 Hello, everyone. Welcome to theCUBE's presentation of the AWS Startup Showcase AI and Machine Learning. The top startups building generative AI on AWS. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem to talk about AI and machine learning. We have three great guests, Broughton Saas, VP, Vice President of Machine Learning and AI service at Amazon Web Services. Tom Mason, the CTO of Stability AI and Aiden Gomez, CEO and co-founder of Cohere. Two practitioners doing startups and AWS. Gentlemen, thank you for opening up this session, this episode. Thanks for coming on. Thank you. So the topic is hype versus reality. So I think we're all on the, the reality is great. Hype is great, but the reality is here. I want to get into it. Generative AI has got all the momentum. It's going mainstream. It's kind of come out of the, it's come out of the behind the ropes. It's now mainstream. And we saw the success of ChatGPT opens up everyone's eyes, but there's so much more going on. Let's jump in and get your early perspectives on what should people be talking about right now. What are you guys working on? We'll start with AWS. What's the big focus right now for you guys as you come into this market that's highly active, highly hyped up, but people see value right out of the gate. You know, we have been working on Generative AI for some time. In fact, last year we released Cold Whisperer, which is about using Generative AI for software development. And a number of customers are using it and getting real value out of it. So Generative AI is now something that's mainstream that can be used by enterprise users. And we have also been partnering with a number of other companies. So, you know, stability.ai, we've been partnering with them a lot. We want to be partnering with other companies as well. In seeing how we do three things, you know, first is providing the most efficient infrastructure for Generative AI. And that is where, you know, things like Tranium, things like Inferential, things like SageMaker come in. And then the next is the set of models. And then the 30s, the kind of applications like Cold Whisperer and so on. So, you know, it's early days yet, but really there's a lot of amazing capabilities that will come out and something that, you know, our customers are starting to pay a lot of attention to. Tom, talk about your company and what your focus is and why the Amazon web service of relationship is important for you. So yeah, we're primarily committed to making incredible open source foundation models and obviously stable diffusion has been our kind of first big model there, which we trained all on AWS. We've been working with them over the last year and a half to develop obviously a big cluster and bring all that compute to training these models at scale, which has been a really successful partnership and we're excited to take it further this year as we develop commercial strategy of the business and build out the ability for enterprise customers to come and get all the value from these models that we think they can get. So we're really excited about the future. We've got hugely exciting pipeline for this year with new modalities and video models and wonderful things and trying to solve images for once and for all and get the general value proposition correct for customers. So it's a really exciting time and very honored to be part of it. It's great to see some of your customers doing so well out there. Congratulations to your team. Appreciate that. Aiden, let's get into what you guys do. What does Cohere do? What are you excited about right now? Yeah, so Cohere builds large language models, which are the backbone of applications like chat GPT and GPT-3. We're extremely focused on solving the issues with adoption for enterprise. So it's great that you can make a super flashy demo for consumers, but it takes a lot to actually get it into billion user products and large global enterprises. So about six months ago, we released our command models, which are some of the best that exist for large language models. And in December, we released our multilingual text understanding models and that's on over a hundred different languages. And it's trained on authentic data directly from native speakers. And so we're super excited to continue pushing this into enterprise and solving those barriers for adoption, making this transformation a reality. Just real quick, well, I got you there on these new products coming out. Where are we in the progress? People see some of the new stuff out there right now. There's so much more headroom. Can you just scope out in your mind what that looks like? Like from a headroom standpoint, okay, we see chat GPT, oh yeah, writes my papers for me, does some homework for me, I'm okay, yawn, maybe. People say that, people excited. Or people have blown away. I mean, it's helped theCUBE out, it helps me feed up a little bit from my write-ups, but it's not always perfect. Yeah, at the moment, it's like a writing assistant, right? And it's still super early in the technology's trajectory. I think it's fascinating and it's interesting, but its impact is still really limited. I think in the next year, like within the next eight months, we're going to see some major changes. You've already seen like the very first hints of that with stuff like Bing Chat, where you augment these dialogue models with an external knowledge base. So now the models can be kept up to date to the millisecond, right? Because they can search the web and they can see events that happened a millisecond ago. But that's still limited in the sense that when you ask the question, what can these models actually do? Well, they can just write text back at you. That's the extent of what they can do. And so the real project, the real effort that I think we're all working towards is actually taking action. So what happens when you give these models the ability to use tools, to use APIs? What can they do when they can actually affect change out in the real world beyond just streaming text back at the user? I think that's the really exciting piece. I mean, so I wanted to tee that up early in the segment because I want to get into the customer applications. We're seeing early adopters come in using the technology because they have a lot of data. They have a lot of large language model opportunities. And then there's a big fast follower wave coming behind it. I call that the people who are going to jump in the pool early and get into it. They might not be advanced. Can you guys share what customer applications are being used with large language and vision models today and how they're using it, transforming