 And welcome to this, I think it will be very amazing and interesting session about the Matwares. And everybody will hear a lot about Matwares, but somebody will think, what's the interest in Matwares? It's like a very strange concept. So I think today's conversation will be very meaningful for everyone of us. As we know that we have already gone through three industry revolutions. And since 1990s, the continuous emergency of tech innovation has greatly improved the efficiency of global manufacturing and global trade. So everyone today will talk about what will be the next generation of the tech innovation. So I think today's three speakers will give us many examples for today's discussion. I will introduce today's session rules today. I hope that everyone here don't forget to use the hashtag, oh, what's the hashtag, I'm sorry. The WEF23, when you are sharing your social media, okay, and we will have two parts of this session. The first part, I will have a conversation with three outstanding speakers. And the last 10 minutes, I will give you the free Q&A time. And if you have any questions, you can handle up and we can talk more. So I can't wait to introduce our three outstanding speakers here. And the first, Mr. Professor Lee, he is from Maryland University and he is Clark Distinguished Professor and the industry AI Center Director. And Dr. Wang Rui in the middle of our seats, she is senior vice president and chair of Intel China, responsible for leading and Intel China business and teams at Intel Corporation. And Ms. Rui joined Intel in 1994 and has reached experience in leadership roles during tech innovation and industry cooperation. And she is also very outstanding female entrepreneur, I feel very respect. And Mr. Ma, he is the founder and CEO of DZ Trim and he graduated from Tsinghua University and specialized in industry 4.0. So welcome our three outstanding speakers today and let's begin our conversation. So the first question today, I want to talk about the concept of industry matters. We had many concepts this year, like industry digitalization, like digital trees, many, many concepts. So what is the industry matters? So can you explain from your own angles to tell us what is the industry matters? We come from our professor Lee. Okay. Well, if we look at the trends of our industry, right, we call the automation period, of course as a mechanization at first, automation, what's automation purpose? To do things humans don't want to do or they don't do well. Then intelligent system, control, feedback, to do things humans cannot do. For example, I want to inspect 100 holes in one second. You cannot do it. You blink your eyes and have a second pass. Intelligent systems do things humans cannot do. What is a digital system, one sentence? How to use data to make decisions, right, the three? So when you look at that, okay, USB, when we look at a cyber physical system early years, right, cyber physical system. So you design engineering system from a physics system and cyber system. So you integrate all the interconnect system into a cyber physical world. So you can model them, you can predict them. You can communicate and you can control, right, that's what it is. Now digital twin, when you have a machine to machine, how to create a digital model so the model can continue to interact with itself, like a jet engine, flying air. After 40 years, engine retire, but the model keep growing. That become a knowledge, right, that's what digital twin is, long term. Few services, life cycle. Now what is, I mean, we know the industrial metaverse, metaverse, meta is a model of a model, supermodel, right, verse, universe. But the way, sometimes people don't understand what that means. So I use another way to describe, verse could be explained like, of course, I'm not saying English, I'm saying it's a virtually engaged recognition and socially exciting, that's verse. Why is that, because it's human in there, right? So human interact with the system, virtual system could be AR, VR, but eventually you interact, you learn, but it's socially exciting. Large language model is only one piece from the language side. Large knowledge model is another piece, right? And another one is, I think there's two integrated together, and that will give a fast learning and the better precision about knowledge engineering, yeah. Yeah, okay, professor, you mentioned many about digital twin, but we had many concepts like industry, internet, industry digitalization, and now industry metaverse. So what's the difference of these different concepts? And I think Dr. Ray, Dr. Wang Ray will have many examples for us. Well, I mean, metaverse has been a pretty hot topic of discussion. Until like a few months ago, chat GPT became the harder topic, right? To me, these are, actually, industrial metaverse is really, I call it digitalization on steroids, okay? And I think one of its main characteristics is really the convergence. The convergence of virtual and reality, the convergence of machine and mind, human, right? And the convergence of digital economy and the physical economy. When those converge together, the application of the meta world, meaning the not so real world and the real world application, finds this perfect marriage of how we can solve problems in the meta world. And that is impacting the real world. Like Professor Li just eluded to. And what's the feature of these? And there's a couple of things. One, I think it's a fusion of technology. It's not one technology. When we talk about metaverse application, especially if you talk about industrial application, you will combine AI. You will combine AR, VR, you will combine 5G, digital twin. All of these methodologies and technologies together to have a meta or a imaginary