 Great. And yeah, so my name is actually Lauren Stovall and I'm the head of nonprofit programs here at AWS, which includes our amazing TechSoup program and partnership with TechSoup. And we have been working with nonprofits for over 15 years, which is as long as AWS in the cloud has really been around. And that's with organizations of all sizes and all missionaries. And what we really focus on is helping nonprofits transform how they achieve their missions. And so I want to take a step back and talk a little bit for those new to AWS in the cloud what AWS is. So the kind of talk track is that we're the most comprehensive and broadly adopted cloud platform that includes 200 fully featured services and data centers around the world globally, whichever way you want to look at it. But what that really means is that you're accessing compute and storage and other capabilities over the internet in a way that allows you to be hyper efficient and how you're consuming types of technology and infrastructure so that you don't have to build it and maintain it yourself. So probably a lot of your organizations have maybe servers in a closet in your office or have built your own data center somewhere and you have to maintain that and plan for use and the hardware and all of that kind of stuff and what AWS does is take that element out of it and you actually then consume using AWS services, in most cases as a utility like you would water or gas or anything like that so you're paying for what you use, as opposed to paying for it, all up front. And one thing that I think is always really helpful or interesting is that the way AWS actually came to be is through Amazon.com so Amazon.com as you can imagine it is a massive global web platform and getting bigger every day. And so they needed a massive amount of data centers to operate that for the peak of performance, which is the holiday season. And so they built this massive infrastructure but they weren't using it except for around the holiday season when orders went of course through the roof, and they realized wait a second. This could be shared. And everyone could get economies of scale and efficiency and price and cost if we shared the infrastructure that we built for our business and so that's actually how AWS was formed. And that's now what it is and growing every day and what's what's possible. I would say, you know, for smaller organizations generally where they start with AWS is things like storage, making sure that your data is not on someone's computer who might leave or it might get lost, storing that data somewhere where you can have of course security and access controls and all of that stuff, but in a way that's hyper resilient and you won't lose it. And it's always safe and accessible and then once it's there in the cloud stored, there's all kinds of things you can do with it, analytics, machine learning, all of that kind of great stuff. And then the other thing and I'll come back to data in a second is compute power. So compute is what allows you to run your website, for example, and that can be, you know, a static website or a very large web application that your beneficiaries, for example, use to engage with you donors that use to donate or find out information about your organization and so compute is really the home of the digital experience, as well as the back end of a lot of this data processing which I told you I was going to come back to and so that compute power that you can access again when you need it and turn it off when you don't is what underlies what a lot of what we're going to all of what we're going to talk about today which is artificial intelligence and ultimately gen AI, which I know everyone is really really excited about and that's what we're going to focus on today, but really at the end of the day with transformation whether using AI or analytics or just basic business intelligence which is quick site is our service for that data is actually a massive asset. And a lot of organizations, I think, look at data as a byproduct, sometimes an unruly byproduct, and the reality is that data is actually an asset, a massive organizational asset. And we want to work with nonprofits to look at the data they have understand what they have visibility into and what they don't, and really how to harness that data to have better insight and better decision making. And so what we really focus on is encouraging data literacy in nonprofit organizations, what data do you have what formats what can it tell you, and then looking at ways to create cultures around data, making sure that when you're making decisions you're bringing data to the forefront of that whether it's on the cloud or not really to establish that culture of, we're going to do this for this reason, but it needs to be supported by data opinions are great we all have them. It's also really important to look at it something objectively, and to measure also objectively on the other side to know if something's working or not to further increase your decision making, and that decision making can be anything from a programmatic decision you're making. It could be a decision about how to make more efficient efficient processes in your organization. It could be using the data to target donors, and see how you can give them more personalized preferences to create that that connection with your organization. And at the end of the day you can start small. It's really about testing and learning and thinking about how to bring data into your everyday life. As you go about doing what is the most important thing and all of this which is advancing your mission and going from outputs to from to outcomes to ultimately impact and data is also a key to that right to be able to understand what is the impact your organization is having. And