So today I'm going to talk about 4.0.0. 4.0.0 is network that stores vast amount of data. So all these data can be applied for machine learning. It's just how we have a state. I'm going to talk more about the democratization of AI, where a lot of smaller users can now use AI. That is a lot. So as you can see here, there are a lot of different regulatorys in the Europe AI Act. They mentioned that advanced models or any deep learning model have to go through a series of evaluations. And any incidents that's caused by the inferences have to be reported to the union. So a lot of different governments are starting to do all of these policies, even in Southeast Asia, it's very fragmented. Even in Asia, it's very fragmented. Every single country has their own version of policies. And Falco itself can ask a provenance layer, because once you store data into Falco, it's immutable. At least you can prove that the data has not been changed in any way. This is a map of all of our storage providers across the world. We have a lot of storage providers all around the world. So we can cover for most of the policies for data locality. If you want your data to be stored somewhere and you need machine, because usually machine learning requires your data to resize, where it occurs. So in these cases, Falco and storage providers can be the data storage provider for the data there in terms of machine learning on a large scale. I'm going to lead on to how the data architecture across the whole entire landscape is changing. So traditionally, we have banks, we do like OLTP transactions. It started off with the data warehouse layer. It went on to the data lake layer. Right now we're in the midst of a two-entry. We are going to transit between these. So a lot of enterprises have built a data warehouse for OLTP transactions. This is for structured data. They did not account into sentiments that came from social media. So social media plays a big part in data right now, data collection right now. You have your semi-structured data as well, your JSON format, that led to the creation of the data lake. So traditionally, this is how it occurs. You have your POS and then your data pipelines. It ingests into a big data warehouse and then they are fragmented into smaller data markets. So these smaller data markets, then the end users can create dashboards of it. So they are very simple dashboards. This was before the event of AI or at least right now there's a lot of AI applications happening. And this was also before like our amps were popularized. Right now most enterprises use data lakes. So data lakes aggregate together. Structured data and structure data. So a mass amount of data is put into the data plane in the data lake itself. And then now you can see there are now data scientists applying machine learning on top of it. So it's slightly different now and also the machine learning training portion. The feature store is also something really new. The feature store allows you to share features across different organizations that the data scientists has created. So one of the use cases on Falcoy is to ensure that the features used for training are stored in the Falcoy network. And therefore they are immutable. You can prove that you did not change any of the features in a machine learning process. So there when any audits or any policies happen, any policies check happen. You can be sure to say that my machine learning process is compliant. Right now it has been decentralized. So they realize that aggregating everything into a data lake is 2,000. It takes too long for it to happen. So a project in Southeast Asia. Any banks that are doing this data lake project, it costs around 30 million USD. And after 30 million USD and 2 years of hard work, they realize that sometimes the domain team, I mean the general consumers don't have the domain knowledge to consume from the data lake itself. So they are now shifting it to the business units or every single domain experts within the organization itself. And every single entity is now creating this thing called data product. It's really similar to what the factory industry is happening now. There's a data contractor. So these data contract can be, you can use FEM for it and FEM contacts. You can create like a perpetual data deal with one of the data organizations in our storage network. So if you know that someone is storing biomedical data in a certain location, you can create a FEM and create this perpetual deal where you keep taking in new data onto the network. Or whenever your machine learning model is not performing as well, you need fresh data to retrain it. You can do this model. So this is the data mesh. So this is one business unit by itself within it. I'll just skip this part actually. So imagine all the storage providers have for FEM within this ecosystem. This is the power customer 360 use case. So this use case that's cross cell. For example, you buy a car and they get some profile from you some marketing profile of you. They can then sell this data or not resupply state that they can aggregate more of it consumer patterns. And then they train a recommendation model across on top of it. So the far coin network can then sell data to one another to create a very complex use case or a more complete use case. Next, I'm going to talk a little bit about ecosystem wins. So there are one of our ecosystem partners. They can't be here today, but they had they created a GPU cloud and all of the data here is thought on at least most of the data here is going to be stored on our point. They're creating a pipeline to ensure that most of these data flow into a far coin so that they can create audit trail for immutability. They have a global reach. They have data centers in Singapore and US and I think they are going to expand to even more countries. But what's different here is that they are and video preferred partners. So they partner out with and video and during the GTC event that happened a month ago, they actually shed a far coin on one of their site events. So this picture here is them sharing about far coin. I'll be showing showcasing this picture as well to let you guys further understand what our partner is doing behind the scenes on the GPU cloud. I'm going to skip the big coin money. So this is their interface. They are interfaces. They have a model training at least a model playground. If you train your models, you can upload it onto bit dears cloud and you can share the models. They have a model marketplace is similar to hugging face, but I think right now they're not charging to consume the models of here. And with the help of IBM far coin IPFS, they can create smart contracts and AI so they can create these perpetual views with different entities that want to use their machine learning platform or at least the models that are taking the data from different places they can use smart contract to enforce it. For the decentralized data and our impact, they are actually a far coin storage provider themselves as well. So they put the data onto their network and they are willing to collaborate with different storage providers as well. So reach out to me if you want any use case on top of this. AI governance as well. As I mentioned there, if you create if you store the steps of a machine learning lifecycle onto far coin, you create audit trail or you create this metadata trail to enforce this auditability. So this is the machine learning lifecycle. I'm going to briefly talk about this. For feature engineering and when you're developing models, you can create a data providence layer so the audit trail is tracked there for the in front site, you can put it says archival as well so that all the prediction results is thought somewhere. With this I'm going to end my presentation but if anyone here has AI projects, please come to me and talk to me about it. I will advertise it in our ecosystem or even advise how to integrate with our con. Thank you.