 Good afternoon and good morning, everyone online. Welcome to this John's session, John's play developed by the World Economic Forum and E-Tai Media Group. My name is Yan Qingyang. It is my great pleasure to moderate this session, Honeysing the Force Industrial Revolution. Comparable with the steam engine, electricity, internet, and the other great kind of technologies, artificial intelligence and also the force industrial revolution technology is regarded to support the economic growth and also transform the global economy fundamentally down the road. In the very recent survey, 60% of the CEO across the globe said that they believe that artificial intelligence will have a larger impact than the internet. So this leadership panel today, we will examine how industries and governments can work in partnership across the globe to unlock the potential of the artificial intelligence and also the force industrial revolution for the benefit and global common good in 2021 and also the beyond. We have a great panel today, a great combination between the government leaders and also the business leaders. We have a Ken Hu, Deputy Chairman of Huawei Technology, People's Republic of China. He's also the member of the IBC in the World Economic Forum. We have Andreas Kuzi. He is the Chief Executive Officer and also co-founder of startup and very new technology driven company, Konex Inc. And also we have Mohit Zhangxi. He is a President of the InfoCy from India. And last but not least, we have Mr. Xiaoya Qin. He is the Minister of the Ministry of Industry and Information Technology from People's Republic of China. We are very unfortunate that we have a Minister from Rwanda at last minute to draw from the session and we hope that we can have her last time. So we're going to have an interactive discussion in the first round and followed with a Q&A from our audience. So we're going to have a 45 minutes today. Okay, let's start by our interaction phase. First of all, we've talked about the AI and the fourth industrial technology as well as the difference between the fourth industrial technology, industry revolution and internet. So my first question is for Mr. Xiaoyao in order to start our discussion. We know that AI and the fourth industrial revolution, this can be have, we can use a top down approach or bottom up approach as well. So the government is playing a very important role in it. So my question is, what is this government do to in tarnishing this fourth industrial revolution? Thank you. Thank you, Ms. Moderator. Actually, we know that the fourth industrial revolution is a very important topic for recent years, an important topic for devil's economic forum. We know that the technologies of fourth industry revolution is pushing ahead, covering several disciplines and sectors. We know that a lot of technology innovations and commercial innovations are standing from this fourth industrial revolution. And we're glad to share a lot of experience with us. I guess the background of economic recession today, I believe that it is important to seize on the opportunities of this fourth industrial revolution. That's the reason why I am very interested in the topic of today. We actually attach great importance to this fourth industrial revolution. And we hope that this technology will boost the upgrade of the traditional industry as well as smart revolution. We know that President Xi Jinping and numerous other Christians mentioned that we have to follow the trend of this fourth industrial revolution. We have to seize on the tendency of informatization, digitalization, etc. We have to explore new model, new industry and to discover new driving force for the growth and new pathways for growth. So I think today it is of more important to remember his speech. So at our ministry, we have carried out a work in three respects. First of all, we have combined a new innovation system with intellectuals, researchers and industries. We are trying to transform the industry, the technology into a reality. We are building a more conducive and transparent innovation environment. Secondly, we are trying to boost the new technology sectors. We are doing breakthroughs in the new technologies. We are enhancing, for example, biology, new material as well as the new energy cars, etc. Thirdly, we are doing work to integrate new technologies and manufacturing sectors. Then we are leveraging the benefits of the digital infrastructure and to develop the manufacturing based on service. And we are also promoting 5G, industrial internet and big data, etc. And as well as their integrations with other sectors. We are also boosting upgrading of traditional sectors so that they can be more smart, more technological oriented. Moving forward, we are going to reinforce our cooperation and open up and we are going to optimize our conducive environment for innovation. We also have to work on the safety aspects and also try to avoid all risks brought about by new technologies in order to play our bigger role in this forced industrial revolution. Thank you. We talked about innovation in industries and how the digital technology can enable in every industry. And also he talked about the right balance between the risks and also the benefits and also pay a lot of attention on the global collaboration. We will discuss further in a minute. Now we have our three global industry leaders from the different regions, from China, from India and also from Europe. So I would like to think that Mr. Mohit Joshi will you share us and