPU, but there is in here, I have to protect the moment some of the technology that we are developing, so I have to be a little bit generic. But it's hybrid chip where one component of the chip is going to be deterministic component that is capable to analyze the evolution in time that you said there, because the model will have to be inference multiple times during the execution of the task, not just a single time that is doing today is, is not enough, okay? And so, again, that's so that's even the silicon, the physics, I will change as well the silicon industry. That will be fantastic, because we're moving closer to how the brain operate, which is right now still the most efficient machine in term of energy consumption and computation. Is this one by far, okay, compared to the other one. The new generation of cheap will get closer.

34:54

Okay. Yes, you're hitting on all cylinders Max. Here's what I also and I. Just you're in San Francisco, you've got that robotic arm in San Francisco, but that robotic arm could could exist here in Louisiana. It's hot, it's a little bit different parameters. And to be able to sort of begin to really bring that all together, oh, I think that's exciting.

35:21

No, precisely the point. So there was a project, Scott, when we launched Zaba. I cannot name it that one, because the confidentiality that we signed, but the project basically was using a robotics arm with laser source in order to augment material property. Let's call it in that way. And so the point is that we observe, because the robot had to create this very interesting nano structure into the material to control or change augment their physical property, that the aspect of working in the morning with 18 Celsius versus working in the evening with 14 Celsius, the result were completely different. And so, unless that was one of the reason that put us in developing the physics in the in the first point, okay, because it was impossible with analytical mathematics and as well, you know, with the with the simple, let's say, regression, to actually control that problem.

36:19

So, see, I, I stumble, I it's, it's so fascinating. I have to ask this question. As you start to talk about the capability, the energy consumption, the ability to be able to to deliver on this x cognition, it's going to continue to evolve, right? Completely. I was reading again. I'm going to digress again. Is, is that this was fascinating to me? Same idea. Okay, you have an idea. That idea never stops evolving. It you never stop, challenging it, discussing it, it never stops. And it seems like, in your case it, it will stop. No, you are 100% correct.

37:14

That's the beauty you see is, Scott, you're attaching to the big point in the moment that we're opening this space. The reason why I chosen to apply physics AI to the industrial world, it was because, to me, it's impossible, for example, to create the next breakthrough that this society will benefit in terms of augmenting quality of life without having the physics AI in the industrial world, in as a first thing, because without that one, Scott, the machine cannot deliver, for example, a qubit, okay, the quantum computing unit, okay, at the scale that we want, because that will require, as well, a machine that can actually produce them, okay, at the scale, and at the cost that will make that revolution in that breakthrough to happening, that's one the other thing are the brain interface, for example, okay, but in order to manufacture a nanobot, or again, or brain interface, you need these kind of things. Is there is on the energy that we're consuming, which is not infinite, okay, unless we improve, again, the business model and how we're producing today. And so, because I saw all this one, my decision was very clear. I had to start first from industrial automation, applying the fact that now finally we can talk efficiently with the physical world. Because in response of that one, I'm breaking the other barrier, which is now when the machine can perform in that way, then I can start to build, you know, not only quantum unit. I can start to build a nuclear reactor as a scheme. I can start to create a building that's saving the energy and material. And I can discover a ton of different graphenes that today have been discovered only by mistake. Okay, is this? That's why I started in that way. But you are 100% correct when you have that connection with the physical world. And this kind of brain is like having, if I go back a moment to what I mentioned 20 minutes ago, is like having an entire team of Riemann, LaGrange, Gauss all around so on, on steroid. Now try to imagine what that one is going to do to the quality that we are experiencing today we cannot even imagine, but we have to remain on task here and not really fall down in the Kool Aid to say, Ah, okay, let me give you. Let me tell you that this one is a physics AI, in reality is an augmented reality where you can see your factory on the phone and your machine moving. But that one is not really saving the energy. Is not really, you know, creating a new nuclear reactor more efficiently is not enabling the qubit from happening. It can't, because mathematically speaking, we talk about the painting, no, simply the data set doesn't contain the data to describe that mathematically in the first place. And so let's stop it. I mean, we should not even. Argue that's that's not it's not correct. It's simply not the correct math.

40:05

I have to ask about quantum computing. What's your thinking? Just real quick, is it going to be one of those things where today we don't have it, but somebody's going to crack the code and and tomorrow we're going to have it, and it's just going to

40:18

well without, without any doubt, without any doubt. So I know quantum mechanics in detail, because obviously is, for example, the reason why I told you the time series Scott is not by chance. The time series is one of the big point in that is really one of the big pillar of quantum in general. Okay, so describing the qubit in the form of this probabilistic function, rather than just a zero to one value. And so most of the mathematics that I'm using is extremely parallel to what a quantum computing is. Number two, I'm a huge passion of Photonics. Okay, so I think that the photonics aspect, my opinion, my humble opinion, of the quantum computing one is the most realistic one, because it doesn't require to work at Crazy temperature or exactly out of whatever world. Okay, what we need now is exactly what I told you, like I saw in my robot with a femtosecond laser, that he was able to change the conductivity of a material. He was able to transform his property in a very, very robust, what they call deterministic, design driven meta material. Okay, so that's exactly what I'm referring to. That's what the physical work can do, that, in my opinion, will enable exactly to create the qubit that is needed with the minimum error that guarantee the computation really efficiently from happening. So are we actually going to arrive that? No doubt, I'm already discussing with some of these, this company, about what I'm talking with you. Even yesterday, I had a giant here in my lab discussing precisely about, okay, hey, we're working in quantum computing. We need to scale that one. We need, you know, that aspect to be really possible, to be scaling the first placement of something that can be done always, I don't know just where the dark matter leaves, so, you know, something more concrete. Yes, I'm big fan of that one. I'm using some of the mathematics there.