that on the early adopter side? And how is that a tell sign of what's to come? You know, one of the things we have been seeing both with the text models that Edan talked about as well as the vision models that are stability that AI does Tom is customers are really using it to change the way you interact with information. You know, one example of a customer that we have is someone who is kind of using that to query customer conversations and ask questions like, you know, what was the customer issue? How did we solve it? And trying to get those kinds of insights that was previously much harder to do. And then of course software is a big area. You know, generating software, making that, you know, just deploying it in production. Those have been really big areas that we have seen customers start to do. You know, looking at documentation, like instead of, you know, searching for stuff and so on, you know, you just have an interactive way in which you can just look at the documentation for a product. And, you know, all of this goes to where we need to take the technology. One of which is, you know, the models have to be there but they have to work reliably in a production setting at scale with privacy, with security. And, you know, making sure all of this is happening is going to be really key. And that is what, you know, we at AWS are looking to do which is work with partners like stability and others and in the open source and really take all of these and make them available at scale to customers when they work reliably. Tom Aiden, what's your thoughts on this? Were customers landing on this first use cases or set of low hanging fruit use cases or applications? Yeah, so I think like the first group of adopters that really found product market fit were the copywriting companies. And so one great example of that is Hyperwrite, another one is Jasper. And so for Coher, that's the tip of the iceberg. Like there's a very long tail of usage from a bunch of different applications. Hyperwrite is one of our customers they help beat writer's blog by drafting blog posts, emails and marketing copy. We also have a global audio streaming platform which is using us to power a search engine that can come through podcast transcripts in a bunch of different languages than a global apparel brand which is using us to transform how they interact with their customers through a virtual assistant, two dozen global news outlets who are using us for news summarization. So really like these large language models they can be deployed all over the place into every single industry sector. Language is everywhere. It's hard to think of any company on earth that doesn't use language. So it's very, very important. Well, we're doing it, we're doing it right now. We got the linguists coming in with transcribe this puppy. All right. Tom, on your side, what do you see? Yeah, we're seeing some amazing applications of it. And I guess that's partly been because of the growth in the open source community. And then some of these applications have come from there that are then triggering this secondary wave of innovation which is coming a lot from, you know, controllability and explainability of the model. But we've got companies like, you know, Jasper which Aiden mentioned to using stable diffusion for image generation in blog creation, content creation. We've got Lenzer, you know, which exploded and just built on top of stable fusion for fine tuning so people can bring themselves and their pets and, you know, everything into the model. So we've now got fine-tuned stable diffusion at scale which is kind of democratized, you know, that process which is really fun to see. Lenzer, you know, exploded, you know, I think it was the largest-growing app in the app store at one point. And lots of examples like Nutcafe and Lexicur and Playground. So we're seeing lots of cool applications. So much, so much applications. We'll probably be a customer for all you guys we'll definitely talk after. But the challenges are there for people adopting. I want to get into what you guys see as the challenges that turn into opportunities. How do you see that the customers adopting generative AI applications? For example, we have massive amounts of transcripts timed up to all the videos. I don't even know what to do. Do I just, do I code my API there? So everyone has this problem. Every vertical has these use cases. What are the challenges for people getting into this and adopting these applications? Is it figuring out what to do first? Or is it a technical setup? Do they stand up stuff? They just go to Amazon? What do you guys see as the challenges? I think, you know, the first thing is coming up with where you think you're going to re-imagine your customer experience by using generative AI. And, you know, we talked about AI down and Tom talked about a number of these ones. And, you know, you pick up one or two of these to get that robust. And then once you have them, you know, we have models and we'll have more models on AWS, these large language models that Ada was talking about. Then you go in and start using these models and testing them out and seeing whether they fit in use case or not. In many situations, like you said, John, our customers want to say, you know, I know you've trained these models on a lot of publicly available data, but I want to be able to customize it for my use cases because, you know, there's some knowledge that I have created and I want to be able to use that. And then in many cases, and I think Ada mentioned this, you know, you need these models to be up to date. Like you can't have it stayed. And in those cases, you augment it with a knowledge base. You know, you have to make sure that these models are not hallucinating. And so you need to be able to do the right kind of responsible AI checks. So, you know, you start with a particular use case and there are a lot of them. Then, you know, you can come to AWS and then look at one of the many models we have. And, you know, we are going to have more models for other modalities as well. And then, you know, play around with the models. We have a playground kind of thing where you can test these models on some data. And then you can probably, you will probably want to bring your own data, customize it to your own needs, do some of the testing to make sure that the model is giving the right output, and then just deploy it. And, you know, we have a lot of tools to make this easy for our customers. How should people think about large language models? Because do they think about it as something that they tap into with their IP, or their data, or is it a large language model