tell us the story from India and why artificial intelligence is different. And how can we mitigate the risk and how to support economic growth in India and also in Asia please. So thank you. Thank you for this opportunity. So in the sense of the company that's headquartered in Bangalore in India, but really we work with large enterprises across the world. So it's pretty much of a global operation. From my perspective, and I think of AI and we especially think of the impact of AI within the corporate community, right within large companies. The way we see it, it has four different levels. So the first level is that business models themselves are changing. We've seen a huge amount of transformation of enterprises themselves. And the way you see this is you see data monetization happening within companies. You see as a service models evolving, you see companies becoming almost completely digital. And you're seeing a blurring of boundaries between companies. So I think one of the impacts of AI is the fact that business models themselves are changing quite dramatically. The second thing that we see is that, you know, there is a convergence of technologies. Cloud is obviously essential from an AI perspective. Then you've got IoT, you've got robotics. So you're seeing multiple technologies come together and AI is just a label that we're applying things that we've seen across the board. So you're seeing a change in industry models. You're seeing a convergence of various technologies. You're seeing questions of ethics being raised in terms of data privacy, in terms of data security, in terms of questions about bias. And then finally, you're seeing AI being applied in the context of, you know, in the context of changing workforce dynamics. The workforce is a lot more distributed now. You have challenges of aging populations across the world. And you also have the need to have really unmanned operations in many businesses, right? So we've seen the rise of autonomous driving, for instance. So when you look at all these four things, right? When you look at the fact that industry models are changing, when you look at the fact that technologies are converging, you look at the questions of ethics and you look at the question of changing workforce dynamics. I think this really means that for enterprises, AI is really going to transform businesses. In the first phase, I think what we've seen is a significant adoption from a consumer perspective of AI. And, you know, you've seen this from a social media perspective. You've started to see this from a manufacturing perspective and from a detailed perspective. But in the second phase, we feel that enterprises are going to be adopting AI in a very significant way and changing their dynamics, you know, both from a cost perspective and from a customer perspective quite dramatically. Let me take banking as an example, for instance. Within banking, we are seeing the impact of AI first from customer interactions. So the use of chat boards, for instance, of virtual agents to interact with customers. Secondly, we've seen the impact of AI on the creation of products. So you can create products almost immediately, create products that are hyper personalized and tailored to individual customers, which you could not do in the past. And finally, we're seeing the impact of AI from a data warehousing, data gathering, models building perspective. And this has an impact on anti money laundering, for instance, of KYC. So if I should take banking as an example of an industry impacted by AI, using a change in customer interactions, seeing a change in the way data is used and consumed within the enterprise. And you're also seeing an impact in the sort of products the financial industry makes. So again, the very, very broad-based change in, we are going to see entire industries being transformed, which is why we think applied AI for enterprises is the next big opportunity. Yeah, thank you, Mr. Josh. You almost touched on every aspect of the artificial intelligence. Now we turn to Mr. Ken Hu. What is Huawei's view? Do you agree that the artificial intelligence is a bigger impact and a bigger new force compared with the internet, please? Mr. Ken Hu, Hu Zong. Sorry, we cannot hear you. Yeah, can I hear you? Yeah, yeah. Yes, please. Okay. Yeah, so thank you very much for the question. And it's a great honor to be here for this discussion. And I'm pleased to learn from Minister Xiao and Mohit for their views on the fourth industrial revolution. And actually, I shared the views from Minister Xiao that the fourth industrial revolution is actually a combination of innovation on business and technology. And Huawei has a global digital technology provider. We have done a lot of jobs with our customers in different countries, different organizations on promoting the usage of AI, Internet of Sense, 5G, and the effectual. So I would say that all those technologies are fundamental elements of the digital society. And they're all essential for the fourth industrial revolution. So many successful reference in different scenarios. So here I'd like to share some of my experience in different industries. For example, in China during the pandemic, we started at the day one, we started to work with different hospitals in China to develop some platform with AI technology. And now this AI supported platform has been deployed in cross China and also in some other countries in the world. So now, you know, with the AI support, now the doctors can build the CT scan in a much