 My name is Wes Floyd. I'm a product manager on the back of YAL team, and I'm presenting new project Waterlily on behalf of a much broader team. Ali Hare is one of our project leads. Simon from our team has been doing a lot of development, Kai Luke, Irina, our team is helping. It's really, I guess a lot of the back of YAL team was also participating, but I just want to call out, there's a lot more folks that are doing the work. I happen to be on the best time zone for this session. So I've got to take you through the content. So I'm going to take you through three components here. First, we're going to talk a little bit about how back of YAL fits in with FEM, a brief refresher. Then we're going to talk about Lillipad, which is an important new component we've built between FEM and back of YAL, sort of a bridge. And then I'll spend most of the time talking about ethical art, AI generated art, and a really interesting novel approach that I think this is one of the first projects to ever do, which is compensating artists not for their work on chain through an NFT, but through derivative styles of their work on chain. So it's a fun use case and we'll jump right in. So for a little bit of promo here, background, back of YAL is sort of almost like an L2 on top of the file coin chain. You know, file coin chain, FEM is where a lot of our coordination work happens. Back of YAL is an off chain compute ecosystem. You can find out information about it. Back of YAL.org to see more about the architecture and effectively it can run any sort of compute that can be containerized in a Docker container or was in binary in a batch mode across the network of back of YAL machine. So really trying to get the best of both worlds, the trust and verifiability of on chain with the verifiability of these new off chain compute systems, but more robust, complicated workloads like, in this case, ML model inference for generating art. Project Lillipad is the bridge between layer one and layer two. So Lillipad is effectively its two components. One, it's a smart contract on Falcon Virtual Machine. It's listening for events. People might want to invoke a back of YAL job. So it's listening for them to invoke those jobs. And then secondly, it's an off chain demon that is listening for events that are triggered through the Lillipad events, call or smart contract and then actually triggering those back of YAL job. So it's a bit of a bridge at this point. I'm not going to use the word duct tape. I'm going to use the word bridge. So anyways, please find out more about Lillipad here at the website GitHub. Here you can see the source code. This is a component that we will potentially open up to more broad use cases. We'll talk in a minute at the end about other applications of this technology. So if you're thinking about scenarios where you have on chain workloads, smart contracts that are very smart, contract intensive, but it could benefit and have more robust capability if it could do off chain compute or if you're already using that off chain compute today and AWS or GCP. But if if that compute were now more trustless, verifiable and open, those are exactly the types of use cases that we want to help you with. This is a demo from Ali's machine of actually invoking the Lillipad caller solidity contracts and in our GitHub repo, you'll see lots of examples of how to build your own here, but eventually it enables a user to pay for a job using fill or T fill if you're on test net, specify as a string input the spec of the off chain job they want to run. What's a Docker container name? What specific code do you want to run? And then it invokes it, it sends it on chain. And then this is an example here. The previous, oops, hold on a second. The previous examples that we use this for was just generating stable diffusion images. And let me do this here. This is an example of saying we're going to run a standard stable diffusion image, stable diffusion, by the way, for folks that are not into machine learning is a framework for generating art. So we give it a text prompt and we say generate unicorns and rainbows and the AI can magically create that art. No human had to draw this. So it's very powerful stuff. But what's interesting and people have talked about in the past is to say, well, what if I wanted to generate that but generate the style of Van Gogh or generate the style of Pablo Picasso? This style transfer thing is another layer on top of stable diffusion. That's a really fun application of the two technologies combined. So this is what we were working on in the past. You can see some examples of how the AI will automatically generate different combinations. It's all random each time. It's all unique of this AI generated art. So the next thing that we did on top of this was to build water lily. And the goal here is to say there's a lot of underrepresented artists, a lot of opportunity for them to better monetize their work. So instead of actually taking their work and selling that work on chain, what if we could train their style? So if there was a new artist in the space, let's say her name was Misty. And Misty's got a tremendous amount of work. She's got 40 or 50 different pieces in her collection. And we're not going to do any work with her copyrighted work. We're just going to generate a style. It's a it's an ML model effectively that represents her style. And so when I generate rainbow unicorns, I want to generate in Misty style specifically. And I also want a portion or all of the payments to go to Misty. This is the impetus behind water lily. And it's a great way to bring together all these different concepts in the one place with a with a nice sort of humanitarian output. So we are going to be launching this project soon the next few days. You'll see some more information about it if you visit water lily.ai. We're still working a few bugs out. We're still growing the number of artists that we have on the page here. But I want to give you guys just a little bit of grounding of what it's this is a couple of fun examples from our internal testing of what it looks like when you apply this style transfer to generated art. So we found in the public domain, this was an artist from the 1800s who had performed lots of drawing of Native Americans and English settlers and things like that. And so we gave it the text prompt generate a picture of Barack Obama. So this is Barack Obama as if that artist from the 1800s had drawn Barack Obama, which he did not generate an image of Captain America. This is all, again, generated images. And in this case, after the work comes back from back, we will be sending the funds to the artists themselves for public domain. We'll probably donate to a to a charity that aligns with the Falcon Foundation's mission. And then a couple of other fun examples. We can even take things that are very messy. Like these are stills from an artist who did a lot of noise generated art with music. We took the stills, we train their style and now we say generate rainbow unicorns. And this is actually rainbow generated unicorns with that that style applied. Some other examples here from a 1920s artist with some interesting illustrations. And now what is what does it look like as if that artist had generated Barack Obama, so lots of fun, fun things we can do there. We're really just scratching the surface. So in terms of what's next for this space, we're going to be building on a couple of things. One, we're going to build on our partnerships in the decentralized science space. We've got some partners that we're very interested in doing some bioinformatics pipelines that generate NFTs. And those NFTs, the back end work would go through Bakeryal. The NFTs would align well with their mission, so more to come on that. And then we have some other partners that we're interested in doing, improving the ability to generate yield and to centralize finance. So rather than just having a smart contract that executes trade on your behalf, if you could have Bakeryal, which consumes large amounts of important information, clean information from IPFS and Filecoin, you could run more sophisticated models of how you want to buy and sell, exchange, create loan contracts within within DeFi. So there's just lots of interesting areas when you're starting to combine the power of FEM, the power of off-chain compute. We're very excited about it. And then if anyone would like to get in touch with us, please reach out. We've got a GitHub information here for these various projects, Twitter accounts. I'm available in Filecoin Slack at West Floyd. Ally, you can see here, developer Ally is the Twitter contact for Ally, who's running a lot of our projects day-to-day basis. Thank you for the opportunity to present. That's all I have and we appreciate it.