twinning or imaging of the real world. And you solve the real world problem when it's hard to solve physically all locations in this meta universe. And then apply those solutions to the real world. And it's much faster, more efficient, and in many cases much safer to do, right? The second characteristic or the feature, I think it's really the fundamental compute platform. This metaverse application live off. And if people talk about AI, we talk about AI for what, 50 years? Why is it more than 50 years, right? And every wave it kind of fizzles off and dies down. Why is it this time it becomes so much more real? It's because over all these tens and years and years of our effort, the compute capability becomes substantially better and it can sustain the kind of imaginary innovation that human has put in, used to be in science fiction. Because of that computing power that we can carry out so much more, all the imaginary AI applications, all the metaverse, the mirroring the reality with a matter representation becomes supportable. And this includes the compute platform, the network connectivity, the sensing, right, the AI application, and the cloud to edge infrastructure. And this five is what we're at Intel. We call it five super technology power. And so when all of these come together, now you have a metaverse application that can really serve a lot of our human needs and industrial needs. And we'll have, I believe we'll have a chance to give examples, but I can give you many, they will say, it's hard to give examples. I said, no, it's really not hard. For example, Intel working with Gilly, this is the car company. Well, we had an intelligent platform for every car that drives out they deliver. We perform 12,000 virtual collision testing and 170k miles of road driving tests. And physically this will not be possible. But through the intelligent platform, we work with Microsoft also. We build that platform so it enables all these virtual testing that reflects the real car. And so every car goes out of the door. Combining that with the physical testing. Now you know how we ensure the quality of every car getting out of the factory. It's much, much more solid than before, right? On which human life is at stake. Olympic Games. You guys, those in China last year, we had the Winter Olympic Games here. The game was held with three years, nobody can come into China. And it's a huge venue of multiple sides of compete. And with the Olympic control center, with the media center, with the village, the athletes live in. And how do you even set up this environment? And make sure when Olympic torch starts, there's no operational failure. What do we do? We digitized, we made a digital twinning of all the stadiums, all the venues. And so the entire Olympic organization in China and in the world can come into that meta world and go do the setups, do the rehearsals. Making sure the giant slalom is running smoothly. The end goes tweeting. And all of this is done in a twinning environment. And that's industrial metaverse, right? Power grid delivery. Today, electric power becomes more and more important part of our industry. The substation of power supply is responsible for generating, transforming, transmitting the power and making sure the power grid does not have failures. Usually these are legacy platforms and they are huge and human intensive. Sometimes it's even dangerous for people to go in and fix the physical network. What do we do? We put huge computing power in there, reduce, consolidated all the workload, and use again the twinning, the metaverse, the virtual reality to really simulate the power distribution and failures and temperature sensing. And if we sense somewhere has a failure in the meta world, it will flag, say, in this location, in this substation, there is a failure there and what's the reason. And you will still have to deploy engineers to fix it, but now the engineer with the goggle on his head, he knows exactly where it is. He didn't need to physically go debug the power system with a lot of physical danger. And he can go there and his vision will tell him, the reason this thing failed is because this overheated and you're going to replace this valve and you'll fix the problem. Imagine this kind of application as we have more and more capability and it keeps evolving, right? And so our world, just like Professor said, things that human cannot do should not do or does not perform well, let's leave it to the virtual world and let us drive that virtual world with the right software, right optimization, make it more intelligent to help us solve real world problem. That's metaverse. Thank you. Dr. Wang Wei talks a lot about their insight and their examples from Intel. And I remember two key points. One is metaverse is not a single technology. It's mixed. It's like AR, VR and AI. And also the second is they will need much more and more computing power. So I think it's very expensive. Yeah. OK, from Dr. Wang Wei's side, she talked about their Intel platform and how about Mr. Ma? I know that you are doing software, right? So as your side, what do you think about the industry metaverse? Well, I think industry metaverse is kind of, I think it's a new definition and a new diagram of technology. From my perspective, we can call it next generation of so-called internet, so-called thing of digitalized platform. It does contain a lot of different technologies. For example, AI, blockchain, 3D engine, and immersive interactive hardware, and the GPU, and a lot of new technologies. Yeah, so for industry metaverse, I think back to the host's question, that we have a very good base, and we have a good existing system right now for the factory, for example, MES, ERP, and IoT system, and PLC system. So based on