AI for Data: When Data meets Intelligence

AI is driving a remarkable transformation throughout the industry, delivering unprecedented productivity gains and enabling rapid insights from vast amounts of data.

In this two-episode season premiere, Tirthankar Lahiri, SVP of Mission-Critical Data and AI Engines, discusses how Oracle AI Vector and embedded machine learning search are harnessing the power of AI to unlock value from enterprise data, and allow developers to build sophisticated RAG and Agentic frameworks that leverage the full power of the converged database architecture of Oracle Database — including its class-leading scalability, fault-tolerance, and enterprise-grade security. Furthermore, Oracle database provides several mechanisms to make data "AI-ready" by enabling declarative data intent for AI. In this session, we will describe these techniques, and more, to explain how to truly build an AI for data solution in this rapidly changing AI landscape! ------------------------------------ Episode Transcript:

00:00:00:00 - 00:00:34:07 Unknown Welcome to the Oracle Academy Tech Chat. This podcast provides educators and students in-depth discussions with thought leaders around computer science, cloud technologies, and software design to help students on their journey to becoming industry ready technology leaders of the future. Let's get started. Welcome to Oracle Academy Tech Chat, where we discuss how Oracle Academy prepares the next generation's workforce.

00:00:34:09 - 00:01:03:23 Unknown I'm your host, Tara Pierce. This is the first of two episodes on AI for data when data meets intelligence. Our guest speaker is to thank Carly Harris, senior vice president for mission critical data and AI engines at Oracle. Here's responsible for the data engine for Oracle database, including areas like AI, vector search, indexing and data compression. He also manages the Oracle Times ten in memory and the Oracle NoSQL database product teams to thank her.

00:01:03:23 - 00:01:33:13 Unknown Has 30 years of experience in the database industry and has worked on a variety of areas such as performance, scalability, manageability, caching, in-memory architectures and developer focused functionality. He has 71 issued and several pending patents. A bachelor's in computer Science from the Indian Institute of Technology and a master's in electrical engineering from Stanford University. In the first episode to thank our talks about how data makes AI intelligent and how enterprises are using AI to get greater value from their data.

00:01:33:15 - 00:01:59:19 Unknown Over to you to thank her. Hi. Hey, guys. Thank you very much for joining. It's a great pleasure to be presenting AI for data. This is an exciting time in technology. AI is ubiquitous. AI changes everything. And I actually makes data intelligent. Let's talk about that today. So you know Oracle is working on AI. As many of you know, at many levels in the enterprise stack.

00:01:59:21 - 00:02:31:22 Unknown We have AI initiatives for applications, AI initiatives for services. I for data. And we're building a lot of AI infrastructure, as you seen from the news. Now I'm going to focus on AI for data. That's the focus of my presentation today. How we bring AI, the power of AI and unleash it on enterprise data. So Oracle's goal is to make AI for data extremely simple for basically everything.

00:02:32:00 - 00:02:54:08 Unknown So no matter what kind of end user you are, whether you're an expert, an AI, or a developer, or a DBA random list, every single persona should be able to leverage AI for data. We want to make it possible for all applications to leverage AI for data and benefit all workloads with the AI for data. So this is the goal that we have for AI for data.

00:02:54:08 - 00:03:25:05 Unknown Now, there's again basically two classic kinds of AI in the classical sense. So let's quickly talk about one before I get to what's new. So the traditional AI, was basically called algorithmic AI. Algorithmic here is based on machine learning models, typically non neural net designed to do predictions classifications, forecasting etc. and for data science people, you know that there's many different machine learning algorithms.

00:03:25:07 - 00:03:44:06 Unknown And these are all now available in Oracle database. So if you want you can use one of these models. This is the ever evolving list. You can use one of these models to load to first of all to train, you know, a sorry, you could use one of these algorithms. Excuse me. I keep that in the trunk.