so, we have a ton of programs also to help nonprofits kind of go on this cloud journey. We understand it can seem overwhelming at times on where to start. And so we have a ton of different programs to be able to support that. And that's everything of course from our AWS nonprofit credit program that we have with tech soup, where you can get credits to underwrite the cost of some of your AWS annually so every year you can come back and access credits and it'll generally cover a lot of your foundational infrastructure like your website for example or some storage. We also have an AWS nonprofit partner network, which is great and growing all the time. Same thing with marketplace so an AWS nonprofit marketplace. This is relatively new but what it is is basically a shop. It's kind of a better word to go and acquire software and other platforms but the great thing about when you use marketplace is that it aggregates it in one bill with your other AWS spend maybe where your website's running and so you're all doing it in a way where everything is aggregated together and you don't have all of these costs kind of all over the place it really streamlines that in one place. And then of course there's all kinds of great tools to explore there. And the other thing is our AWS Imagine grant so that is our annual grant program is a cash grant program. The cycle just ended for this year but it'll be open again next year so you can start thinking about your projects. But there are two categories momentum to modernize for those just getting started, and go further faster those are really taking it to the next level in cloud and innovating with things like machine learning algorithms for example. We also have an annual conference so the Imagine nonprofit conference that's in DC, well it's moved around but it'll be in DC again this year as it was last year, and we hope that those that are around can attend or get the opportunity to travel to visit with us, though, all of that is available on demand after the conference and is now from our last conference in March of 2023. We have another time this year and so we'll keep everyone posted but really recommend going and checking that on demand content out it'll definitely give you an even deeper sense of how nonprofits are leveraging the cloud. And then finally, the AWS powering purpose in the cloud guide. So this is based on extensive research as well as data from our Imagine grant program that really showcases the state of cloud adoption in the nonprofit space. What it does is share, you can benchmark where your organization is in your cloud adoption journey, you can see again how other organizations are leveraging the cloud and the types of solutions that could be very valuable based on your organization's goals. And so we have a QR code there you can take a second to scan it. And again, through that you can get the benchmarking tool and the guide. It's very long. It's like 60 something pages but it is also very, very, very comprehensive, and I think thought provoking to help organizations even begin to start talking about what are we doing with our data, what are we doing to leverage digital to reach more audiences or enhance the services that we're providing through our programs. And so we would love for you to take a look at that and certainly reach out to us on any of these things if we can help you move further in your cloud journey and the work that you're doing on behalf of your mission. So with that, I'm going to pass it over to the star of the show Evo to talk about what's possible with you guessed it, Jen AI. Hey, thank you Lauren. Oh, can everybody hear me. I think so yes perfect. Hey, so appreciate that. So, like Lauren said, if you don't know much about able us you probably know that we're a cloud hosting service hosting companies websites hosting your data hosting your business processes right. So what we do way more than that so compute and just your virtual machines is one, but today what I really want to do is kind of peak your interest and try to have you think beyond just using computers as a virtual machine and think what else is possible. Right. So let's dive into a little bit here so what do these images all have in common. So they look like typical photos of dogs and food and mountains. But actually, all these images were artificially created or generated, and they were created with a new form of artificial intelligence that we call generative AI. So today what I want to do is talk to you about generative AI, but kind of a state of the industry and how Amazon can help you use these new technologies to drive your nonprofits forward. So, my name is Evo Jensen. I'm a solution architect here at AWS. I'm in the nonprofits team. And like I said, it's going to be fun. So today. There we go. So what we're going to talk about today is what is generative AI, and how can we use this for your nonprofit. And then once we establish why this is going to be useful for you, we're going to talk how we can meet that demand for you. I'm going to end this with a little cool demo that you might find interesting as well. So generative AI is a new type of artificial intelligence that can create new content, new ideas, like conversations, stories, images, videos, music. And it's, it's powered by large machine learning models that are pre trained on enormous amounts of data like internet scale data with a thing that we call foundational model foundation models. And we've seen a revolution in this field over the past couple years, especially the past six months. I'm sure you've heard of chat GPT. It is everywhere. Right. I'm sure you might also have heard of tools like mid journey stable diffusion. These are some of these generative AI solutions that can generate images from just text prompts. So this