all of this, and also 5G, based on all of this, we can connect them together. And we can talk to each other with a different system. And upon that, we can make the data useful and usable. And we can make them to reach the person we call it. We let the right data to reach the right person at the right time with the right way. So I think, basically, industry metaverse is to make data and make existing technology more useful and more immersive and more friendly and less stressful to everyone, to not only the expert in the company, but also to every operator, every worker in the company. Everyone can use software, and everyone can use data to make their own right decision. So I think the purpose of industry metaverse is to make physical work and make the factory run more efficiently and more close to the customer. OK, thank you, Ms. Ma. And at the very beginning, I introduced three outstanding speakers, but I forgot to introduce myself. So I'm He Zhenzhao, and I'm from TMT Post Media Group. And we are based in Beijing, and we are a technology and financial business media group. And we also have Office and the English version based in New York. So after this event, you can also download TMT Post's APP, like a Chinese name, Time80. Time80's application, and you can also see all their events report about these summer hours. And so as a media people, everyone knows that the media like to talk about the problem. So I also want to talk more about the problems of their matters in history and in future. So I will give their questions to three speakers. What do you think the problems now is a very urgent time of using their matters, especially the industry matters? So what's the main problems, do you think, from professionally? I mean, technology itself cannot solve a problem. First, we have defined the purpose. In our previous session, I talked about clear. We are in a problem-rich environment. We are. Doesn't matter if you are in manufacturing or you're in service business. Always problem, problem, problems. But you run a business for the purpose. The customer is what your purpose is. And the worry-free service is your purpose. So the process is the technology where it should be. So purpose is very important. Problems are always there. But the process is how the metaverse should be growing. So one of the things that we look at the future, we say, OK, you have AR, VR. Yes, but there's a tool, a glass. You can see what you don't see. That's great. But what are you going to do? So the three things are going to happen. Number one, how do we select the right data? Generate data fast enough. I call it connection phase. I have all the sensors. Yeah, but only generate data useful. For example, and you have semiconductor. There's so many different recipes. But those are critical steps. Those are very important. Or you have a manufacturing system. The critical quality relevance, which parameter? So you have to know those. You don't want to collect all the data. To me, that's a waste of time. So I sometimes hear people say, internet connect everything. No, not really. Internet connect to the right things. That's important. So data generation. Second thing is model generation, fast enough. If you look at it most recently, the NVIDIA announced the neuroangelo. You see the iPhone scan object, quickly modeled. And the cloud AI will support that, a platform. No longer you have to buy your own GPUs. How do you build a model fast enough? So that's very, very important. Use a low cost way, but the platform driven. So data center will become a computer. Eventually will be powered AI. The third thing is make decision faster. What I mean? You can make a virtual decision, try, and virtual fail. That's very important. When you have a virtual fail, then you can make decision faster. The reason we don't make decision because we are afraid to fail. That's why delay your decision. But if you can make a decision, virtual fail fast, then you can make decision better. I think three things you got. So AR, VR, it's just a tool. They're not a purpose. Data model decision. The three things if you integrate well, that's a purpose. Good. So it's the difference between tools and the future. It's different. Yeah, but you have to integrate those tools into the right process. Cannot be fragmented. You cannot be trying error. Just like in the kitchen, you have each tool, you can cook different things. But the purpose, for example, I want to eat steamfish, steamer. I want to fry fish, fire. So customers ask for something first. Then chef cook the way they want to eat. So the tool is based on purpose. The tool itself cannot generate anything. Microwave sit there. So I think it's important to connect tool to purpose. OK. So Dr. Ray, what do you think the problems are being solved today, you think, for their industry diversity? Well, I think the problem of being solved today are continuously to be problems that we need to solve in the future, too. And so because like Professor Lee said, it's about the right data at the right time. How do we find the right solution fast? All of these will continue to evolve. And as we gain the capability of solving one thing fast, I'm sure the demand will be solving it faster. Because as the capability increases, we need to solve more complex problems. And so I think the fundamental challenge, again, is a collective innovation. I talked about the fusion of different technologies that's needed to make this solve real world problem. And so it also takes collaborative innovation, meaning at the hardware level, at the platform level, at the software optimization level, how do we democratize this whole process and every entire industry can contribute? How do we generate heterogeneous infrastructure so that you don't