 what kind of the project and so then I'm looking forward to I think to get a very interesting information. Please, floor is yours. Thank you very much, thank you. Please, please, please have some sympathy for me because I have to follow Larry Prusek and it's lunch next. So, you know, have some sympathy for me. Yesterday I talked about a methodology, a rigorous, pragmatic, measurable, funded by the European Commission, many, many examples of successful implementation. Tomorrow I'm going to talk about ISO Global KM Standard which we've been working on with a committee of experts to try to get to global consensus. But today I've been asked to talk a bit more about the results and I was thinking a bit about this, you know, and first of all I was going to talk about the World Bank but the World Bank is a big case study so that's fine but I worked in Ethiopia with the World Bank and yesterday I used the terms knowledge driven and knowledge based. If you really consider knowledge as the key asset you are very knowledge driven in what you do and the World Bank shifted to that from a perception that they were a financial bank to a knowledge bank, a knowledge driven bank. It's more important than money. I'll let you look at the case study for that. So then what I thought, what should I talk about as a result? And I'm passionate about flying an aircraft and for 25 years I was a private pilot so I was delighted when I was invited by Airbus to implement this methodology. I was like a little boy in a sweet shop, you know, the big Airbus 380s in Toulouse and I was working with the Chief Knowledge Officer and he told me that Airbus have been doing knowledge management for 15 years. Very pragmatic, very scientific, very measurable and each year they were creating more knowledge about different domains. Wing safety, safety like you is number one for Airbus. At each year they would collect this knowledge, this domain knowledge and they were getting more and more valuable perceptions about knowledge to Airbus. They weren't just an aircraft manufacturer and designer and builder and then one day they got the shift and he said to me as we were looking at the Airbus A380 and how they put it all together and all the knowledge and he said to me we can make far more knowledge, far more profit selling our knowledge of aerospace to China than we ever will making and building aircraft. They suddenly became knowledge driven, continually improving their operational knowledge is why they could suddenly realize that knowledge was a key asset for them, they could even sell it. But I'm not going to talk about Airbus because I want to be a bit more challenging if I dare. I thought I know I'll talk about Shell because I have been to many conferences that Shell talk a lot about their implementation which is very very successful and the interesting thing just to mention here is that they used to say they were in the business of oil exploration, oil refining and oil distribution and they don't say that anymore. They say we're in the business of gaining the best knowledge of oil exploration. Who actually does the exploration is a secondary decision. They may even subcontract it. The most important thing is having the best knowledge, it's having the recipe that's important. But I'm not going to talk about that and I thought I'll talk about Toyota because they also are rather like the nuclear industry as well. They're all interested in designing, building, operating and your decommissioning of course. And again they said exactly the same thing. They'd made the paradigm shift to be knowledge driven. They realized that who designs and who manufactures and who serves comes next once you have the best knowledge. And so that's what I thought I was going to talk about. But when I was here and I was listening we're talking a lot quite rightly about all the different aspects of effective knowledge management. Not least people as the key asset. And people and culture. And there's lots of debate about culture. You know you can argue that you can change a culture or as Ashok said it can take five years. Or you can argue that culture is just an outcome of leadership and structure and strategies. And technologies. And it occurred to me that maybe I should talk a little bit about and challenge us about the latest technologies. Recognizing people, process and technology. How many of you here are familiar with industry 4.0? The fourth industrial revolution. Which is not very many. Well I thought at least I could make you aware of what this is. And these new technologies. Now you may be horrified, you may be delighted, you may agree with me, you may disagree with me. I'm going to challenge you. And challenge myself. Will machine intelligence have any implications on the future of knowledge management? Most people will say no. We've had a lot of intellectual knowledge here. What's your intuition? What about intuitive knowledge? You know I go to knowledge management conferences regularly. That's how I learn. And one of the conferences I go to each year is KMASHA. Which is normally held in Singapore or Hong Kong. You know last year in November in Hong Kong the keynote was somebody talking about artificial intelligence. All the KM, I was horrified. Horrified. Because all the KM practitioners went into defensive mode. KM practitioners are telling you how to change. Yet suddenly he said to these people, can machines make decisions better than humans? Will machines