Part II of AI for Data: When Data meets Intelligence cover art

Part II of AI for Data: When Data meets Intelligence

Part II of AI for Data: When Data meets Intelligence

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 AI is driving a remarkable transformation throughout the industry, delivering unprecedented productivity gains and enabling rapid insights from vast amounts of data. In the second of a 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. AI is triggering an incredible transformation across the industry, enabling breakthrough improvements in productivity and rapid insights across massive volumes of data. However, in order to fully harness the power of AI, it is necessary to enable AI processing where the data resides. Oracle AI vector and built-in-machine learning search bring the power of AI to 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 - 00:00:36:04 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 next generation's workforce. I'm your host, Tyra Peirce. 00:00:36:06 - 00:01:02:02 Unknown This is the second episode in our two part presentation. AI for data when data meets intelligence. In this episode, our guest speaker to thank Tirthankar Lahiri, senior vice President for mission critical data and AI engines at Oracle, concludes his presentation on AI and enterprise data. If you've not heard episode one yet, please go to oracle.com/podcast where you will find all episodes of Oracle Academy Tech Chat. 00:01:02:04 - 00:01:32:05 Unknown Now let me hand over two to thank for the conclusion of this presentation by. Hey guys, thank you very much for joining. It's a great pleasure to be presenting AI for data. Now let's talk about vector sequel. I've already mentioned this briefly earlier, so I won't spend too much time on this, but basically you can do vector distances to find similar, objects using the vector distance function and very simple query finding the customers that are similar to this photo. 00:01:32:07 - 00:01:55:00 Unknown And I wonder what the top five customers and we could add other complex conditions. Like I said earlier, maybe only one customers in San Francisco that match this photo. Again, that filter will be added to the query to find only the customers matching the photo in San Francisco. We could join, various with other tables as well. 00:01:55:01 - 00:02:25:20 Unknown For instance, we could join it with a status table that finds the customers only with a certain spending tier. So this sort of, you know, extension gives me, again, as I said earlier, the power of composing SQL with complex conditions to run very sophisticated searches. Okay. Once again, in the query, you can also specify how accurate you want the search to be, regardless of the index accuracy. 00:02:25:20 - 00:02:48:01 Unknown You can make a search more accurate by spending more time on the search by going deeper in the index. Same deal. The index. The default accuracy. Maybe the default was that 85%, but I want 95% for a certain search. I want to do a deeper search. This directive with the SQL lets me do exactly that. I can specify again how much I want in the SQL itself. 00:02:48:01 - 00:03:13:16 Unknown Maybe it's a different search. Maybe my recommendations initially were great. I want like a really deep recommendation based on my last search. My last browsing or buying patterns. So this lets me crank up the knob, crank up the dial rather for the accuracy based on my use case. Again, to allow the search to proceed deeper into the vector index. 00:03:13:18 - 00:03:34:07 Unknown Okay. All right. I'm going to, you know, vector index support transactions. There's not a whole lot to say about that. As you update the data, the vector indexes get maintained. The core indexes, you know, I'm not going to spend a whole lot of time on this because some of this is very oracle specific. But we scale our Oracle database in a number of different ways. 00:03:34:09 - 00:03:58:12 Unknown And each of those scale out mechanism, the Oracle database runs on multiple physical computers. All of those mechanisms support vector indexes. So vector ...
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