 For centuries, we've been captivated by the concept of machines doing the job of humans. And over the past decade or so, we really focused on AI and the possibility of intelligent machines that can perform cognitive tasks. Now in the past few years with the popularity of machine learning models, ranging from recent chat GPT to BERT, we're starting to see how AI has changed in the way we interact with the world. How is AI transforming the way we do business and what does the future hold for us there? At theCUBE, we've covered Oracle's AI and ML strategy for years, which has really been used to drive automation into Oracle's autonomous database. We've talked a lot about MySQL Heatwave in database machine learning and AI pushed into Oracle's business apps. You know, Oracle intends to lead in AI but not competing as a direct AI player per se, but rather embedding AI and machine learning to its portfolio to enhance its existing products and bring new services and offerings to the market. Now last October at Cloud World in Las Vegas, Oracle partnered with NVIDIA, which is the go-to AI silicon provider for vendors and they announced an investment, a pretty significant investment to deploy tens of thousands more NVIDIA GPUs to OCI, the Oracle Cloud infrastructure and build out Oracle's infrastructure for enterprise scale AI. Now, Oracle CEO, Safra Katz said something to the effect of this alliance is going to help customers across industry, some healthcare, manufacturing, telecoms and financial services to overcome the multitude of challenges they face. Presumably she was talking about just driving more automation and more productivity. Now, to learn more about Oracle's plans for AI, we'd like to welcome in Elad Ziklik, who's the vice president of AI services at Oracle. Elad, great to see you, welcome to the show. Thank you, thanks for having me. You're very welcome. So first, let's talk about Oracle's path to AI. I mean, it's the hottest topic going for years. You've been incorporating machine learning into your products and services. You know, can you tell us what you've been working on and how you got here? So it's a great question. So as you mentioned, I think most of the original four way into AI was on bedding AI and using AI to make our applications and databases better. So inside MySQL Hitwave, inside our autonomous database, in parallel, we've been driving AI all across our SaaS apps. So Fusion, our large enterprise business suite for HR applications and CRM and ERP and whatnot has built in an AI inside it. Most recently, NetSuite, our small medium business SaaS suite started using AI for things like automated invoice processing and whatnot. And most recently, over the last, I would say, two years we've started exposing and bringing these capabilities into the broader OCI Oracle Cloud infrastructure. So the developers and ISVs and customers can start using our AI capabilities to make their apps better and their experiences in business workflow better and not just consume these as embedded inside Oracle. And this recent publisher that you mentioned with NVIDIA is another step in bringing the best AI infrastructure capabilities into this platform so you can actually build any type of machine learning workflow or AI model that you want on Oracle Cloud. So when I look at the market, I see companies out there like DataRobot or C3AI and there's maybe a half dozen that sort of pop up on my radar anyway. And my premise has always been that most customers, they don't want to become AI experts, they just want to buy applications and have AI embedded or they want AI to manage their infrastructure. So my question to you is, how does Oracle help its OCI customers support their business with AI? So it's a great question. So I think what most customers want is business AI. They want AI that works for their business, they want AI that works for their enterprise. I call it the last mile of AI. And they want this thing to work. The majority of them don't want to hire a large and expensive data science teams to go and build everything from scratch. They just want the business problem solved by applying AI tools. My best analogy is Lego. So if you think of Lego, Lego has these millions Lego blocks that you can use to build anything that you want. But the majority of people like me or like my kids, they want the Lego Death Star kit or the Lego Eiffel Tower thing. They want to think that the just works is very easy to use and still Lego blocks, you still need to build some things together. It just works for the scenario that you're looking for. So that's our focus. Our focus is making it easy for customers to apply AI where they need to in the right business context. So whether it's embedding it inside the business applications, like adding forecasting capabilities to your supply chain management or financial planning software, whether it's adding chatbots into the line of business applications, integrating these things into your analytics dashboard. Even all the way to, we have a new platform piece we call ML applications that allows you to take a machine learning model and scale it for the thousands of tenants that you would be. Because this is a big problem for most of them and use cases. It's very easy to build something for a proof of concept or a pilot or a demo. But then if you need to take this and