replace humans? How will machines work with humans? What are the implications on knowledge management? Everybody said no. Never happened. Never. Well, I'm going to briefly present the book that was published by Klaus Schwab called The Fourth Industrial Revolution. Just to make you aware and then you can tell us afterwards during the conference if you think this will have an implication. I'm going to say to you that I think in five years time we'll be talking about knowledge management in a completely different way because people will be working with intelligent machines in a completely different way in the way we manage our knowledge. But let me start with my favorite Drucker. What he says about this. Peter Drucker said this back in 1993. Every few hundred years in Western history there occurs a sharp transformation. Every few hundred years. Within a few short decades society rearranges itself. It's worldview. It's basic values. It's social and political structure. And it's key arts and key institutions. And the people born then cannot even imagine a world in which their grandparents lived and into which their own parents were born. I have six grandchildren. They would not believe the world I was born into. I was born into a world where there were 3.2 billion people on the planet. Today there are 7.6 billion people on the planet. One of the inevitable trends is that by 2050 there will probably be 9.6 billion people on the planet. That's an inevitable trend. Another inevitable trend is that technology will automate a lot of jobs. We see that every major government around the world is worried about the implications of youth employment. And what are the jobs of the future and technology. Drucker believes we're in this period now. We're currently living through this and it's creating what he called a post capitalist society in 1993, what we might call a knowledge society today. So the book is published by a man called Klaus Schwab who is the founder of the World Economic Forum. You know that meets in Davos each year. Leading politicians, leading technologists, leading sociologists, leading thought leaders. Get together. And he realized over a period of years that there was some remarkable presentations being made perhaps in the area of biology and science there. And there were some remarkable presentations being made with technology. And there were some remarkable presentations made social and political implications of all this. But what he detected was a convergence of social, technical, material science, biological. They were all converging in a way that we've never ever experienced before. And he called this industry 4.0 or the fourth industrial revolution, which is suggesting that the first revolution was when we discovered how to use water power and steam to increase our productivity. And we moved into factories and mass production with the second revolution with the assembly line and electricity and oil and gas and things like that. Around the 1960s, we moved into the third industrial revolution according to him, which was computers, information and computing technology. But he now argues very strongly that we're in the fourth, which is where we're bringing all these technologies together in an integrated fashion, which will even redefine what we mean by humanity according to him. My company sponsors TEDx Cambridge University every year. This year, we had a professor from Germany. He's called the Bionic Professor because he has a Bionic arm. He was born. He's got the most advanced prosthesis ever seen. He walked onto the stage and he said, I'm better than you. I can do things you can't do. Lifted his hand and spun his hand around really fast. He's a walking example of technology enablement. He didn't see it as a threat. He saw this as enabling clearly. So what is this thing? Because why I thought I've challenged us is, what implications might this have for the nuclear industry? If any, he talks about the fourth industrial revolution and I was invited to host a group of 20 Asian countries to come to this exhibition that was held in April this year in the UK on the fourth industrial revolution. There were people from all of the industry sectors. There wasn't anybody from the nuclear industry actually. Most other industries, but it's the same thing. You design a nuclear power plant, you build it, you operate it, and you decommission it. That's similar to a lot of other industry sectors. The principles are similar. We all think we're unique. We are unique with our experiences and with the content, but we're not unique with the principles of design, the principles of building, the principles of operating effectively. There's a lot of commonality there, I would suggest. So Klaus Schwab identified 13 technologies that would disrupt all of our thinking, disrupt all of our business models. I'm going to quickly pass them by you and see if you think any of these you're currently working with, you're the future. Or you think intuitively you will be working with, or you may be horrified and say no way. But I'm not going to let you not be aware of it anyway. There are 13. I'm not going to go through them briefly now. First one, autonomous robots. What is an autonomous robot? It's actually something that does something without human beings and makes its own decisions. The military, like always, were very innovative with this and then the military produced autonomous robots rather than putting human beings