that they apply into their system? Is it interface that way? What is the, what's the, what's the interaction look like? In many situations, you can use these models out of the box. But, in typical, in most of the other situations, you will want to customize it with your own data or with your own expectations. So the typical use case would be, you know, these are models are exposed through API. So the typical use case would be, you know, you're using these APIs a little bit for testing and getting familiar. And then there will be an API that will allow you to train this model further on your data. So you use that or, you know, make sure you augmented the knowledge base. So then you use those APIs to customize the model and then just deploy it in an application. And, you know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then, you know, you're going to use an endpoint API and use it in an application. All right. I love, I love the example. I want to ask Tom and Aiden, because like most my experience with Amazon Web Services in 2007, I would stand up in EC2, put my code on there, play around. If it didn't work out, I'd shut it down. Is that a similar dynamic we're going to see with the machine learning where developers just kind of log in and stand up infrastructure and play around and then have a cloud-like experience? Si, I can get first. So we're obviously with AWS working really closely with the SageMaker team. They do a fantastic platform there for ML training and inference. And, you know, going back to your point earlier, the, you know, where the data is, it's hugely important for companies. You know, many companies, you know, bringing their, bringing the models to their data in AWS on-premise for them is hugely important. Having the models to be, you know, open sources makes them explainable and transparent to the adopters of those models. So, you know, we're really excited to work with SageMaker team over the coming year to bring companies to that platform and make the most of our models. Aiden, what's your take on developers? Do they just need to have a team in place? If we wanted to interface with you guys, is it, can they start learning? What do they got to do to set up? Yeah, so I think for Cohere, our product makes it much, much easier to people, for people to get started and start building. It solves a lot of the productionization problems. But of course with SageMaker, like Tom was saying, I think that lowers the barrier even further because it solves problems like data privacy. So I want to underline what Brutton was saying earlier around when you're fine-tuning or when you're using these models, you don't want your data being incorporated into someone else's model. You don't want it being used for training elsewhere. And so the ability to solve for enterprises, that data privacy and that security guarantee has been hugely important for Cohere. And that's very, very easy to do through SageMaker. But yeah, the barriers for using this technology are coming down super quickly. And so for developers, it's just becoming completely intuitive. I love this, there's this quote from Andre Caparthi. He was saying like, it really wasn't on my 2022 list of things to happen, that English would become the most popular programming language. And so the barrier is coming down just super quickly. And it's exciting to see. It's going to be awesome for the companies here and then we'll do more. We're probably going to see explosion of startups already seeing that, the maps, ecosystem maps, the landscape maps are happening. So this is happening, I'm convinced, it's not yesterday's chatbot. It's not yesterday's AI ops. It's a whole nother ballgame. So I have to ask you guys for the final question before we kick off the company showcasing here. How do you guys gauge success of generative AI applications? Is there a lens to look through and say, okay, how do I see success? And it could be just getting a win or is it a bigger picture? Bratton, we'll start with you. How do you gauge success for generative AI? You know, ultimately it's about bringing business value to our customers and making sure that those customers are able to reimagine their experiences by using generative AI. Now the way to get the ease of course, deploy those models, deploy these models in a safe, effective manner and ensuring that all of the robustness and the security guarantees and the privacy guarantees are all there. And we want to make this, we want to make sure that this transitions from something that's great demos to actual at scale products, which means making them work reliably all of the time, not just some of the time. Tom, what's your gauge for success? I think we're seeing a completely new form of ways to interact with data, to make data intelligent and directly to bring in new revenue streams into business. So if businesses can use our models to leverage that and generate completely new revenue streams and ultimately bring incredible new value to their customers, then that's fantastic. And we hope we can power that revolution. Caden, what's your take? Yeah, reiterating Bretton and Tom's point, I think that value in the enterprise and value in market is like a huge, it's the goal that we're striving towards. I also think that the value to consumers and actual users and the transformation of the surface area of technology to create experiences like chat GTT that are magical. And it's the first time in human history we've been able to talk to something compelling that's not a human. I think that in itself is just extraordinary and so exciting to see. It really brings up a whole nother category of markets, B2B, B2C. It's B2D, business to developer. Because I think this is kind of the big trend. The consumers have to win. Developers coding the apps. It's a whole nother sea change. Everyone uses the Moneyball movie as example during the big data wave and value of data. There's a scene in Moneyball at the end where Billy Bean's getting the offer from the Red Sox and the owner says, if every team's not rebuilding their teams based on your model, they'll be dinosaurs. I think that's the same with AI here. Every company will have to need to think about their business model and how they operate with AI. So it'll be a great run. It'll be a great run. Nathan, Tom, thank you so much for sharing about your experiences at your companies and congratulations on your success. And it's just the beginning. And Bratton, thanks for coming on, representing AWS. And thank you. Appreciate what you do. Thank you. Thank you, John. Thank you, Nathan. Thank you, John. Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching.