 So, I think that the main advantage or opportunity that this summit is bringing is that it took or it brought together different players and different stakeholders, and some of them are more focused in this AI world, in this AI industry, and until now I think that the UN in general has been a bit behind, and it's been the private sector, the one leading it. So, I think that this will help bring together all these different players and also understand the benefits or the things that each of them can bring to the table and hopefully work together among each other. So in regard to Hangar, I'm going to give you three examples of the work that we are doing both with data and with AI. So the first one is we are trying to use facial recognition and imagery and apply AI to it to try to access malnutrition. Until now it's been done with some bands. So now we are trying to use AI and estimate or measure this more accurately because many times the problem is that we don't know where the most affected people are and how much they are affected. So that's one of the examples where we are using AI to fight against Hangar. Another one is similarly we are using high resolution satellite imagery and apply machine learning to map infrastructure and facilities such as schools. And once we know where these services and facilities are, we can use them to improve or optimize the supply of food, etc., and bring food to the places that it's most needed. And then the third one would be again using satellite imagery, other types of data to try to predict when these droughts, etc., are going to come in advance so that we can prepare the response in advance. So not a big change that needs to happen within the UN is that we need to build internal capacity as well because you say a lot of work with private sector, etc., but I think that to drive these conversations and move this work towards a better future and a brighter future we need to build some internal capacity that will allow us to do the work internally better as well and create these kind of AI solutions together with the private sector but as one-to-one relationships rather than going to them asking for help and things like that, being able to interact equally in this space. So I think that's something that we should start working towards building this capacity and internal knowledge. So as we've been discussing during this summit, AI provides or is bringing a lot of opportunities but there are also many difficulties or challenges that if we don't face them can create or increase the inequity. And these challenges as UNICEF are first of all, there is no data about the people that we are working for. Most of the places where we work there is very very little data and this data is bad quality so it's hard to build machine learning algorithms or models that learn from this data. So that's the first one, how to fight against this bias. The second one is that we don't know what are the needs of these people many times. The solutions are mainly built in the western world and they don't necessarily address the needs of local communities and local people all over the world. It's the second one and the last one is that not only we don't know what are their needs but these solutions most of the time don't reach them because they don't have access to the digital technologies that they need to have. So I think that these are three areas that we really need to make sure that we include everyone in this new world that is coming.