is not really that new though chat GPT stable diffusion they are now new public models and really have brought this to the public. But to be honest, AWS has been doing generative AI for years. For instance, this type of machine learning this type of AI. That's really what powers to search results on Amazon.com. And it also powers Alexa to create a human like conversational experience. Right. So over time, these models become better and better to to fine tune into your needs, and especially in the terms of Alexa to really become this conversational like interface with you that goes way beyond the chat box that you had maybe 10 years ago. So I'm going to do a little bit technical here but I'm going to bring it back into why this is useful for machine for for nonprofits. Machine learning has been around for a long time. And with traditional machine learning models, you need to do a lot of work to actually make it work for you. You would, for instance, have to train your data with like label data. So for instance, if you want to build some artificial intelligence that can recognize houses, you have to feed it hundreds of thousands of images of houses. And after that, it starts recognizing other houses, or what you can do is you can give it a whole bunch of existing shopping data. For example, on Amazon.com, like, hey, people who bought like, you know, batteries also bought flashlights. Right. And over time, these models learn to correlate those two and I can recommend purchases for for shopping cards. For example, the creation models is new type of AI. Instead of gathering label data for each model which is very intense labor intensive, we're using unlabeled data. Basically, what we do is we throw the internet at these models, and basically let them learn on their own. Right. This is kind of like that neural artificial intelligence. This is what chat GPT and stable diffusion do. So these are these giant public models that allow you to to basically lean on the knowledge of the internet and then generate net new things, not new text, not new code, not new recipes or images. Right. The cool thing about these foundation models though is that we can adapt them for specific use cases. And this is where you have to stop thinking how can this apply to my company. Right. We can adapt these models. So they can be customized to perform very specific functions that are unique to your business. And that you can actually do with very small amounts of data. So this is kind of that that that shift that has happened over the past 12 months to make machine learning and artificial intelligence really reachable to smaller companies and the public at large. So one of these general use cases for generative AI. Some of these use cases, again, were already possible with the machine learning of the past 10 years, and now they're completely improved with this new type of generative AI. So let's think about chatbots. Right. You've had chatbots when you check with your airline or with your bank. They've been kind of clunky generative AI really is transforming the space with much, much better interactive capabilities. So it just sounds like you're talking to a real person, right. You can see it in the evolution if you have an Alexa, or maybe one of our competitor devices even at home. It talks back to you like it like it becomes more and more life like overtime. So those are customer experience enhancements. It can help customers boost employee productivity with like search content creation summarization, etc. And we can even improve your business operations with like document processing predictive maintenance quality control that kind of stuff. So let's take a little bit deeper onto how this can help nonprofits. And here's just a tip of the iceberg on how nonprofits could leverage generative AI. And really, the purpose of this slide is to get you thinking on how you leverage this new technology. Right. So you're just a few examples. Let's think about fundraising. Like these language generation models like chat GPT can be used to generate qualification emails, thank you letters, or even generate new proposals. Member interaction, you can improve your customer service. Think about again like these chatbots of the past, they are now completely overall become much more interactive and much more smart about your business. And grant writing, you can use these these tax generation models to draft grant applications project proposals using data and text from your database of grants. Companies, especially nonprofits, they often have a huge knowledge base of data, a lot of customer data. So let's think on how you can connect this new technology to your existing CRMs and other systems of records to to search across your knowledge base. Content creation. There's a couple of public AI platforms can turn blog posts and written content into videos automatically. So that's new. We have five synthesis that AI, these are some of the public companies that help you at this. And we have tools that can create a targeted fundraising emails like I said, volunteer management. So we can use this this predictive aspect of of AI to help you identify and engage volunteers match them opportunities track their progress, etc. And one that I think is pretty cool is outreach. I'm sorry, multimedia. I think it's actually happening right now, whereas closed captioning that you can turn on the zoom call right now. So that's a type of artificial intelligence where it picks up your voice and helps summarize video or transcribe video to make them more accessible. So what you need to do is to think about all these things that are possible. Right. So, here at AWS we're all about what we call the art of the possible. And we'd love to hear from you what kind of use cases you can come up for this new