out there to take decisions. Are you using autonomous robots in the nuclear industry? Remember, I know nothing about the nuclear industry. No? Yes? Tell me quickly. Yes or no? You'll have power. So yes, you are. Not in nuclear power. Okay. I don't know about autonomous robots and driverless cars and things like that. They make decisions. The key point with this technology is they make a decision. They don't wait for the human to make the decision. Actually, we all work with technologies, all of us fly here. We're dependent on decisions made by computers and air traffic control all the time. I fly on an Airbus 380 which has 250 million sensors on it. It has 22 computers on board monitoring and making decisions about everything a human couldn't possibly do in that time. That's the first one. Autonomous robots. Intuitively, do you think they will be more and more used in the nuclear industry or not? Yes? Definitely yes. Potentially. Autonomous. Because we're looking for knowledge transfer and safety and things like this. Then you've got the other issue, ethical issues. Driverless cars, we all know the story of the ethical issues of what happens if a driverless car suddenly has to make a decision about human life. You know, a driverless car has GPS, so we know where it is. A driverless car has radar, so it can actually sense all the other cars around it. A driverless car has amazing computing power, so it can work out all of these things faster than we can. But if it has to make a decision between an old person crossing the road and a young child in a car that it would otherwise hit, what does it do? These are the ethical issues and I predict there will be more conferences and discussions on ethics. Ethics in the nuclear industry when this becomes more prevalent. Systems integration. I think that's one we're all familiar with. What this really means is we now have the internet, the worldwide web, we have computing power which is growing exponentially so we can integrate things that were before separate. The idea of a platform, we call them platforms, don't we? Where we can integrate all the different parts of the company. One of the big barriers to effective knowledge sharing is silos in an organization. Systems integration technically removes silos. Technically we can have all of the complex activities of an organization working together. It's a question we're talking about ethics, we're going to talk about politics, we're going to talk about power. We're going to talk about human limitations and we're also going to talk about what humans can do that machines cannot do. Internet of things, we've all heard that. We connect everything to the internet. There are certain smart cities in the world now where all of their street lights and traffic lights have sensors. There could be video cameras and audio and they're sensing everything and the computing power is so powerful that the computers can work out and divert traffic without the need of traffic signs or traffic systems. They can do it dynamically. We can connect everything to the internet. So we now talk about the industrial internet of things, IOT. And this is where industry 4.0 kicked off. How can we connect everything together in our manufacturing process of design, build and operate by connecting it all to the internet in new seamless ways. That's the industrial internet of things. And it's affecting all of our lives. It's affecting our power, our homes, our healthcare. It's affecting everything. Everything can be connected together. The predictions for the number of billions of sensors that will be in this world in just five years time is mind boggling. If you want to go that way, that's a third industrial internet of things. What about the nuclear industry? How much within the design, the build and the operate and the decommissioning is internet based these days? Security is the big one, which I thought you'd say. Not talking about the cloud at the moment, it's the open cloud. I'm talking about using, so everything is based on local, non for security reasons, for safety reasons. That sounds very sensible to me. Do you think that will change in the future? You hope not. Yes, it will. Yeah, of course. Just the capability is there. We have sensors, all sorts of sensors. We can connect them to the internet. We have computing power that can actually analyze this and make predictions or make decisions. Some say faster than humans. We'll look at that in a moment. Number four, simulation. It's been around for a long time, particularly in the aircraft and the military industries. You can simulate anything in any situation. Now, I'm sure simulation is a big part of the nuclear industry, isn't it? And we'll continue to be so. And you're probably world class of this simulation. When you're designing products and prototyping, goodness knows what, simulation is big. So I think you will understand that one. It's each of these 13, he reckons when combined will change things dramatically, disrupt things. And of course, simulation is big in the health industry now. Doctors can do incredible teaching and education. And simulation is big for education, isn't it? And we can learn by doing. I love going in aircraft simulators. I have to admit it's just I really feel I'm there. I get you know, I get all hot and bothered. And even pilots do. They simulate a burst tire on takeoff or they simulate an explosion of an engine. And the pilots go in, you know, physiologically, they're there. Simulation is very well advanced. This is the big one that many people are talking about. Number five, additive manufacturing. Now, what does that mean? Additive manufacturing means 3D printers. Have you heard about these? 