then deploy it across your thousands of customers or your thousands of regions or facilities, then it becomes messy. So this is where we spend our time making it easy to take these things into production in the context of your business application or your business use case that you're interested in right now. So you mentioned chatbots and I want to talk about chat GPT but my question here is different. We'll talk about that in a minute. So when you think about these chatbots, the ones that are conversational, my experience anyway is they're just math. They're not that great, but the ones that actually work pretty well, they have a conditioned response. Now they're limited, but they say, which of the following is your problem? And if that's one of the following is your problem, you can maybe solve your problem, you know? But this is clearly a trend and it helps the line of business. How does Oracle think about these use cases for your customers? Yeah. So I think the key here is exactly what you said. It's about task completion. Okay, the general purpose bots are interesting, but as you said, like are still limited, they're getting much better and I'm sure we'll talk about chat GPT. But I think what most enterprises want is around task completion. I want to automate my expense support processing. So today inside Oracle, we have a chatbot where I submit my expenses. They ask, the bot asks a couple of questions, I answer them and then I'm done. Like I don't need to go to our fancy application and manually submit an expense report. I do this via Slack. And the key is around managing the right expectations of what this thing is capable of doing. Like I have a story from I think five, six years ago when technology was much inferior than it is today. Well, one of the telco providers I was working with wanted to hold a chatbot that does real-time translation. So it was for a support center, one of the call centers. And what they want to do is, hey, we have English-speaking employees or 24 seven, if somebody's calling and their native tongue is different, like Hebrew in my case or Chinese or whatnot, then we give them a chatbot that they will interact with and will translate this on the fly and everything would work. And when they rolled it out, the feedback from customers was horrendous. Customer said, the technology sucks, it's not good. I hate it, I hate your company, I hate your support. And what they've done is, they've changed the narrative. Instead of you go to a support center and you assume you're gonna talk to a human and instead you get a crappy chatbot, they're like, hey, if you want to talk to a Hebrew-speaking person, there's a four hour wait, please leave your phone and I'll call you back. Or you can try a new amazing Hebrew-speaking iPower bot and it may help your use case, you want to try it out. And some people said, yeah, let's try it out, press one to try it out. And the feedback even though it was the exact same technology was amazing. People were like, oh my God, this is so innovative, this is great, even though it was the exact same experience that they hated a few weeks earlier on. So I think the key lesson that I picked from this experience is it's all about setting the right expectations and working on the right use case. If you are replacing a human, the level is different than if you're just helping or augmenting something that otherwise would take a lot of time. And I think this is the focus that we're doing. Picking up the tasks that people wanna accomplish or that enterprise wanna accomplish for the customers, for the employees and using chatbots to make those specific ones better rather than, hey, this is gonna replace all humans if we will and just be better than that. Yeah, I mean, to the point you mentioned expense reports, I'm in a Twitter thread and one guy says, my favorite part of business travel is filling out expense reports. It's an hour of excitement to figure out which receipts won't scan, right? We can all relate to that, this is the worst. So when you think about companies that are building custom AI driven apps, what can they do on OCI? What are the best options for them? Do they need to hire an army of machine intelligence experts and AI specialists to help us understand your point of view there? So over the last, I would say the two or three years we've developed a full suite of machine learning and AI services for, I would say cover pretty much every use case that you would expect right now from applying natural language processing to understanding customer support tickets or social media or whatnot to computer vision platforms or computer vision services that can understand and detect objects and encounter objects on shelves or detect cracks in a pipe or defecting parts all the way to speech services that can actually transcribe human speech. And most recently we've launched a new document AI service that can actually look at unstructured documents like receipts or invoices or government IDs or even proprietary documents, loan application, student application forms, patient ingestion and whatnot and completely automate them using AI. So if you wanna do one of the things that are I would say common bread and butter for any industry whether it's financial services or healthcare or manufacturing we have a suite of services that any developer can go and