 You know, the good thing about this panel is we go from colleagues into the parliament, to the Ministry of Foreign Affairs, to ambassadors, to central bankers, and then to scientists. So let me also, our fourth speaker Ernesto Demiani is a professor at Khalifa University for the Department of Electrical Engineering and Computer Science. He's also the Director of Center for Cyber Systems. He will be, his remarks, the opening remarks will be about trust and governance issues of multi-regional deployment of AI. Please Ernesto. Oh, thank you very much. I was the only one apparently who had taken the opportunity that was given to us to present a couple of slides. I don't know if they are available or not. We just try to press this button to see whether this is the case. If not, I'm, I can, okay, here they are. So basically, I really listened with a lot of interest to what was said before me, and as you said, I'm not an economist and I'm not, but I'm very much interested, of course, in the economy of the processes, especially the processes, large-scale processes and the regional processes in terms of, you know, large-scale supply chains, business processes that involve multiple countries. And I'm interested from the point of view of the underlying technology. And so I would like just to add a few words on what could be the day after of, you know, a deglobalization that is taking place. And there are two, two words that I want to say before starting to show you a couple of slides. And the two words are words that are very fashionable in Europe. And I also heard them in the region here a lot. One is decoupling, and the second one is de-risking. So what is decoupling from the point of view of a technologist? Introducing redundancy. So if a part of a process is not feasible due to some conditions that happen. For example, a supplier is no longer available, and then you want to have a second sourcing. So you need to have this second alternative part of business processes. And this is called decoupling. You are introducing means, of course, you are, in a sense, paying more. You want to be at an optimal solution just because you want to decouple. You want to be able to cut out some parts if this is needed. And the second is de-risking. So the fact that I wanted to put risk as a first class citizen in my decisions. And these are decisions that from the technology point of view are decisions that are about business processes and supply chains. So this is what I wanted, a very interesting picture that we have in front of us of de-globalization and arising areas of conflict. And of course, the impact is that we have a technology platform that needs to handle decoupling and de-risking. These are the two words. So in a sense, the problem is that our platforms, we run them based on data. So supply chains are optimized every day, cargo shipments are optimized every day. Regional, of course, processes are, and inter-regional processes are particularly important in this region. We are in a place which is the hub, the hub between the east and the west. So I don't want now to enter into this from the point of view of the economist or the politician because it's really not my daily way. But from the point of view of the technologies, this makes a very fascinating place. On the other end, the problem is that normally we have to optimize jointly, meaning that we have to solve jointly some problems of optimization to run global airlines, to run global cargo, to run the business processes. And in order to do that, the big problem is that the notion is that we must have some joint strategy. The actors that will take decision together need to be able to basically trust each other enough to do joint optimization of large-scale regional and inter-regional processes. So my problem is that we have, most of the tools, of the technology tools we give to decision makers are based on this assumption they will trust each other enough to make a joint decision because this is a major part of any of any. So in a sense, well, I, being a scientist, well, you see, I have a nice picture. This is called the Pareto Frontier, Pareto was a, you know, so in the decision making, you tended to find those points, which basically will be a sort of a compromise so everyone can agree on them with a minimum of damage or a minimum of penalty. The problem is that in order to do that, you must know, have a joint knowledge of the data, of the information that you are jointly taking a decision on. And the problem is that in this situation, we are less likely to be able to do that. The worst part, now let me inject a bit of AI. I hope I'm not boring you too much. The worst part is that most of the optimization decisions today are taken by systems. Humans have a role in starting, have a role in sharing the information, but then the notion, for example, of optimization supply chain. We did a master class here in the University together with a number of European University on the pharma supply chain at the time of COVID. So basically, you need to optimize the pharma supply chain to be able to, for example, to do the vaccination rates that are needed for the population. And this is something that you can do if you basically tell each other what are the sizes of the warehouse, what are the availability of the instruments. So the problem is that these days optimization are done using systems. And they are done using systems that are a little bit difficult to open and identify. I know there has been a session in this conference specifically on this topic in the previous days by a colleague of Califa. And I want to underline this notion. We are accustomed to optimize trusting each other, putting the data in a box, and then running a sort of the algorithm and getting out of our solution that will bind us all. But the problem is that the data or the information, the trust level, may be decreasing much in this region specifically in the near future. So the big processes may be, in a sense, less easy to optimize. So there is a problem of failure happens. And we may be seeing in the future, our models, we do run simulation models, of course, of this. And our models say that we may see lower accuracy of the joint models. We may see fast model degradation. So a number of assumptions that led joint running of processes may be sort of show that lower performance in the next future. And the mutual trust, mutual trust will become scarce, so scarcer then. So this is what from the technology perspective I wanted to highlight. This is not just political, this is also technological. Because the technology platforms, and this has been discovered in Europe with the Ukraine war. And with the push for the risk and the decoupling, the supply processes that involve certain countries. So this is something that, again, I would like to highlight to you here. So, let me just skip all this part because I had a sort of a try that too. And I want, let me just arrive to this discussion point. We were trying to do a second digital revolution that was deploying large scale joint optimization, artificial intelligence especially, across markets. We were trying to do it in this region specifically, we're still trying to do it. It's similar to the introduction of the Internet in the 90s. But in the sense for how perversive it is. The problem is that the introduction of the Internet was done in a moment in which there was a globalization type of trend. Everybody was trusting each other or could pretend to trust each other. The deployment of AI and the joint optimization of large scale processes need to take place in a situation in which there is not enough trust. So we need to find another way to take joint decisions in order to handle this limited trust that we have. Of course, technically I could show you how optimization in a non-trustworthy environment could take place. But I just wanted for the moment to highlight this thing. It will be the day after, because whatever the future brings, certainly the globalization in a mutual trust environment is going to be, I believe, a memory of the past. Thank you very much. Thank you, Ernesto. We, in economics, we did borrow so much from science. And most of the terminologies, you have said, we use them in economics, but I hope we use them the right way, de-risking with correspondent banking. So this is the way we have been using it. But in science, it's being used differently.