need to say, if solve this problem, I need this infrastructure. And if I need to do AI, I need that infrastructure. How do we platformize it so that your platform can support these computations and AI innovations in the same platform? And you can write your software optimization across a heterogeneous system, and you write it once, you can deploy it everywhere. And so these are from hardware, software, to application layer. How do we collectively drive that moving forward? Now, one company, not even at the scale of Intel, we can do it alone. We need this community and the ecosystem to work together. And it also demands an open system, because if everybody doing their own closed system, we'll never get there, right? And so we need to build it on an open ecosystem that everybody can add to it and we drive those innovations. I think that's one of the big challenges moving forward. How do we get innovation to get to where professor says, we can solve the problem fast, make the decision fast, get the right data and make the right judgment? I think the second big challenge for our industry is also about talent, right? As the world changes, when we went to college, I'm a EE major, I also studied philosophy, but we have pretty strict meet and come way, lies the way for us and fundamental things we study. But today, what do we teach our students at university so that can come out and really face the challenge of the new world, including the matterwise, right? What skills they need? How do we make sure both at the research level we continue to cultivate the best talent, but at the application layer, we have the workers that can operate in this world, right? Those education also becomes hugely important. Our next generation will live a life maybe 70% in the metal world, only 20% in reality, right? But how do we get people trained to push those technology forward? I think that's another big challenge. There are many more, but those are just two examples. Yeah, very good. I think it's a very good point that it's not only the problem of their mentors, it's also the problem of the whole industry system, right? Okay, so if Ms. Ma, do you think it will be the opportunity for their big company like Intel because it's very complicated technology? So why do you choose such an area to launch your own company? To do their startup is maybe not a very good choice for, I think, the young people? Yeah, it's a good question. So I think every technology, especially MetaVis or DigitalTrain and AI, I think that should come from startup because startup is very flexible and very quick. Stop company can try everything they think right, but which big company like Intel, they make decision very carefully and it's a big decision. But for startup, they can try every single opportunity to verify whether it's right in terms of technology and in terms of market driven and customer needs. So I think definitely this MetaVis and DigitalTrain is a very good opportunity for startups. So we choose it as my own company and now we provide this technology, this software, this service to, from my thinking, I provide this product firstly to Fortune 500 company because I think they have very good base of, as I said, data and IT system and talent and everything organized very well. So I think they prepare very well for this new technology. So I provide, you know, product, existing product and I call, you know, innovate together with them to try to together with them to find the way this technology can go to customer. Yeah, I think the second wave of this technology must be coming to the SME company which are suppliers and supply chain downstream and upstream of giant company because they set up this standard and they set up this digitalization requirement to their suppliers so we can go along with them to the SME company. I think that's the way. Okay, thank you Ms. Ma and there's another question related is that how to prepare and teach your employees, especially for the big company like Intel's, how to teach all their employees to maybe to know their metals, to know how to use their digitalization. I think it's very difficult and but it's important in future I think because people is the most important thing not owning technology. Totally agree. I'd like to say we start with sand, everything else is value add by people. I thought that's a really good quote of one of our founders. I think there's two aspect, one is within the company, how do we continue to grow our employee our technical competency as the universe is shifting so rapidly. I think that's really a general, not just a matter world, anything, right? We used to say if you're a doctor, when you're a hundred, your residual value is probably even more when you're an engineer, if you don't keep learning by the time you're 40, you're obsolete, right? So it's a fundamental gene of continued learning and continued keeping up with the new technology. And as a company, we're always number one, we have all kinds of trainings. Our employees, 87% of our 130,000 employees are technical and they come from technical background. We provide many rotations, studying training opportunities, but fundamentally it's promoting that learning attitude, right? It's when you need to solve a new problem, what skill do you need? It's not in the past, it's in what you study, what you learn and what you go invent. It's no different than if I'm a PhD student under Professor Lee doing research, who is gonna teach me other than the fundamentals? The biggest teacher is ourselves, but in a big company, if you promote that kind of culture, then your employee basically are encouraged to continue to learning. And by the way, you are measured by it too. How innovative you are when you scale your gaining, right? So that's