use easily customized with their own data you don't need to be an expert in deep learning or large language models you could just use our auto ML capabilities and build your own version of the models just go ahead and use them. And if you do have proprietary complex scenarios that you need custom ML form scratch we actually have the most cost-effective platform for that. So we have the OCI data science as well as built-in machine learning platform inside the databases inside the Oracle database and MySQL HeatWave that allow data scientists Python-wielding people that actually like to build and tweak and control and improve have everything that they need to go and build the machine learning models from scratch deploy them monitor and manage them at scale in production environment. And most of it is brand new so we did not have these technologies four or five years ago and we've started building them and now at enterprise scale over the last couple of years. So what are some of the state of the art tools that AI specialists and data scientists need if they're going to go out and develop these new models? So I think it's on three layers. I think there's an infrastructure layer where the NVIDIAs of the world coming to play you for some of these things you want massively efficient massively scaled infrastructure place. So we are the most cost-effective and performant large scale GPU training environment today. We're going to go on board we're going to the first cloud on board the new NVIDIA H100 these are the new super powerful GPUs for large language model training. So we have that covered for you in case you need this because you want to build these ginormous things. You need a data science platform a platform where you can open a Python notebook and just use all these fancy open source frameworks and create the models that you want and then click on a button and deploy it and it infinitely scales wherever you need it. And in many cases you just need what I call the applied AI services you need the Lego sets the Lego Death Star Lego Eiffel Tower. So we have a suite of these sets for typical scenarios whether it's cognitive services of understanding images or documents or the way to solving particular business problems. So an anomaly detection service demand forecasting service that will be the equivalent of these Lego sets. So if this is the business problem that you're looking to solve we have services out there where you can bring your data, call an API, train a model get the model and use it in your production environment. So wherever you want to play all the way into a bed of this thing inside the applications obviously whatever you want to play we have the tools for you to go and to go and engage from infrastructure to SAS at the top and everything in the middle. When you think about the data pipeline the data lifecycle and the specialized roles that came out are kind of the Hadoop error if you will. I want to focus on two developers and data scientists. So the developers they hate dealing with infrastructure and they got to deal with infrastructure now they're being asked to secure the infrastructure they just want to write code. And data scientists they're spending all their time trying to figure out okay what's the data quality and they're wrangling data and they don't spend enough time doing what they want to do. So there's been a lack of collaboration have you seen that change or these approaches allowing collaboration between data scientists and developers on a single platform? Can you talk about that a little bit? Yeah, that is a great question. One of the biggest set of scars that I have on my back from building these platforms and other companies is exactly that. Every persona had a set of tools and these tools didn't talk to each other and the handoff was painful and most of the machine learning things evaporate or die on the floor because of this problem. It's very rarely that they are unsuccessful because the algorithm wasn't good enough. In most cases it's somebody builds something and then you can't take it to production. You can't integrate it into your business application. You can't take the data out, train, create an endpoint and integrate it back like it's too painful. The way we are approaching this is focused on this problem exactly. We have a single set of tools that if you publish a model as a data scientist then developers and even business analysts that are seeing inside a business application could be able to consume it. We have a single model store, a single feature store, a single management experience that caused the various personas that need to play in this and we spend a lot of time building, I'm borrowing a word that a certain folks used and I really liked it. Building inside highways to make it easier to bring these insights into where you need them inside the applications both inside our applications, inside our SaaS applications but also inside custom third-party and even first-party applications. And this is where a lot of our focus goes to just because we have done with so much pain doing this inside our own SaaS that we now have built the tools and we're making them available for others to make this process of building a machine learning outcome-driven insight in your app easier. And it's not just the model development and it's not just the