42:12

So, yeah, I'm so excited. I gotta tell you, Max I gotta tell you all, truth be told, this was a heck of a roller coaster of a conversation. I strapped myself in, I buckled my and I was just having a grand time, just enjoying this conversation. Me too, me too.

42:34

I enjoy. And you don't know how important it is, because at the end of the day, we're still exactly a small drop in the ocean. But, you know, they'd like to say in this, in that way, no, and I believe strongly on that one, an avalanche always start from a smaller rock. Okay, it doesn't start from from a tsunami, immediately start from a small rock. And so thanks to you and the opportunity of event like this one, education is the most important thing in order to make the avalanche happening. And so thank you for that. Yo, you're

43:07

Oh, that's so kind. Because that's my big purpose. That's my beef. I believe companies to succeed going forward and to create that resilient business, need to tell their story this face. Need to bring whatever they do from a solutions perspective and make it approachable.

43:26

Absolutely, Scott, because at the end of the day, I know by fact that what I told you before I can develop physics, AI, well, developing physics AI. But if, for example, if the chip industry doesn't really change accordingly, and many other ones that are building the machine. We're not creating that ecosystem. So one more time, we're going to solve one component, but without all the rest, it will remain a component. Again, the graphene nature is a great example of that. The graphene does incredible property because the hexagon of the carbon is actually complete, not because you only have one edge and the other one. No, okay, has to be complete in that ecosystem, and so we have to arrive there. Okay, number theory and symmetry in geometry are some of the two greatest science that teaches exactly how ecosystem work together and deliver incredible results.

44:24

So you're great. I got to tell you, you were absolutely spectacular. How do people get a hold of you? Max, what would be the best way they're saying? Yeah, he's speaking my language. I want to be able to reach out to him.

44:38

What's the best way? So for my LinkedIn, provide that's one, probably the best one, email the other one. And then I'm going to speak in San Jose at the IoT conference in May, around May 15 this year. And so I. That IoT conference at the IoT conference in San Jose, here in close to San Francisco, part of the Silicon Valley. So there is an IoT conference in the IoT, there is a specific Avenue dedicated to physics AI, so I'm going to speak on that one. So that would be an opportunity to engage in a discussion. Otherwise. LinkedIn profile, my main Max@zaba.ai

45:24

that's another way to reach me. We're gonna have

45:26

all your contact information out on industrial, yeah, we're, we're gonna chirp this one pretty high and mighty, because it was, it was great. His name is Max, Massimiliano Moruzzi, and it's, it's multi syllable right there. I see it right there. Outstanding. Thank you. Thank you so much. Really appreciate this conversation. All right, listeners, we're gonna wrap it up on the other side. We're gonna have all the contact information form Max out on Industrial Talk. Yes, this is a must connect. Stay tuned. We will be right back.

46:00

You're listening to the Industrial Talk Podcast Network.

46:12

Come on. Was that an incredible conversation? There's a lot of energy going there. Xaba brought to you by Max. You again, out to Industrial Talk. It's easy peasy. Find Max's conversation. You've listened to it because you need to. You need to listen to it. Just do so you go out there and on his little, you know, landing page right there, saying, Hey, here's Max. You need to say, hey, I want to connect. And there it is. There's no friction to connect with Max. And I gotta tell you, you're gonna be better off. You're just gonna be better off. You're gonna look at the world differently, because Max does that. As you can tell through the conversation, I was like, I was having a grand time. He was touching on pretty much everything, probably, probably not the most organized conversation, but it was fun, and it just continues to reinforce why industry is so incredible, and that's why we again celebrate you because of Max I he could use this for a bumper sticker. I blame Max I do. I got all giddy about that one. Yeah, that was a great conversation. Here's an analogy when we were talking about it, and, of course, another analogy so when we were talking about what Xaba was doing, and that deep dive, and that just innovative thinking, that amazing stuff, it reminds me, and I mentioned it in is like, ideas never stop. Ideas never so you come up with an idea and it it just continues to evolve, grow and and so I always use the analogy of the Mississippi River. When an idea happens, it's up at the top of the United States and nothing big. It's just like, yeah, it's up there. No big deal. But as time goes on and the tributaries feed that Mississippi River and becomes deeper and wider and and more more forceful, that's an idea. That's what, that's what Xaba is doing. They're taking that idea and they're just making it richer and deeper and more relevant. I'm excited, yeah, I'm excited again. You need to tell your story again. You need to tell your story. You need to, you need to get on a podcast, and you need to get on Industrial Talk, and we need to work together to be able to tell that story. You know why? Because it's a content generating machine. And you need to develop more content, not just old, stodgy content. You need to have a dynamic content creating machine. That's what, that's what Industrial Talk is all about. So go out to Industrial Talk. Connect with me. Let's have a conversation. Let's make you a success. Be bold, be brave. Derek, greatly. Hang out with Max. You're gonna change the world. We're gonna have another great conversation shortly. So stay tuned.