within the company. I think I talked about just human resources in general in the ecosystem. We're also investing in education very, very deeply. I think we're also now realizing the challenges started to really investing in vocational skills, meaning you don't just need top scientists, you also need the future worker. They have to be AI trained, they have to be matter aware, right? And how do we help the local government collectively to drive those kind of education and training? In China, if you guys don't know, right? The new China educational system gets to not everybody is guaranteed to go to high school. From there, they started branching out on vocational schools. And we think that's also a great opportunity to train technology aware, occasional education so that the workers of the future can really be, they have a job, not only they have one, they're in high demand because the work has changed, right? And so I think from a human resource standpoint, we both have to be doing it in our company and helping the ecosystem. If I may add this, right? Because I work with the industry a lot. My former, I was five chairman of FastCom before, so we use a 4P approach, P. First P, called principle-based. You have the principle, right? Doesn't matter as a calculator or whatever. Data science, you have to understand what data science is. AI is basic stuff. What you can learn by yourself, I can give you a module, that's very important. Second thing is very important, it's called practice-based. We will collect a set of data, the baseline data, internally the company, so Intel have their own data. You can train your engineers. I use data, use a tool, you do repeat. You repeat, repeat. Many people repeat, so you can compare. The third, project-based. You take the tool you learn, go back to your team, formulate your own problems, right? You take the tool, formulate your own problems. Eventually, professional-based. You have to be a professional, like mass black belt, to teach other people to do one, two, three. Principle, practices, project, and professional. They're like six sigma, yeah. But that's in my book, industrial AI book. I got a version. So anyways, I think it's important. It's not just to understand the talent side, but also the system to nurture, to cultivate the skills, that's all I'm going to get. Good, so because of the time limit, I will give you the last question about the future. So from Professor Lee's Academy of Angkor and from Dr. Wang Ray's industry, and also the platform, Angkor, and also for Mr. Ma's. Maybe you are doing the application, like from the applications angle, and can you talk more about how do you think the future of the metals, about the industry metals, and also can give us some cases. I think it'll be very useful for startups to choose how to make their own company, and also for those, I think it's not only the opportunity for the big companies like Intel. So from Mr. Ma. Well, I mean, if you look industry them, they take the whole process of business, right? So you have a design, you have manufacturing processes, then you have a worker suppliers, eventually you have to provide customer service or maintenance training. So there are many spaces, right? I think that maintenance is a big field. Maintenance could be jet engine, could be anything, data center could be anything. So maintenance always has been experience-based. In the future, it's going to be evidence-based. So basically you have a metaverse support that evidence-based system, not trying error, systematic, precision maintenance is what you want, speed, precision, right, and preventive. So then you have a manufacturing, it's all the manufacturing problems, right? But you cannot do traditional trying error again. It's called, you have a transparency, traceability, right? That's what metaverse is supposed to be, predictability. And the design space, collaborations, right? Speed up a possibility without the possibilities. So eventually from design, manufacturing, from maintenance, they all have a very important, of course, most fundamentally training. How do you mean for training, right? That's very important. So I mean, just typically all business doesn't matter your medical company, your semiconductor company, aerospace company, same process, yeah. Because of the time limit we have everyone, one minute to conclude what will be the future in your side, yeah. Future is very bright. How's that? There's infinite amount of problems to solve, whether in professors listed, manufacturing, error detection, specifically to anything. Again, go back to think about what problem is tedious and hard for human to solve. How do we train machine to do that? How do we use the metaverse to do that? How do we use virtual reality to replace what it's hard for us to do and free us up to do the next generation innovation? So it's plentiful. Okay. Yeah, so I can describe this in three, eight. Four P and three A. Okay, one A is any one, another A is anywhere and the third is anytime. I think it's the future because anyone, meaning that everyone, even a school boy, they can use AR or VR device to interact with software and can enter metaverse, even industry metaverse. This is everyone, another is anytime, meaning that it's just like a smartphone. We can enter our industry, society system anywhere, anytime, even during the nighttime because metaverse is not physical one. We don't need to care about the light and the sound rise or sunset. So I think anyone, anytime, anywhere, we can enter a digital world and which is shared the verse together with physical and digital world. Okay, thank you. Three A, I remember it. Okay, the last three minutes, I will have my time to you all, to you, everyone. I will hand it over to