deployment, it's the entire journey of taking the data, building the model, training it, deploying it, looking at the real data that comes from the app and creating this feedback loop in a more efficient way. And that's our focus area, exactly this problem. Well, thank you for that. So last week we had our SuperCloud 2 event and I had Juan Loiza on and he spent a lot of time talking about how open Oracle is and its philosophy. And I got a lot of feedback. They were like, Oracle, open? I don't really, but the truth is, if you think about database, Oracle database, it never met a hardware platform that it didn't like. So in that sense, it's open. So my point is a big part of machine learning and AI is driven by open source tools, frameworks. What's your open source strategy? What do you support from an open source standpoint? So I'm a strong believer that you don't actually know, nobody knows where the next leapfrog or the next industry shifting innovation in the AI is gonna come from. If you look six months ago, nobody foreseen Dali, the magical text to image generation and the explosion that it brought into just art and design type of experiences. If you look six weeks ago, I don't think anybody's seen chat GPT and what it can do for a whole bunch of industries. So to me, assuming that a customer or partner or developer would wanna lock themselves into only the tools that a specific vendor can produce is ridiculous, because nobody knows. If anybody claims that they know where the innovation is gonna come from in a year or two, let alone five or 10, they're just wrong or lying. So my strategy, our strategy for Oracle is to, I call this the Netflix of AI. So if you think about Netflix, then they produced a bunch of high quality shows on their own. A few years ago, it was House of Cards. House of Cards, last month, my wife and I binge watched a genie in Georgia, but they also curated a lot of shows that they found around the world and bought them to their customers. So we started with things like Seinfeld or Friends. And most recently, it was Squid Games and there was a famous Israeli TV series called Fouda that Netflix bought in. And they bought it as is, and they gave it the Netflix value. So you have captioning and you have the ability to speed the movie and you have it inside your app and you can download it and watch it offline and everything. But nobody Netflix was involved in the production of these first seasons. Now, if these things hunt and they are great, then the third season or the fourth season will get the full Netflix production value, high value budget, high value location feuding or whatever. But you as a customer, you don't care whether the producer and director and screenplay writing is a Netflix employee or it's somebody else's employee. It is fulfilled by Netflix. I believe that we will become, or we are looking to become the Netflix of AI. We are building a bunch of AI in a bunch of places where we think it's important and we have some competitive advantage like healthcare with a certain partnership or whatnot. But I want to bring the best AI software and hardware to OCI and do a fulfillment by Oracle on that. So you will get the Oracle security and identity and single bill and everything you'd expect from a company like Oracle. But we don't have to be building the data science and the models for everything. So this means both open source will recently announced a partnership with Anaconda, the leading provider of Python distribution in the data science ecosystem where we are doing a joint strategic partnership of bringing all the goodness into Oracle customers as well as in the process of doing the same with NVIDIA and all the software libraries, not just at Harvard, both for ML stuff like Triton and but also for healthcare specific stuff as well as other ISVs, other AI leading ISVs that we are in the process of partnering with to get their stuff into OCI and into Oracle so that you can truly consume the best AI hardware and the best AI software in the world on Oracle because that it was what I believe our customers would want, the ability to choose from any open source engine and mostly from any ISV type of solution that is AI powered and they want to use it in their experiences. So you mentioned chat GPT, I want to talk about some of the innovations that are coming as an AI expert. You see chat GPT on the one hand, I'm sure you weren't surprised on the other hand, maybe the reaction in the market and the hype is somewhat surprising. They say that we tend to over-hype things in the early stages and under-hype them long-term, you kind of use the internet as an example, what's your take on that premise? So I think that this type of technology is going to be an inflection point in how software is being developed. I truly believe this. I think this is an internet style moment and the way software interfaces and software applications are being developed will dramatically change over the next year, two or three because of this type of technologies. I think there will be industries that will be shifted. I think education is a good example. I saw this thing opened on my son's laptop. So I think education is going to be transformed, design industry like images or whatever has already been transformed. But I think that for mass adoption, like beyond the hype, beyond the peak of