technology. Just a real quick example here. Like I said, a transcript summarization or article summarization for for grant writing this is some stuff that our partners co here on AI 21 do war tune is a cool tool that actually helps you improve your writing by making it more engaging image generation. This is kind of what stable diffusion does this is actually a temple by stable diffusion 2.0 and this little room that you see doesn't actually exist. It's completely computer generated. Right. You can think also about taking two different concepts like a human face and a VR glasses, and then actually ask the model ask this AI, and you combine those two and actually give me a human face with a VR goggle and sure enough, I can do that. So we're not new to this. We have here at AWS have been doing machine learning and have been taking this to our customers and making it easier for customers to do for the better part of a decade now, we have over 100,000 customers using AWS for machine learning alone, including many, many nonprofits. So, so I think at this point it's clear that this is going to be useful this is going to be transformative for the industry for all industries, including a nonprofit industry. Right. And we've seen these public models chat GPT stable diffusion that are out there that anybody can use. You can download an app. You can log into a discord server and start creating these images. But think of this right all that data is publicly available. And that's challenges on how to apply that to your specific specific company. So there's a lot of privacy concerns and I noticed questions in the chat. So I'm going to address those right now. That's privacy concerns about feeding your data to these public models like chat GPT right because think about it. These public services will learn from your IP to typing into it, and actually might give it back to other customers and you really want to avoid using these public models from for your targeted enterprise enterprise applications. Second, you also need to customize these models with your company data. What good is a chat GPT if it if you have it on your website, but it cannot actually answer the FAQ for your specific company. Right. And all this has to be done in a secure way, and in a cost effective manner. So let's see how AWS specifically can help you take this concept of generative AI and make it make it applicable to your company. So, first of all, how do you even get started. Right. I mean I know that some of you are new to AWS. I know some of you that I've always seen chat GPT in the news. So how do you get started with that right. So we actually made this available to you so what we have done is we have curated a selection of these foundation models as they are called from an anthropic from stability AI and even some of our own models, and we make those available to you through our investors services, and we'll dive a little bit deeper in how we do that. Right. And you need to customize it right so it's one thing that you have access to these these models in a in a secure and private way, but then you need to be easy to take this base financial foundation model and then build these different differentiated apps on top of it that use your own data. And again, your data is your IP. So this is a secure, protected and private during the whole process you need to be able to be to be in control of your data. So you know it remains private and confidential. Right. So, and we do that. So if you know a little bit of AWS, each little what we call virtual private cloud within AWS is completely private to you. We're running on a on a wreck and some data server right next to what your bank or next to Netflix, but we have segregated this. The government uses as well. So you can trust it secure. Any data that you store in your virtual private cloud will remain there and will not be shared across any other customers and that's really important. Basically, everything we do at AWS is pay as you go and you use something in AWS and when you're done with it, you can turn it off and your cost goes all the way down to zero. Right so you don't have to pay or get locked into upfront contracts, you go like hey I need a virtual machine for the next month, or I need this gen AI model for the next five minutes, and you get built per second basically. So that's how we do pay as you go we even developed our own chips, our own graphical chips to make it even more cost effective to you. Okay so you need to be able to use this quickly right so like I said in the first bullet point and flexibility we make these things available to you, and how do you even get started to using those. So we have a number of different services that called Amazon SageMaker. I'm going to talk a little bit about Amazon Better Rock, which is a new service for Gen AI. These services allow you with very minimal coding sometimes even no coding to implement these generative AI solutions into your websites and into your business processes. No coding solutions. So what we do here at Amazon is we start building taking these, these, these bits and building blocks and bits and pieces and we make them available to you in a completely managed way. We already do this with OCR document scanning with text to speech speech to text. Now we have a new service completely built on generative AI called Code Respawn. So if you're a development shop building code. I'm going to give you a little bit demo of this and a little bit later. It can actually write code for you. This is just crazy. How easy it is. Okay, so for those of you that do know about in the US and the services that is the net new that we've announced for this year within the context of generative AI. There are two new services. Well, really one new service. One was there already a little