faster way before an experienced doctor stand around 12 minutes to review a single CT scan. But now it takes just two minutes. So you can see that from 12 minutes to two minutes, this is a big saving on time. But for me, it is not just the saving on time. It's the saving of life because during the pandemic, AI will help us to, will help the doctors to spend more time on the patients. And another example is in the mining industry. Last year I paid a visit to an open side of a mining company in the Mongolian China. You know, there they transform because in the mining side, yeah, we always see a lot of trucks, very, very big trucks for the mining store. And now in this company, they transform the traditional mining truck into the autonomous driving car, autonomous driving truck. So before they had to hire lots of drivers, but normally they have to hire four drivers for each truck with very high salary, which is much higher than the market average. And even in that case, it's hard for them to hire the driver they need. And now with the autonomous driving truck, they don't need to hire so many drivers such as full. And particularly from the efficiency perspective, they can enhance the speed of the truck from 10 kilometers per hour to 35 kilometers per hour. So that's a big increase in terms of efficiency. So in addition to the business benefit, this is grants to the company. You also create big social value because the condition of safety of the workforce in those mining company has been greatly improved. So we can see that, you know, lots of very good reference in different industries that we can see more business interest and social impact. You know, positive social impact generated by the AI and also other technologies. So I would say that there's a lot of room for us to work together moving forward to generate more benefits from all those emerging technology. However, as all the revolutions, we also need to pay attention to the other side. Yeah, we'll have lots of benefits. However, we also need to figure out what will be the hurdle, what will be the risk, what will be the challenges. From my perspective, obviously, there will always be some challenges in terms of technology, in terms of business model. And for business opportunities, to be honest, those challenges are quite exciting because they will bring us lots of opportunities for innovation. However, from the society perspective, I would say we need to pay more attention to the people. We need to figure out what will be the impact of those innovations to the people of course. First industrial revolution, all this emerging technology will bring us great benefits. But there will also be some disruption in many industries. As a result, many jobs and lives will be affected. And I think for us, the right strategy is to make sure that all the stakeholders, government, industry, people work together to think about how to expand opportunities during this revolution and to think about how to help every people get ready for the future. Yeah, thank you. Thank you very much. Wonderful examples from the medical area and also from the mining industry for telling us how the pandemic ignited the speed up of the artificial intelligence and this kind of new technology to support the people and save lives. Wonderful, wonderful examples. And also, you touched upon the people, a very center of the forced industrial revolution. We will discuss that later later. Now we will turn to Mr. Andrews from Z-Tel, let's some story from the CONEX. CONEX is a real force industrial revolution company combining IoT and artificial intelligence. Tell us your story, please. Thank you very much. And I cannot say anything else than I agree to what Ken Moritz and the Honorable Minister said before. I think the human should be in the center of everything. And when we apply AI and also at CONEX, the human and the input perspective has to be in the center. So what do I mean by that? There were great examples already from the mining industry but also from other industries where AI helps making jobs easier and also saved human lives. And we at CONEX, our vision is to transform railway operations for a more sustainable future. And we say for us, the human and sustainability is in the center of everything we do and we apply AI to make the jobs of workers easier. And that's because as an example, when you look at all the rail tracks and the railway is the most sustainable mode of motorized transportation because it just takes 7% of emission compared to a short distance plane. And if you look at how does the system operate, there are a lot of humans involved going out in the fields every day and night. And repairing, inspecting tracks, and this is a very risky job, so especially the inspection part of things. So it's in the top, it's one of the most top 10 dangerous jobs in the world. And we are helping them and railway companies to create a safer environment by applying IoT sensors into the field, gathering the relevant data of the infrastructure and then applying AI to tell when somebody has to go out and maintain what with which tools and how effective this maintenance was. And thereby we are helping really to reduce the number of actions that have to be in the field because you can imagine, looking at a big rail network like we have in Europe and in China, there are huge distances where people have to travel to go out just to gather the status quo to visual or manual inspections. And this is quite dangerous if rail, if trains are passing by with 100 