 So artificial intelligence and machine learning give us an incredible opportunity to extract useful information from petabytes of data that are available and I'll provide three examples about how the forestry division is using this approach. The first example in the Great Green Wall we have this really important tree called the Bulbab that provides fruit, shade and value for local communities and we're working with UNCCD to train AI models on the location of Bulbab trees. This will use drone imagery and basically train in a module within FAO's CEPAL cloud computing platform to then predict the location of Bulbabs across the Great Green Wall countries. This is really critical for local communities that are trying to protect and restore this really valuable resource. The second example is where we are working with US Forest Service and many other partners to develop what we call early warning systems. These systems use artificial intelligence and machine learning to sift through the petabytes of geospatial data that's available to identify rare deforestation events in remote forests and this is really powerful for governments to then act on possibly illegal deforestation earlier. Previously it took two to three months to do this but now it takes two to three days and this is also thanks to high spatial and temporal resolution data coming from Norway's International Forest and Climate Initiatives data program. The third example is the forest data partnership where we work in with an incredible set of partners such as Google, World Resources Institute, NASA and Unilever to basically use artificial intelligence and machine learning to extract the best truth from several incompatible geospatial data sets. I mean this sounds very complicated and maybe that's where AI and machine learning has its role is that it can basically allow us to understand useful information from a lot of information and for this particular case, the forest data partnership, we would like to understand the deforestation footprint of the commodities that we consume. This is really powerful data for consumers, things like cocoa and chocolate, things like palm, soy, beef. So this five year project, the forest data partnership, we would like to have an architecture using AI and machine learning to have the best truth on where the commodities are coming from and their deforestation footprint. So the project really aims to accelerate innovative monitoring for forests and then deploy that to FAO member countries over five years, 24.5 million pounds and we'll be innovating, I'll provide four examples of how we're innovating. In the forestry division we're really pioneers of what we call digital public goods with things like sepal and open forest. These are completely open source digital goods that are widely used by FAO member countries. We'll be strengthening them under this program and deploying them and making them even more useful and accessible. The second innovation is we're going to be using a lot of novel learning methods under this program. We're going to be using online facilitated courses. We're going to be using standalone e-learning to really try and maximise our efficiency in technology transfer and capacity development. As an example, one of the first activities under the program is an online facilitated course in the use of sepal for producing good data, for doing restoration, for restoring your forests and already we have thousands of government officials enrolling this and we'll be running this over a month and it's really innovative, efficient and a great way to transfer technology to the FAO member countries. The third innovative approach is we're using what we call a country led planning process in this program. We're developing this with the Global Forest Observations Initiative and it's about putting the country in the driving seat on deciding what sort of technical assistance they need, how they need it and when they need it. So we really want to turn the normal paradigm around. We want the country to tell us what they need, when they need it, how they need it and we will then support them. We think this is much more effective and we'll allow the country to be more independent and make better decisions about the technical assistance we're providing. The fourth innovation is all the data, the methods, the outcomes of this project are going to be completely open. This is something that the Forestry Division is a pioneer on. Open data allows all stakeholders to have access to the data which is critical. It enables transparency and it just enables a really good practice for a project because all the information lives on after the project or the program as an open source resource. So through the program we'll be developing a lot of innovative methods, a lot of innovative data sets mainly tailored for national governments but we're really keen to make sure that Indigenous peoples and local communities are also able to use this technology and data for their own purposes. I mean there's mounting evidence that Indigenous peoples and local communities are the best stewards of their forest. So we want to find a way to use the innovations, the data to help them monitor their forest area in the most effective way and strengthen their stewardship role which is already shown to be incredibly effective.