you. Okay, goodbye, gentlemen. Okay, good afternoon, everybody. I am Oscar Antonio, representing Global Shapers, Luanda Hub, Angola. So every good technology also comes with some challenges and the more we're spending time using those technologies like social media, for example, we've been seeing some consequences, especially for young people. So I would like to ask about the implications. What could be those and how are you expecting to address the emotional and mental health implications of metaverse, either for young people or for employees? Thank you very much. I think this question, for Fresenny. Yeah, well, we can answer quickly, and then Intel and the other can answer. Well, first for employee, right, is you, when you say empowerment, what does that mean, empowerment? They have to make them excited, right? So you provide a tool for them to say, hey, we're helping you to augment your productivity, not to monitor you. The human being is a fear, right? When you feel, oh, what do you want me to do? So I think it's a more, the baseline is important. We show the baseline where we are, till the objective, the bottom line, where to be. So I give the tool to you so you can improve that productivity, right? So the small success, give them confidence. Rather say, I'm just using this whole thing. Probably say why, right? I think the evidence-based is very important. Small things, first. Small success, build a good confidence. I think that's very important. I think it's actually a really good question because I didn't tell, well, we'd say we want to drive technology for good, but not always that happens, right? And in the sense, we have very little control of how the universe uses the technology we create, any technology, right? And particularly in what we're talking about really is about, I used to say, if a family has a kid, they were forbidden ever to play any video games. When they go to college, all they do is play video games. The forbidden fruit is so strong. Instead of forbidden something that human nature wants us to discover, and it's in a way also addictive, how do we create a good experience? How do we direct them into the better spaces, right? And we always have this kind of conflict, but there is always room if we put consciousness into it and if we set the right standard, the policies, all of that has to follow. Whenever there are explosive technology growth, it has to follow with regulatory rules and with consistent social consciousness that we promote, right? And so again, it goes back to, it doesn't take just one company or one person, it takes the whole community. It truly takes the world as a village to how do we evolve into, we use the technology, these superpowers for good. But there is no guarantee, unfortunately, right? And so, but we can always do our part. Yeah, I have a point to add on to guests that we need to learn, we need to try to learn the new technology instead of avoiding of them. So for example, that AIGC right now is very hot and for our company, we have some AIGC's customer in the school because they are afraid of replacing, be replaced by AIGC because they are designers, students. But the things that they need to learn how to use AIGC to do the future work instead of afraid of replacing or replaced by them. So the thing is that we just need to learn and to fix it, that's a fact, yeah. So the time limit, we have last question, okay? This late. Okay, thank you so much for sharing the very impressive information. My name's Jin Shen, I'm from China. I also graduated from the University, so we are from the same university. I represent global shippers from the World Economic Forum. My questions were very quick. So we're talking about this combination of a different technology in industrial metaverse. So there's a one principle is talking about it's data security. So we are using artificial intelligence or data science. Data security is a very important part of it. So I wanna maybe, could you guys please share a little bit about what we can do in the future and any way we can take to protect data security in it, no matter like in industrial metaverse or industrial digitalization. Thank you. Okay, I think this question is very... So maybe I can start with, because I'm not, okay. So I think data transparency and the security is very important. One point is that I think for technology point of view, we are trying to implement blockchain technology to do this. And another thing is that we try to propose the government to make some policy regulation about the data production and also data trade policy. So that's what I want to say. Thank you. And yeah, clearly there is policy. There's also technology. So Intel is very big on security. So we built really hardware security features into our system. So that makes it reliable and transparent and secure. Secure meaning that when you don't want to expose your data, how can we enclave it so that it doesn't get exposed, right? So there's technology solution, but it has to coupled with the policy and has to coupled with the overall global standard, right? So it's a complex problem, but we need to be very conscious about it from everything we do. How do we make it secure, right? Okay, anything to add, Mr. Lee? Well, this is the long term. Yeah. So nobody has a solution short term, but the most important thing is the understand the constraints, right? Address, continuous improvement. There's no one time solution. Yep. You gotta have a continuous improvement, right? Okay, that's great. I think it's a very interesting topic and interesting conversation. And they're interesting speakers and also interesting our questions from China's friend, Angra's friend. Thank you so much. Thanks again for your coming. Okay, thank you very much. Thank you.