kilometers an hour, right? You can imagine that force applied that environment and we are actually enabling them to just go out when they have to and with a concrete action. And so, and I think taking that one step back, it's really about as also the other speakers already told, it's really differentiating when applying AI between first of all, two things, right? What kind of data do I use? Do I use machine data? Or do I use personal data? For both different types of data, different rules should apply because the one is really when we say putting the human into the center of everything, we should save the human and also their privacy. But on the other side, when we look at machine data, we should really focus and leverage what we have already and creating applied, applied AI that creates impact for the humans and sustainability. And also, this is really what we as columns are committed to. Thank you. Thank you, engineers, for expanding our boundary from the human data to the machine data and how can you combine the different sets of data together and also put the people, human in the center at the same time. We can say they are very dangerous, these kind of jobs, transform these kind of jobs from the human to the machine. I think it's a very big revolutionary step. We will come back to that a little later. Now, I think that we have some time for the second round of this discussion. I would like to talk a little bit on the international collaboration also. The jobs of the different governments and also the harnessing the issue and also the governance issue. I think we have a little bit of time for the second round of the discussion. Now, second round of questions. I'd like to ask Minister Xiao. International cooperation is very important to further harness for industrial revolution. There will also be different standards. How do you look at international cooperation? Minister Xiao, could you hear me? In terms of international cooperation. I can hear you. Thank you for your question. So, what's your take for international cooperation? I think it's indeed very important in terms of AI development. International cooperation is very important. Now, there's no border for AI technologies. You cannot look at it in a very traditional way. So, for us, in terms of international cooperation of AI development, we have to work together. And this international cooperation can be within, between companies and between different stakeholders, between different regions, between different countries, of course. Of course, we have to strengthen exchanges and mutual understanding. If we can do this, we can better cooperate. Without this cooperation, it's very difficult to develop AI. We can have a dialogue mechanism because the fourth industrial revolution speaks to a very different kind of set of policies. So, coordination and cooperation are very important. Secondly, we have to cooperate in terms of new technologies because this is where AI will develop. So, each country, each stakeholder has its advantages which can be pulled together through a cooperation mechanism. Then we also have to do demonstration projects. Mr. Hu from Huawei and the representative of Connex gave a lot of examples. So, projects, cooperation, especially pilot and demonstration projects which can be used to further train talents and explore different patterns of cooperation and coordination. There are huge prospects here for China from the perspective of the government. Of course, we welcome this kind of cooperation. You are all welcome. Thank you. For emphasizing different governments and also the different role for us to collaborate very simply to harnessing and also unleashing the power and potential of the artificial intelligence and also the force industrial revolution. Now we have received a bunch of questions from our audience online and they are focusing a lot of attention on the governance issue and especially on the data issue. They are talking about the trust and the safety and the transparency of the data. So, Mr. Mr. Mogi from Joshi from the InfoSea. InfoSea is also a global company. So I think that Mr. Joshi will have a lot of insights on the governance issue, especially on the data issue. Joshi, please. So I think as far as AI is concerned, there are a lot of questions from an ethics perspective. And I think they go down to three or four issues, right? What is the issue of bias? Because we're making decisions based on existing data sets. We have to make sure that these data sets don't in themselves codify a level of bias. For instance, if my existing lending operation is biased against a certain race of people or against a certain demographic of people, then the machine learning algorithm is going to end up making the same mistakes. So I think bias is one key issue to look at, one, you're creating these algorithms. The second is the issue of explainability, right? How am I able to explain the decisions that I've made using an AI algorithm? Because at the end of the day, the decision has been made using massive sets of data and the same sort of explainability that a human actor has may not be possible. But this is one key thing that we have to keep in consideration while building these, you know, these algorithms. There is the question of control. How can we be sure that we have control over the algorithms that we've created? And so while creating an AI strategy for our clients, these are issues that we ask them to focus on, you know, the issue of bias, the issue of explainability, the issue