3D because material science has developed to such extraordinary level. You can now load an industrial printer with something like a powder, which creates solid objects. Ford Motor Company, the chief executive of Ford predicted that in three years time, Ford's will be the first in the world to produce mass produced, customized, driverless cars locally with 3D printers. All of the material can be built. All of the knowledge goes into the design of the products and the material knowledge. And these printers will produce it locally. They started off in the healthcare industry with examples of prosthetics. And how there was a story of a little boy who couldn't afford these prosthetics because he was only six years old. And by the time he was seven or eight, his arms have grown larger and he couldn't afford it. Now they just print another, I know they print just print another arm. They can do it at such reduced costs. Very soon, organ transplants, we just print them on demand. We can print. In fact, one of the predictions of this book is by 2025, we'll have the first 3D printed liver available to people on demand. What's really shaking many industries is if you take any of these big industries and look at the supply chain. A lot of companies, particularly when I worked in Asia with Asian companies, they were able to make components in the value chain much cheaper. Now 3D printers locally in your country can produce these products cheaper than outsourcing, a huge implication to outsourcing, which we haven't thought through yet. Some of these technologies are miraculous, but it takes us a long time to understand the implications, good or bad. These industrial printers are getting better and better. Additive manufacturing is called because rather than the old way of manufacturing, which is to take a lump of wood or to take some steel and reduce it to the shape and the object we want, now we have the material science to add it gradually layer by layer to produce it locally at a fraction of the cost. And all of the effort is in the design and the design software and the design is what's key, the recipe. The sixth one is cloud computing. I guess we're all heard the term simply means that everything appears to be in the cloud. We use that analogy. But actually it's all sitting on computers somewhere but groups of computers in data centers. So the important thing is that with smart telephones, with iPads, with laptop computers, we can all be connected anywhere, anytime through the cloud. But the other big thing about cloud computing is the economics of cloud computing, because it's so much cheaper to do it that way. A lot of companies initially said we'll never do that, we'll never do that, whether it's a small company or a large company because of security. We don't want all of our data in the cloud. Yet Google will boast that they have a team of 680 cyber experts, the biggest team in the world that will knock out everything in anything on their platform, much, much more powerful than any small company or any company can do of its own. It might have a few cyber professionals, cyber security professionals, but never the economy of the large ones. But there are political and economic and ethical issues. I'm just quickly presenting these technologies that everyone's talking about for advanced manufacturing. Seven, I heard this mentioned I think yesterday, augmented reality. What does that mean? It means taking the virtual realities and overlaying them on our physical reality. So you're starting to see that now on your phones, aren't you? For example, you can go down the high streets and you can look at a shop and it will say what products and you can see the products and see the prices and you can see all the information that you need to make a decision. It's augmented reality. Is any of that going on in the nuclear industry? In education and training? Construction. Yeah, claim this will be a big one across all industry sectors. Augmented reality. We will all need to learn, you will need to learn with all these new augmented realities, different realities, superimposed on one another. Is this horrifying you or delighting you? Great. Big data. Well, that's easier to understand. Computing power has been increasing in power exponentially since the 1960s. But it hasn't been that noticeable before because if you look at the graph which I'll show you in a moment, the exponential growth of computing power has reached the stage now, which is unbelievable. And if you hear heard of Ray Curvitz, the futurist, there he has. I'll show you one of his slides in a moment what he says about the future of big data. All it really means is that because computing power is so powerful today and will grow exponentially, it can process huge volumes of data, huge, huge volumes so fast, and make decisions. Today, I gave the example of traffic flow in smart cities, but masses amounts of data we can actually process in seconds or fractions of a second. Cyber security. Very big issue. One of the big issues and one of the skills that I think, you know, a lot of people say to me, well, what should I do for the future? What can humans do that computers can't do or won't do or artificial and machine