bit so Amazon Better Rock is a completely new service that makes it super easy to you to build and scale new generative AI solutions with these foundation models from our partners. So this is the Amazon SageMaker Jumpstart, which allows you to basically take these models and modify them for your specific use case. I'm going to show you a little demo of SageMaker Jumpstart in just a little bit. Let's see a special specialized chips. I'm going to jump over them. It's really cool. We have cool technology and Amazon Code Respawn is this new managed service that allows you to help build your applications and your mobile apps faster. Okay, let's dive a little bit into each of these before I dive into my demo. SageMaker Jumpstart is a tool and you'll see it in live in action in just a few minutes. You get full control of your infrastructure. You can full control which foundation model you want to use, how you want to customize it. So it's quite easy to use. We have a long list of publicly available foundation models that you can use and run and customize and then eventually also deploy into production into your ecosystem. We have lots of different models. We have proprietary models, publicly available models. The proprietary models are that cost a little bit more, but they're super accurate. That's why that's why they charge a little bit for them. But even the publicly available model, they still offer complete visibility and control over all the parameters and you can still customize as well. So SageMaker Jumpstart you'll see in a second. It's a little bit. If you're not used to coding, this might be a little bit of a lift. For those of you that are a little bit familiar with, with writing code to see how easy this to generate to use these foundation models. And then we have a new servers in preview called bedrock, which we're going to make it even easier to use genoff live AI. It's completely serverless. So no worrying about CPUs or virtual machines or CPU hours in and costs like that you really just pay per API call. So with bedrock's serverless experience, you can easily find that right model, easily customize it again privately and securely with your own data and then integrate and deploy it into your applications that you also have running on AWS like on a web server or on a database or something like that. And as I said, we have partnered with top AI startups to bring these foundation models to you in an easy way from AI 21 and tropics stability AI. And even ourselves so even Amazon, we've built our own foundation model that basically based on 20 plus years of our own experience with for instance Amazon.com search or Alexa. This is just a really cool application for for completely managed. There's no customization needed. So what we basically done is AI coding companion. It helps you quickly write secure code by generating full function code suggestions in your favorite ID in real time, based on this on comments. So I just type in my code like, hey, I need to parse the CSV string and total list, but ignore list that you type enter and hop Amazon code whisper actually outputs all this code right for you. This is really powerful stuff. You might have actually seen this if you're into coding that this is something that we do that that chat GPT can do as well. But again, think about it right chat GPT that is a public service and got its training from the internet. And as we know the internet isn't always right. So the results you get back are not always right either. So we did with code reservoir is train it on specifically curated code to make sure that we get really high quality code back. Cool. So, I talked a lot. So what I talked about is basically was genitive AI, right, we had cogeneration we had text to text the text to image, and we showed how you can apply this to your start a nonprofit in a in a secure and cost effective way. So I'm going to kind of give you a little demo of Sage maker jumpstart and show you how easy this to do that. So I'm going to switch to the console here. So if you've not logged into Amazon and just go along for the ride and enjoy. If you have logged into AWS before, then this might look familiar for you. Right. So, in the console. So what I'm going to do here is go to Amazon Sage maker so you can see a lot of different services here easy to those are virtual machines but most of the building off and I think cloud computing. That's other things storage as three but I'm going to go to Amazon Sage maker here. See on the left is also a jumpstart at the foundation also click on that. So right here in our console, you see all the foundation models that we have partnered with and that are available to you inside Amazon Sage maker. There's hugging face tech generator. There's some GPT stuff as well. And all the way to bottom here is a stable diffusion. So stable diffusion is that text to image generator tool. I'm going to click on view model here shows you the ULA. And so really what this does is once you click on open notebook and studio. This takes a few minutes so what I've done is instead of clicking on here I'm going to switch to this new tab, where this is already opened. And what this gives you is a playground to play with this model to kind of try it out to customize it even. So what this whole is basically tutorial it's a lab. Right so it goes through all the steps to do to to get this model up and running again in your own virtual private cloud. So nobody else has access to this particular one is all yours. And then we'll show you how you can customize this. So this is coding. This is a lot of Python code. And this is why we came up with Amazon bedrock which actually makes it a lot easier and Amazon bedrock I don't have a demo for that now but it's in preview right now. So basically, it's really a