of control. At the end of the day, we want to make sure that as far as possible, people have control over their own data that they're able to, you know, they're able to have a degree of control over their data such that if it's being used in algorithms, at least it is being driven by a level of informed consent. I think that this is a very important issue because it goes to the level of trust in AI and obviously as, you know, as a community, we want to make sure that there's a very high level of trust in what the algorithms are delivering. And having, you know, a strategy on this being able to lay out a charter or a set of guidelines is very important for companies as they embark on their initiatives. Yeah, thank you, Mr. Mohi Joshi, and you talk about the bias of the data and also control of the data and also we need some global comment charter to push forward for the utilization of the artificial intelligence. So I would like to turn to Mr. Ken Hu and also Andrews, who see and you are a real industrial practitioner using the technology, using artificial intelligence. When we talk about the data privacy, maybe we can use some new technologies like a federated kind of machine learning, we can solve the problem, but as to the bias and also to the control and safety of the artificial intelligence. Maybe it will be a little bit more difficult. So what is your view on the broad or the specific issue of the governance of the data and the governance of the artificial intelligence. Ken, please, and then Andrews. Okay, thank you. This is a very important question in the process of the forced industrial revolution because I believe that data is a key asset in this revolution. And yeah, we have to maximize the value of the data. And in order to do that, we have to make sure that more data to be shared and more value to be generated. However, in order to make people comfortable to share that data fundamentally, we have to make sure that all the data are well protected. And to this end, I believe that the cooperation is highly needed, the cooperation between public and private sectors, cooperation across different industries. From my perspective, I think government can help us to make a clear legal framework and provide clear guidance for different business community on data ownership and share. And in some of the cases, government can even help to build platforms for sharing data in a special scenario. For example, during the pandemic, many countries in many countries government to build the public share platform to share the data for pandemic response, which is greatly helping our human society dealing with this pandemic. At the same time, the business community, the companies should do their own job as well. And how we have a big emphasize on the data protection, because as I just mentioned, the better you protected the data, bigger, the bigger benefit you'll get, you'll be able to generate from the data. So, as a company, there are a lot of things we can do as well. Firstly, as a technology company, we should try our best on innovation to adopt new technologies like the reliable and trusted computing technologies to make sure that all the data are reliable and the security and particularly to make sure that none of the data will be compromised during the process of data sharing. And secondly, for any organizations, I would say that we have to make sure that we fully comply with the data protection regulation in all the countries where we operate our business. For example, in Europe, the EU has already announced the GDPR, which is a pretty useful practice. So we, as a foreign company operating there, so we have to make sure that all of our business operation will fully comply with those regulatory requirements. And certainly, as a business community, we are in the field, we are working in lots of real cases. So that will give us a chance to provide some probably very helpful inputs to the regulators to help them for a better legislation or better regulations. Thank you, Ken. A very good elaboration on the collaboration between the governments and industries and also how we can really make sure that the data is seriously protected by the regulation. Then the GDPR is a very good example. And now I would like to turn to Andrews to talk a little bit on the maybe experiences and also the people thinking in Europe. In terms of the data governance and in terms of the technology governance, Europe is a, is have a pioneering role across the globe. And some people will say that maybe the Europe's regulation is the most strict. How could we make sure that the Europe's role will be shared and also Europe, the spirit on the governance of the technology will be shared by Asia, by the United States. And how can we make sure that the global elaboration, global collaboration on this regulation will be successful. Please. Thank you very much. I think this is a great question. I think GDPR is really focusing on protecting private data of people. So also the human is again in the center of this regulation, which protects the people. So if you look at, but if you look at this distinguishment between machine and personal data, really this GDPR is all about this personal data part. And I think there it is there to protect the people's privacy, which is a great example and which people in Europe would value quite a lot. If you look at the machine data perspective, I think there, because you mentioned that before, and also can mention that before, that how can we ensure that we share data