intelligence will not do or is not good at. I'll leave that in a moment. Because of big data, because of great processing power, now we can not only analyze the past, but we can actually start to predict future scenarios and make decisions based on that. Predictive analytics. I would have thought was something you were doing in the nuclear industry. If you're doing simulation, I'm imagining that you're doing predictive analytics to the degree that you are looking and trying to model different scenarios. It's a big area. Predictive analytics. I'm suggesting all these things might have, you might be wondering why I'm presenting these all so quickly. I think they will have an impact on the way we look at knowledge in the future. When we come to human machine interfaces, which is where there are predictions, for example, by 2025, there is a prediction that there will be 20 different turning points. And the way this was achieved is that Klaus Schwab interviewed all the chief executives in the world he could get hold of to get their probability of what is likely to happen. And the view is that by 2025, 10% of us walking on this planet will be wearing our computers in biological ways. 10%. Redefines human being, doesn't it? In a degree. I mean, I can understand it in healthcare and the examples I've given with healthcare, I can see the good. But we're now talking about adding to all human beings in new ways. Connecting us all together in new ways. Working with machines. You see, I've heard as I would naturally expect to hear at knowledge discussions, cooperation, collaboration, co-creation. But I am going to argue that we need to learn how to cooperate with these tools to collaborate with these tools and to co-create with these tools. Already, we have a term for it, co-bots, which is collaborative robots that are working with human beings to do our tasks. Digital prototyping, we talked about that. And the third one, how many of you have come across the final one, blockchain technologies? Yes? A couple of you have. This is very interesting. Yes. Well, actually, it's an offshoot of a computing, a centralised computer model. The blockchain essentially is a distributed database that facilitates a sort of database system that records updates information. It records it in blocks and not in record format. It just keeps them in blocks. That's a very large byte of data in the database, and then it automatically updates itself. It provides enhanced security feature for single point of failure that the traditional computing model offers sort of a centralised model. But this one is more or less decentralised database. It's a decentralised ledger. And if we look at the example of the banking system, we trust an intermediary. We trust our banks, at least most of us do, and we trust that they will receive and process the payments. It's a centralised system. Blockchain technology is disrupting the entire banking and financial industry, because instead of a centralised system based on intermediaries, they are decentralised. So because everyone has enormous computing power, the same is on every single computer, you do not need an intermediary at all. It becomes a trusted network, an encrypted trusted network where you don't need any of these banking institutions at all. And all money will be transferred, if you accept their predictions, through blockchain. But what does it affect us in the knowledge management world? Intellectual property can be contained and managed and protected on blockchain technology, distributed encrypted ledgers, new models of working that are with us. So that's briefly industry 4.0. The book is called The Fourth Industrial Revolution. If any of this interests you in any way, and I think we need to be aware of it when we look at knowledge management. Take a look at the book and what they say about the 13 different technologies. But just just a couple of minutes, the Japanese have gone further. They've said industry 4.0 is fantastic. It's advanced manufacturing. But what we want in Japan, because we have huge societal problems and problems of employment, we want to use these technologies for the benefit of society, not just for the benefit of advanced manufacturing industries. And so in 2016, the Prime Minister of Japan was visiting Germany where they have the annual Hanover Fair, which is the Mecca for technologists. And this is where industry 4.0 was born out of the Hanover Fair in 2012. In 2016, he went there and stood against Angela Merkel and said, we in Japan are moving to society 5.0. This is the big societal transformation plan to Japan. Now, whether it affects the nuclear industry or not, it's going to affect human beings. The one thing that is common is people. And the Japanese are saying that all of these technologies can be used for the benefit of humanity and the social problems that we have today. If you're interested in knowing more about that, just Google society 5.0. There is a Japanese government five year plan on this society transformation using the 13 technologies that I briefly introduced. And finally, our friend Ray Kurzweil, the futurist, this is looking to 2050. Ray Kurzweil wrote a book in 2006. And it was called The Singularity is Near. What he was predicting is that because of the exponential growth of technologies, we've now reached a critical phase where humanity and technology are connected together as one. And we need to learn how to work with that. We need to learn, we