tutorial so you can follow along. And really all the coding has been done for you you just have to click on yeah let's do this let's do this. What it does all the way that the end is build that that that image generation interface all the way in your own in your own account. So this is completely done in my own sandbox. Again, nothing anywhere else is not public just for me, but once I'm done with this I can type problems like draw me a cottage and impressionist style and sure enough, the computer now builds because the computer being trained, the model being trained on this internet scale data knows what a cottage is right and knows what impressionist style is. So this is a net new image completely generated by by computer. So far, this is possible out on the public Internet as well. Right, so you can actually go to to stable the fusion website, sign up for a free account, log in and start generating images yourself. But like I said anytime you generate an image that gets that's a public image, I want people can see it too. And now all the prompts you give it, the model kind of learns from that as well so that is not the private data. What I want to show you now is how you can take this stuff and customize it with just your data. As an example, what I did here is I took a couple pictures of a of a dog. The dog is called Doppler is very cute. But this is this is a dog that that the model doesn't know yet about. Right, these are this is this is not these are not public images of the dog that the model knows about. This is a completely separate dog. His name is Doppler. And basically what I have is about, let's say, you know, 20 or so pictures of Doppler. So now the question is, how can we take this jointly trained model that took like a year to make, and how can we make it so it now knows about Doppler. So it can start actually giving us net new image generation to just Doppler. Right. So I have these pictures, a little bit of a little metadata here that says hey, this dog is called Doppler and yes it is actually a dog. And so when I have all these files in my directory what I do is I upload them scroll down a little bit here. So what though is I basically upload them to Amazon. So I upload them to S3 S3 is our storage within AWS so everything that we store websites, data, et cetera all goes into S3. So you can get in there. And again, you can upload your own images as well, right. But I chose these pictures of Doppler right here. And then what you do is you basically train the model on a on this differential data, and we call it transfer on him. So, again, all the code is right here for you to execute it if you want to try this. Let's talk about cost for just a second, right how much is this cost. You only pay for what you use. So the model is already pre-trained we actually make that available to you at no charge. If you want to do this additional incremental learning, you'd have to pay for the time that you use these CPUs train it. So pictures takes about maybe like an hour or so for this for this to learn about this dog, about one and a half hour I think I did yesterday to train this model. The CPUs that we use are about, let's say $3 an hour. So let's say it costs about $5 in total to teach this model on how to think about this dog. And now it's now it's ready to go. So now I can actually give it a new prompt and go like hey, I want a photo of Doppler but now with a hat. If you think about it right. Nobody, none of the pictures I had actually the dog had a hat. So what if I just have the model do this takes about 10 seconds to generate. There you go. So this is a completely newly generated picture of that dog. It does look like him, right. And now it has a hat. So this is a completely new picture. And the model is trained on my private data, which is completely different from using these public stable diffusion services on the web. So let's do something else is this is a red hat. Can I do a, let's see, can I do a yellow hat. Try something else live always dangerous if you do them alive. There you go. Now it's a yellow hat. Right. Maybe I want him on the beach. It's about 10 seconds. So the system, the cost of this by the way so again think about it right this takes about 10 seconds to generate. Like I said, the CPU that Amazon that areas charges you to do this is about $3 an hour. And so think about how much it costs to do 10 seconds of image generation. Now we go now we have Doppler our our trusty dog with a yellow hat on the beach. So one more example. I actually want a Picasso style painting, instead of picture. Let's see if you can do that. So again, each each generation here is literally pennies. Right. There you go. Isn't that cool. So the model stable diffusion already knew about Picasso already knew about the style of Picasso, but we just taught it with this transfer learning about my dog Doppler. And now it can actually make the castle style paintings of Doppler on the beach with a yellow hat. Okay, so let's let's go back to our slides and let's see what we've learned here. So whatever you learned here. Right so this is Sage maker jumpstart. So we used a pre trained foundation model in this scale case stable diffusion, which already knew a lot about the world. It knew what a beach looked like nobody dog looked like it knew what a hat looked like, but it didn't know Doppler. With Sage maker you code in Python and you pay for CPU used by the second. So again, took about like maybe like $5 to teach it about my dog. And then it takes literally pennies for 10 seconds of image generation. Amazon bedrock is a new service as in preview right now so if you're interested in that will have some QR codes on the screen in just a second to teach you about about new services. It will be serverless there will be a lot less Python coding