Kick off Season 3 of Inside MySQL: Sakila Speaks as leFred and Scott welcome Matt Quinn for an engaging introduction to the world of Artificial Intelligence. In this episode, we step back from the database and explore what AI really is, how it's shaping society and technology, and why it matters to anyone in tech today. Whether you're just curious about AI or eager to understand its key concepts, join us as we break down the basics and set the stage for a season of discovery.

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Episode Transcript:

00:00:00 :00 - 00:00:31 :22
Welcome to Inside MySQL: Sakila Speaks. A podcast dedicated to all things MySQL. We bring you the latest news from the MySQL team, MySQL project updates and insightful interviews with members of the MySQL community. Sit back and enjoy as your hosts bring you the latest updates on your favorite open source database. Let's get started!

00:00:32 :00 - 00:00:58 :22
Hello and welcome to Sakila Speaks, the podcast dedicated to MySQL. I am leFred and I'm Scott Stroz. Join us today. It's Matt Quinn, vice president and head of AI at Orracle. Matt leads how Oracle Cloud Infrastructure's AI services are adopted by customers in EMEA. Matt brings deep expertise in enterprise software strategy and a passion for making AI both powerful and its adoption practical.

00:00:59 :00 - 00:01:21 :03
Today he is here to help us unpack what GenAI really means for the organizations we work for and buy from, and what it means for developers, data professionals, and MySQL users everywhere. Matt, welcome to Inside MySQL: Sakila Speaks. It's great to have you with us to kick off season three of our podcast. Thank you very much, Fred,  Scott, great to be with you.

00:01:21 :08 - 00:01:43 :21
Looking forward to, to an interesting conversation and getting us going for season three. Awesome. Matt, thanks for being here with us. So right off the bat, when most people hear the term AI, they probably think of chat bots. But that's just one form of AI. Can you help provide us with like a high overview of the different types of AI that exist?

00:01:43 :23 - 00:02:15 :10
Absolutely. And I think AI and itself is a broad church, right? There's a number of different, kinds of AI. The term actually dates back to the 1950s as a concept for you know, machine thinking. It's had a couple of false dawns over the time when compute and data to train. I wasn't really quite ready for this, but as we got into the 90s and the early noughties, as compute power grew, as storage grew, a confluence of internet accessibility, lots of data becoming available, and then we time fed forward.

00:02:15 :12 - 00:02:33 :12
We found that organizations could do the fundamentals of what we know of AI today things like machine learning. So learning a trend and a pattern, looking at what happened in the past and do a statistical regression on that to predict some future outcome based on what happened in the past. And we use examples of this today without even knowing it.

00:02:33 :12 - 00:02:52 :11
You know, is this email that's coming into my email system, is this spam or not spam? Those kind to classifier types of AI have been prevalent for the last ten, 15, 20 years, and we're moving forward to where AI has this more kind of human interaction. It's surfacing and it's suddenly popped into the zeitgeist, for for conversation.

00:02:52 :15 - 00:03:14 :03
So it has multiple facets. We have machine learning trained something to do, something very specific, show it, something that it's seen before and enable it to predict the future based on what it's learned. But we're starting to see this wave of generative AI do more advanced, more nuanced, more humanlike things, and I think that's a really powerful kind of inflection point that we've seen in the last two, three years.

00:03:14 :05 - 00:03:39 :02
Thank you. So because in your first, answer, you said you said about the 70s and 90s, but why is I having such a huge moment right now? So what changed since that time? I think that the real inflection point is the the kind of conversational nature of it. You can speak human to it, and it can speak human back to you.

00:03:39 :04 - 00:04:01 :13
If I think about how compute evolved, you know, it used to be I had to type cryptic commands on the green screen in order to be able to use a computer, which meant the audience of people who could use computer to do something was very limited. In the 80s is the GUI. The graphical user interface kind of emerged suddenly it was a keyboard in a mouse, and the population of people who could interact with the computer was much broader.