need new laws. We need to understand the instruments that we'll need for the future to make this happen. Quite scary stuff this. He actually showed this graph here, which shows that over a period of time, human intellect is growing. But technology, which really started in the 60s was growing exponentially. And for the first few decades, we didn't really notice it. The reason we're noticing it now is because it's reached this stage, which is called the knee, the critical knee. I don't know why they call it that. But I suppose it's like a knee, where computer technology power is equal to the human brain. Do you believe that will ever happen that the power of computing will be equal to a human brain? Yes and no, right. Okay. They predicted in the book in 2006, it would happen by 2030. They've now revised their predictions to 2027. Based on the computing power that's actually growing by 2027, according to them, the computing processing power, the human brain works with neural networks, at incredible and an analogical analogy, forget it, fast speeds, not just digital, but fast speeds. The computing power will reach the same as the human brain by 2027. He goes on to predict that by 2050, computing power will be equivalent to all the human brains on the planet. All human brains combined. First of all, the prediction of the population is going to be about 9.6 billion. I think they work on 10 billion people. By 2050, the computing power will be equivalent to the brains of 10 billion people. That's what the book says. If you're interested in this, this might have a slight impact on knowledge management. But if you are interested in this, you might like to Google the Singularity University, which is on the West Coast of America. The Singularity University is basically, as it says in its byline, preparing humanity for accelerating technological change. What can humans do that computers can't do? Any suggestions? Given what I've just suggested, we talk about machine intelligence and human intelligence. If we go back to knowledge, let's first of all talk about our memories. We have human memory and we have computer memories. Which one's better today? Computers. Depending what for? But if I were to say to you, what did you have for breakfast 24 years ago, and what time did you have breakfast? The computer would tell you. Computer has perfect recall. There are a few humans that actually do have perfect recall, but unfortunately, only a few. So memory is quite a competition going on between machine memory and human memory. What else do we do when we're learning? We analyze, we receive information and we store information, and we analyze that information critically. What about computers and humans? Do humans do analysis? Computers? Computers? Well, my take, actually, on this question of what humans can do and computers can do is this. When Turing conducted his experiment far back, then what we know in computing parlance as the church Turing thesis, he was trying to test human intelligence with the computers to match it. Though presently, Garry Kasparov of Russia has been able to defeat the fastest computer in the chess game. That's the Supermonster computers and the research facilities in US. The question is, computers have actually some level of intelligence. They have intelligence, but humans have intelligence a bit above them in the sense that human beings have experiential learning, right? The computers doesn't have. That's one of the distinctive features between computers and humans. But with time, the evolution will reach a point where they will be trained. Well, in the subfield of computing, we call artificial intelligence. They will be taught how to think and be able to use the sort of element of intelligence and it does the best of the experiences they have gotten. They store it in reference engines and they will be able to pull it back and use it to take decisions. That's why we have expert systems in geology, medicine, oil exploration, space exploration. They are autonomous systems that take decisions too. They have components that enables them to think, reason and then store some of their experiences they have learned and whenever any situation presents itself, they will be able to record those information back and take appropriate decisions like humans. The interesting thing is when computing power got to a certain stage, there was the breakthrough when, as you pointed out, the computer could beat the human at chess. Chess has a finite number of possibilities and the computing power reached the stage where it could process the finite number of possibilities faster and better than the human. But the other big breakthrough was machine learning because that was based on programming a computer as good as the rules that you program. But with artificial intelligence, the machine learns itself. The machine isn't just what it's programmed to do but based on the decisions, based on the experiences it learns and so there's the other game that I'm sure many of you are familiar with called Go. From Asia, the Asian Masters of Go, the big difference between Go and chess is Go has an infinite number of possibilities. 