00:03:44:08 - 00:04:05:22 Unknown These are algorithms. You can use one of these to train models and then to run inferencing using these models. So you imagine you can take, you know, linear linear regression. The algorithm used that to train a model and then applied that to data in real time to basically do predictions. So that's what in database machine learning lets you do.

00:04:06:00 - 00:04:30:18 Unknown And we've had this, capability for a while now. So what is new is something called I vector search, which is the primary focus of a presentation today. And this is newer, you know, and if this is beyond classical machine learning. So basically yeah vector search the new technology that enables searching for data by semantics rather than values.

00:04:30:20 - 00:04:54:11 Unknown The why why is this important? Because if you look at what databases traditionally do, for those of you who've been in the database field or have studied databases, databases essentially do what we call value based searches, where given a value, they can search by that value, like for instance, finding the revenue by each product. That's a very typical search you run inside of a database.

00:04:54:13 - 00:05:22:10 Unknown And they've excelled at this through various, you know, techniques like query optimization, SQL document processing, etc.. However, there is an ever increasing volume of unstructured data which you really can't search by value, but they have to be searched by semantics or meaning, like, you know, photos or images or description. Long complex textual descriptions. There's no real value that you can search those with.

00:05:22:10 - 00:05:52:08 Unknown Effectively, you need to search them essentially by their semantic content, not by the value content. For instance, finding products that match a particular photo or match a description that's not really something a database could do very well in the past. And this is a very important, an ever growing use case, because, you know, businesses need to do this today on a routine basis, forgetting about AI just in general to keep the business running in a healthy fashion.

00:05:52:10 - 00:06:25:14 Unknown There's a lot of examples of use cases where a business needs a search its data by, sort of the semantics. For instance, if you know, you have parts going into the sub line for manufacturing, the photo, the part should, quickly tell you whether that part might be defective, when customers log in to e-commerce sites, then when you browse products, so you try to check out a certain product, there is a desire from the e-commerce site to see what else they could then recommend to you in real time.

00:06:25:16 - 00:06:43:19 Unknown These are all examples. Another one is, of course, biometrics. You know, I'm coming in to the airport. I need to, you know, I go through facial recognition. They want to make sure that I'm the person I said I am when I, you know, when I submitted my visa application. So all of these cases require semantic search, not value based search.

00:06:43:21 - 00:07:11:12 Unknown And, vector searches. Exactly. That enable searching data based semantics. That's precisely what it does. And it does that using a construct primitive known as a vector, which is very simple actually. You know, if you think about this, the beauty of this is the basic concept is very easy, very simple. A vector is simply a long string of numbers that capture the semantics of much more complex data.

00:07:11:12 - 00:07:36:14 Unknown And they're produced by something I call black magic deep learning, machine learning models that take this, you know, unstructured set of data on the left, apply these complex algorithms and machine learning algorithms to that data and then outcomes a vector. It's actually incredible that this this actually works, that you can take something as sophisticated as a Picasso painting and convert that into a string of numbers.

00:07:36:14 - 00:07:59:16 Unknown That represents that painting. That's basically what a vector does. It's a string of numbers encoding the semantics. And once you do that, well, how do you then measure for similarity? The way you do that is by measuring the mathematical distance between the vectors. Now for those of you who've of course all of you are familiar with the vector concept, I'm sure from mathematics and physics.

00:07:59:18 - 00:08:22:09 Unknown Basically, vectors are points in multidimensional space, and there's many different ways to measure distance between them. You know, a simple example, a simple distance function is what we call Euclidean squared. We just take the square of the difference, the sum of the differences of each coordinate. That's a that's one distance for a function. However, there's many formula for distance.

00:08:22:11 - 00:08:45:07 Unknown And each machine learning model and each data scientist prefers a different one. Let's talk about how vectors get used in the real world. Now, if you think about, a very simple business example, I know that most of you not not, you know, business people, but most of you use products and sometimes, you know, products go wrong and you have this file, you know, ask for help from customer support.

00:08:45:08 - 00:09:10:11 Unknown Support incidents are very complex, documents, very complex entities. T