and the way to pay for it is basically by API call rather than by having to think about CPUs and stuff like that. And it's customizable with your own data right and that's so important for for you as a company, how can I do this targeted for my use cases, while I retain control of my data. This is a graphical example, because I thought it would make a nice demo with a cute dog. Right, but think about the use cases that you can also do right earlier we talked about. We talked about a grant writing right so if you have a database of proposals, you can feed that into these text models these chat GPT light models, and actually start building that new content trained on your data. But then for your purpose only based on that data, you can train it on your customer database, you can train it on your on your FAQs itself. And all of this is based you go pricing I think I mentioned that right so right after this demo I'm going to shut everything down and my cost from that point on will be literally zero. So this whole demo basically the full price of this was maybe like a few dollars. So if you're used to Amazon, go to the console go to Sage maker jumpstart and pick the stable diffusion 2.1 model. It will actually like populate your your editor with all that code you don't have to know Python you can just click through it. You can see all kinds of examples of auto generated images and you can upload your own images as well, and see how you can customize these models for your particular use case. Cool. So that kind of concludes the demo I'm going to have some some links here. These slides will also be available on the tech soup website starting I believe tomorrow. If you have a chance to snap all the QR codes right now. The slides will be on on tech soup and on YouTube pretty soon for you to to see them again. And so with that, I'd like to say, thank you. With my pleasure to demo this to you, I hope you had a was informative. And let's stop thinking on how you can use this for your use case. We do actually have some questions if you don't mind. Not at all. All right, the first one is from Tom. And his question is regarding stable diffusion and image creating with AI. How are copyright and the artist rights being considered and the images we create. Is it safe for our organizations to use images created with generative AI. That's a good question. So in the, do I still have that up. I think here in the foundation model if you click on view model I already had it somewhere here right. So the you guys right there. I'll be very honest, I have not read through the whole you know myself, but we do provide with each model exactly what kind of requirements there are of using the generated images from here. So to be honest, I have not read right through the whole ULA. So I don't have a specific answer your question, but what I'm going to say is, you can probably find these answers in the ULA that we provide with each model. Okay, and then Brittany had a question to add to Tom's which is, we have been using AI to generate news articles and other marketing materials. So should we be providing the AIs we use, should we be giving credit to the AI program we use. Yeah, I think that's going to be the same answer. I'm not a lawyer. I'm a technical guy who knows how to make a cute pictures of dogs. But I would say let's read through the ULA on exactly what the copyright implications are. And I to be honest, I'll go read through that later myself and maybe I'll learn something from the next time. Okay, this one's from anonymous. Regarding the use of large language models or LLMs. How protected is our personally identifiable information for our participants. Yeah, so that's exactly what you thought we showed you today, right. So basically what you're doing is you're spinning up a copy of that model into your own account. So think about it this way, your data is already safe in your own VPC, your virtual private cloud, and you're bringing the model to the data. And as on the public, you take your data to the model on the internet. So really by having your data already private and then running that model, the LLM inside your own accounts, none of the data that you have will be sent back or used to to train the original base model. It all stays local through this concept of transfer learning. So if you're asked and if you have something Evo. Great, all I can answer it to start. It says can you share some reference accounts that nonprofits of nonprofits that are using generative AI today. The short answer is that generative AI is relatively new on the scene we have a number of organizations that are not yet in terms of that use case and story. So we always of course with any of the organizations nonprofits we work with, ask their permission, when we share any reference so for the moment, not referenceable however, if you saw Evo slide earlier you can go back to it with the NASCAR slide with a lot of organizations on it. Those are all nonprofits that are using AI or and or machine learning in some way today. And so that's organizations as large as the nature conservancy new organizations that are just growing like stop soldier suicide. And across you'll see all different mission areas and be the match national merit donor program is of course in the health space stop soldier suicide mental health and suicide prevention nature conservancy all of those and so as you see hard American Heart Association. The number of organizations again, all over in terms of size it's really depends on the use case that you have for AI and ML it doesn't solve for everything but if with the right use case, it can be a very powerful tool. And so we certainly have on our website, which is our AWS nonprofits website, a number of case studies and use cases around AI we have a nonprofit