and collaborate across country lines, like trains are traveling across country lines. How can we ensure that the AI actually proves its value so that that means that we really ensure that the benefit and the experience so that the on the one hand, the explainability is there. This should be done by testing so AI has to prove itself against whatever has been there before. And I think this is a very important part and I think the more we talk about it the more clear it will get how we do this testing and how we will do the certifications in the different sectors and segments. And I think we all know that more data will enhance algorithms and more it mentioned that before. And I think that means to let AI prove itself against whatever has been there in terms of processes before in a certain application. The collaboration across country lines and with governments and with about machine data has to improve and has to get much more fluent, because that will enhance algorithms that will enhance the benefits of the applications in the different applications where the human is in the in the end the benefit of every of those so for those applications. So my view is that I think GDPR is a great example for the protection of private data. If you look at machine data, I think we have to treat that differently. And I think collaboration across country lines should be easier, because we all just benefiting from it. I think this is also where governments can help themselves and each other to really foster that understanding and then we can develop certain rules and standards, which are just for the benefit of every human. And because we're, as we said before, think I should really focus on those impact areas and that's the every speaker here said, we're human man, my mankind has actually a benefit from. Yeah, thank you and just very much. We have a lot of the questions coming in, but we are running out of the time so very quickly I would like Mr. Mohi Joshi will respond a little bit for the follow up question from the audience. They are interested in that how can we make sure that algorithm of the machine learning can really solve the problem of the bias. Can you solve that problem very quickly please. I think look, you know, you can never be sure that the issue of bias has been solved because you may get rid of one kind of bias you may get rid of a bias that comes from, let's say to my earlier example of lending by race, but you may still have the issue of lending by sex. The issue is that if you have a very clear charter from an ethics perspective of what you're looking to do by using AI technologies that charter is readily accessible to the entire organization, and that it really serves as a guide map for what you're looking to accomplish and you know issues of bias issues of privacy issues of control issues of explainability are addressed. Then I think you will have a good outcome. We work with several companies across the world. We think applied here is a huge opportunity for enterprises, but having this charter of AI ethics upfront is a useful first step to really commercialize AI for the entire enterprise. Yeah, thank you. And also, our audience are asking can Mr can who about maybe the artificial intelligence are replacing the machine are taking all of jobs and replacing the human jobs and workers so are you concerned about the unimportant issue here. Can please. Yes, I think that that's kind of the hurdle just mentioned on your adding revolution will generate some disruption in the different industries. And of course, as a result of the deployment of the AI, many jobs will be automated. And so that would be some challenge for us. However, I always believe that the challenges is also an opportunity. So what we should do is to figure out how to expand the opportunities for our existing work forces on how to. Yeah, we will have the chance with the new technology will have the chance to create more opportunities instead of just on trying to maintain the existing jobs. Yeah, that's why we have a very interesting example we can. Yeah, maybe we can create more jobs. Yeah, that's that's maybe an angle for the solution. Yeah, just a one job. Yeah, just a one job as far away. Many years ago, we started to introduce the technology into our job. And while more and more jobs in R&D and production line automated, of course, we didn't need as many as the, you know, regular workforce as we pull. However, at the same time, we hired more and more engineers for creative jobs. And yeah, as a result in the past 10 years of our work for extended year by year. Yeah, thank you. Thank you, Mr. Yeah, thank you very much. We have running off time. I'd like to invite Mr. Shell to conclude in terms of cooperation in AI. Please, Mr. Shell. Could you repeat your question? So, open cooperation in terms of AI between governments between governments and companies. How does Chinese government look at this cooperation. Thank you. We all care about cooperation. But we also have to follow rules of innovation and development. We have such a consensus at the same time rules can help us to further develop. We have to have a good set of rules so that we can drive further development of AI and we need you all to cooperate. And also the consensus from the all the practitioner and also stakeholder from all across of the roads and also from all across the globe. Thank you very much. I think our way have had a very lively and very interactive very good discussion today. Thank you very much. See you next time.