18 months ago, the computers beat the champions and they gave up with Go. The computer power could do it better with infinite possibilities and machine learning and inference, as you say, than the human. Take this to the next step. If think artificial intelligence creates its own logic and knowledge and thoughts, then what's it to prevent itself from ever being able to get turned off? What do you think humans can do better? The suggestion is that human beings, when it comes to analysis and certain forms of decision making and memory and learning and computing power, computers can do that better so we should work with them, not against them. Human creativity and intuitive knowledge. We talked a lot about intellectual knowledge but not enough about intuitive knowledge and that's something which it is believed machines will not do and hence we have my final slide is this. When I was in in Brussels in 2003 somebody from NASA provided this slide and Larry knows this well. In 2003 there was a conference in Europe it was called staring into the crystal ball where everybody had to project and NASA said in 2003 this is their 25-year knowledge management roadmap. In 2003 they predicted that then they were in the era of sharing essential knowledge critical essential knowledge that's where they were and then in 2007 they were moving towards an integration of all the different systems with NASA and then they predicted that by 2010 they will be starting to capturing externally in their space missions and then it goes right through to 2025 where knowledge systems collaborate with experts for research. So the reason that I presented this to you I'm not a technologist I believe in the integration the holistic approach with people process and technology and I believe fundamentally in strategy but I felt that this technology development particularly industry 4.0 advance manufacturing which deals with design build operation is something that we should put into the mix of our thinking of how that might have an implication on knowledge management of the future and as I said in the beginning I think in five years time we'll be talking about human and machine intelligence in slightly different ways than when we are today. Any further questions comments yes well first comment as I already said we should not look only at where we are going in the far future but we should also look at where we are coming from the deep past right because the creature that now is facing these problems has a long deep history if we don't understand that long evolutionary history during which we built a physiological system where our emotions for example are dictated by a number of hormones and so on and other humans have used this physiological system to create hierarchical society and use them to stratify society and control different parts of the society during the millennia we have exploit this very well machines will be able in the far future to use our system to control us in a way they already know more about each of us than we probably personally know and for sure more than what our friend know about ourselves so I could have other comments but what I'm wondering is that the Japanese society 5.0 is a society of humans of or post humans right in practice we could be replaced totally because if you implement even only the 13 points that you discussed today we will not be strictly speaking necessary right well tomorrow I'll come back down to earth and I'll talk about ISO global standards and what we've learned about that but it's very interesting to to at least be aware of this because it's going to face us all and our grandchildren are going to have to deal with this most certainly so they think computer did you did you see that youtube clip the researcher knows there was an assumption of this there was a researcher there was a researcher into this that had his daughter that I think was two or three years old did you see this youtube clip and he gave her an iPad and within just a few moments she was going like this with the fingers like this and then he gave her a color magazine and she was frustrated because she was going and that was what she was born into so your point is well taken so maybe just one comment before going to lunch you showed that graph showing the human cognition going up and then the machine again we must be very careful when we talk about human intellect because since as I said my previous discussions since approximately a hundred thousand years ago is not an individual intellect since long long time ago that is a collective intellect so we should be careful not get confused a bit and we think that this intellect if the individual human like yourself myself now right talking about something that we already share so already now I think if you compare the individual intellect probably is much below what you're plotting there and as we know we are delegating more and more of that intellect as I said to other humans and now also to the machines so there is this interplane which is also very important and you know in just an example in the U.S. there are 1600 hospitals 1600 hospitals that are doing diagnostic diagnosis using uh visual the X for example which is a system looking at big data looks at big data humans would not do that the future look at a pair of plants with all the sales that now can give data big data to the computer will be out of control for humans so this workshop will not be necessary I think because will not be under the control of humans the knowledge that the machine looking after the nuclear power plant will have is it just provocative a bit? Claudia you are getting me set and upset and then I think the best way to avoid this I can say move in my body on my head go to the lunch thank you very much