blog that does the same. And certainly you can reach out to us directly via our website and we can go into much more detail about the various uses of AI and case studies that exists, as well as the use cases and deeper in yours in terms of generative AI. So did you want to add anything to that before I move on. No, I think I think that was exactly right. Thank you. Perfect. Okay, this one's from Tom again. Thank you for all the questions Tom. What metrics do you have published on the accuracy of code whisperer and your other models. Let me pull this slide up here didn't show it in the terms of speed. So, those are accuracy. I'm not exactly sure but we did have some metrics around that. In the preview we did some challenges and some some examples because a lot of this also relies on how the actual users used code whisperer by actually typing useful and correct prompts to actually generate that code. What we found is that participants who use code whisperer 27% more likely to complete tasks successfully. I know that's not 100% your question. I don't have a specific step on actual code accuracy and I'll see if you can find that for you. All right, and last one, does AWS integrate with other systems such as quick books online. Is there a donor management component. Are there other demos or support if we are interested in joining AWS. Right so to answer that there we have a large ecosystem of partners and we basically partner pretty much every company out there. And we build a lot of integrations and customers build integrations with AWS right so AWS is more than just stage maker. It is the virtual machines is databases all it's 200 plus services and all these services we are what we do is really interconnect all these services with your existing systems that might still be not in the cloud. They might still be in your own data center. So, we have a huge expertise in building these integrations. Yeah, and to add to that point there's a service called app flow, which helps build those connectors from your solutions to the cloud if they're not on the cloud today but for example a lot of those connectors already exists the Salesforce connector already exists for all of their capabilities the blackboard has a number of connectors for example if that's a tool that you use. So the connector some of them are pre built some of them need to be built depending on the situation, but all of that is possible integration is absolutely possible and it's frankly recommended, because that's how you get your data all in one place. And then you can really take advantage of capabilities beyond even the platforms and software you're using, whether it's analytics and other types of capabilities quick site to do visualization and really create that one source of truth for the organization where you can tap that data with the right permissions, as you need to to look at it for different reasons the CFO might have a different reason and question of that data than your fundraising team. And so that really really bringing all that data in one place is a highly effective way to create insight for your organization to make data driven decisions. So I'll add one point to that Lauren. So my role is solution architect so apart from doing webinars every now and then really my role is to talk to our customers to make them help them integrate all these solutions and make them have to make a sense of all our solutions. So, not sure who you're with, but please reach out to us and we'll be more than happy to set up a conversation with the solution architect that that covers your, your specific company or domain. Perfect. Thank you, Eva. And then for those of you I know a number of people and there was also kind of a question in here about training and capability. And so I will say that we have a ton of free digital training on a variety of AI of course but everything you know what is the cloud all the way up to really advanced use cases. So absolutely recommend checking that out and again, the partner network is super super important we absolutely understand that nonprofits are change the world companies and they're not always deep technologists and that's okay. That's why we have cultivated a really great network of organizations that are dedicated to supporting nonprofits and helping build that capacity. And that's what we help with as well to Eva's point our solution architects are here to create that guidance. And in talking with the solution architects based on what you're trying to do. They're really a great resource to help you figure out that journey what training should you take what partners are good to engage with so you don't have to do it out there all by yourself. It's really we serve as a trusted advisor in that capacity. So you can get to the right place quicker. That was so excellent. Super super informative. I mean, wow, I'm looking forward to the next one. And I'm not just saying that this was a lot. I learned a lot. Very different from your previous webinars. Just, they just keep building and building and building so if you, if you miss a previous AWS webinar go to our YouTube channel and text YouTube channel. I know some people here may not have been technically savvy, but this is the direction that the world is going to. So I know this webinar is going to be super helpful for you if not today, that in the future and we look forward to having the AWS come back again Tom, I mean, Lauren Larkin, any last words, Eva. Thank you. And we are happy to talk more to reach out I know that has been a question go to our AWS nonprofits website there's a start a conversation link that will come right to us and our team, who will understand what you're looking to do and get you to the right people who can provide that guidance. And have a great day everybody. Bye bye.