00:04:01 :15 - 00:04:19 :02
Mobile did the same for us, but you still had to learn things. You had to take the human to interact in a way that made sense to the computer. With generative AI, I think what's happened in the last 2 or 3 years is actually the computer is coming to meet the human. Suddenly it's able to interact with us in our language.

00:04:19 :07 - 00:04:37 :19
I can have a conversation with it. I can ask a question in natural language. Now I might need to engineer my prompts to get the right kind of outcomes to guide it. Actually, the computer understands what I say. It can meet my language and understand that interact with me in a very human way. And I think that's caught the imagination of people.

00:04:37 :19 - 00:04:59 :18
They've suddenly had this 'aha' moment and that then has gone from, you know, an academic or data or IT kind of problem. It's broken out of it and gone into the board to say, well, actually, what does this mean? How will this work? And as people start to imagine what it could do beyond, you know, asking a question about, you know, what recipe do I have?

00:04:59 :18 - 00:05:20 :13
Or how can I find an answer to a question I could historic could use a search engine for, but save me some heavy lifting organizations to look at it and say, oh, hang on a minute. What manual processes in my organization...What low value repetitive tasks are happening in my organization that this might help me change? So suddenly AI has gone from being an IT conversation to being a business conversation.

00:05:20 :13 - 00:05:48 :15
It's it's got the opportunity. It's got the ear of the board. And suddenly that's just pivoted the demand and the interest in AI I think in the last couple of years. That is quite insightful. So because I has become the big thing in the world and everybody is talking about AI, there's got to be some, some common myths or misconceptions about AI out there that you've heard give us one or a couple that you've you've heard that you need to clear up and be like, that's not actually the case.

00:05:48 :17 - 00:06:11 :19
So there's a couple of things that I think, reoccurring in the conversations I have with customers, with, with engineers, with particularly people outside of IT. And one of those is around privacy. And I think that the challenge that we have with AI is the first services that really burst this into the public domain. There's kind of ChatGPT services.

00:06:11 :19 - 00:06:31 :04
There's first, opportunity where you could just go to a website, sign up for free, try something for free, engage with it and have a human like conversation. But that spread like wildfire, like 100 million users in a crazy amount of time. The interesting thing there is that free service, and I always like the phrase if something's free, you are the product.

00:06:31 :06 - 00:06:54 :21
That's those kinds of public sites where it's, you know, it's a consumer-grade service. There's no charge. The huge costs sitting underneath those models, like running the infrastructure, running the applications, having train the models. So the reality is in that environment, the value exchange it was happening is the prompts that I give that free service are available to be used to retrain the model to extend it, to make the product better.

00:06:54 :23 - 00:07:24 :01
So you're giving access to the data that you provide through a prompt to the service provider that is running that service. That's the value exchange. Now that's created this perception in people's minds that AI isn't private or safe or secure. And I think the reality is, when you do this in an enterprise context, you can absolutely run those models in a ring fenced way, the same way you'd run a database platform where it's isolated.

00:07:24 :07 - 00:07:41 :09
It's not sending data back to the model provider, it's secure and it's yours. And that enables you to do things. Bring your private data combined with the intelligence that the model has been trained on with public data. And that's what builds builds a system. But it doesn't have to be a system where you're losing control of that data.

00:07:41 :13 - 00:08:02 :22
So I think there's a lot of FUD around that fear, uncertainty and doubt. And it's up to us as technologists to help dispel the myths and separate where that might be happening in certain domains. That free service is public services. Maybe that is happening, but in an enterprise it scenario, you absolutely can put the security and privacy guardrails around it to meet the kind of enterprise controls that you'd expect.

00:08:03 :00 - 00:08:36 :10
Whilst reaping the benefits of the AI productivity gains, that you could have. So I think that that to me is the big one. Awesome. Thank you. So, because you said that, you you talked about AI, in industries, and how it's used. And I really like the analogy with the database. So for us, with MySQL, we really enjoy, the databases, could you, paint a picture of how AI is being used across the industries, or is it just specific, or can we use it, in different ways?<