inflected expectations if I'm using got another terminology, I think certain things need to go and happen. One is this thing needs to become more reliable. So right now it is a complete black box that sometimes produce magic and sometimes produce just nonsense and it needs to have better explainability and better lineage to, how did you get to this answer? Cause I think enterprises are going to really care about the things that they surface with the customers or use internally. So I think that is one thing that's going to come out. And the other thing that's going to come out is I think it's going to come industry specific large language models or industry specific chat GPTs, something like how OpenAI did co-pilot for writing code. I think we will start seeing this type of apps solving for specific business problems, understanding contracts, understanding healthcare, writing doctor's notes on behalf of doctors. So they don't have to spend time manually, and manually recording and analyzing conversations. And I think that would become the sweet spot of this thing. There will be companies, whether it's OpenAI or Microsoft or Google or hopefully Oracle that will use this type of technology to solve for specific very high value business needs. And I think this will change how interfaces happen. So going back to your expense report, the world of I'm going to go into an app and I'm going to click on seven buttons in order to get some job done, like this world is gone. Like I'm going to say, hey, please do this and that and I expect an answer to come out. I've seen a recent demo about marketing in sales. So a customer sends an email that is interested in something and then a charge EPT powered thing just produces the answer. I think this is how the world is going to evolve. Like, yes, there's a ton of hype. Yes, it looks like magic. And right now it is magic, but it's not yet productive for most enterprise scenarios. But in the next six, 12, 24 months, this will start getting more dependable and it's going to change how these industries are being managed. Like I think it's an internet level revolution. That's my point. Very interesting. It's going to change the way in which we have, instead of accessing the data center through APIs, we're going to access it through natural language processing. And that opens up technology to a huge audience. Last question is a two-part question. And the first part is what you guys are working on from the futures. But the second part of the question is, we got data scientists and developers in our audience. They love the new shiny toy. So give us a little glimpse of what you're working on in the future and what would you say to them to persuade them to check out Oracle's AI services? Yeah. So I think there's two main things that we're doing. One is around healthcare. With a new user and acquisition, we are spending a significant effort around revolutionizing healthcare with AI across many, many scenarios. From patient care, using computer vision and cameras to automating and making better insurance claims to research and pharma. We are making the best models from leading organizations and internal available for hospitals and researchers and insurance providers everywhere. And we truly are looking to become the leader in AI for healthcare. So I think that's a huge focus area. And the second part is, again, going back to the enterprise AI angle. We want to, if you have a business problem that you want to apply to solve, we want to be your platform. You could use others if you want to build everything complicated and whatnot, we have a platform for that as well. But if you want to apply AI to solve a business problem, we want to be your platform. We want to be, again, the Netflix of AI kind of a thing where we are the place for the greatest AI innovations accessible to any developer, any business analyst, any user, any data scientist on Oracle Cloud. And we're making a significant effort on these two fronts, as well as developing a lot of the missing pieces and building blocks that we see are needed in this space to make truly like a great experience for developers and data scientists. And what would I recommend? And get started, try it out. Like the, we actually have a shameless sales plug here. We have a free tier for all of our AI services. So it literally costs you nothing. I would highly recommend to just go and try these things out. Go play with it. If you're a Python-wielding developer and you want to try a little bit of AutoML, go down that path. If you're not even there and just like, hey, I have these customer feedback things that I want to try out if I can understand them and apply AI and visualize and do some cool stuff. We have services for that. My information is, and I think ChefGPT got us because they see people that have nothing to do with AI and can't even spell AI going and try it out. I think this is the time. Go play with these things. Go play with these technologies and find what AI can do to you or for you. And I think Oracle is a great place to start playing with these things. Eli, thank you. Appreciate you sharing your vision of making Oracle the Netflix of AI. I love that and really appreciate your time. Awesome, thank you. Thank you for having me. Okay, thanks for watching this Cube Conversation. This is Dave Vellante. We'll see you next time.